@article {Marker2017, title = {1.5 Tesla augmented reality navigated interventional magnetic resonance imaging: paravertebral sympathetic plexus injections}, journal = {Diagnostic and Interventional Radiology}, year = {In Press}, month = {01/2017}, author = {David R. Marker and Paweena U-Thainual and Tamas Ungi and Aaron J Flammang and Gabor Fichtinger and Iulian Iordachita and John A Carrino and Jan Fritz} } @article {Yeo2017, title = {3D Segmentation as a Surgical Planning Tool for Residents in Liver Resection Surgery}, year = {In Press}, abstract = {

Introduction: Liver surgery requires identification of tumor(s) in relation to key vessels to preserve healthy tissue, while obtaining negative margins. Current planning is mentally strenuous for residents as it relies on mental 3D reconstruction, anatomical knowledge, spatial sense, and experience. The purpose of this study is to determine if 3D segmentation improves resident ability to devise appropriate liver resection plans.

Methods: Senior general surgery residents were recruited. Pre-operative CT/MR images were selected if they reflected actual surgeries performed. Images were segmented to create interactive 3D models. Residents were asked to devise surgical plans for case-matched 2D and 3D models in an alternating, randomly generated order. Primary outcome was correct preoperative plan based on actual surgery performed. Secondary outcome was time (seconds(s)) to devise plan. Planning data was analyzed using Wilcoxon test, time was analyzed using paired t-test.

Results: All 14 senior residents from our institution participated. The average correct response was 1.7 of 5 (34\%; range 1 to 4) for the 2D group, and 3.1 of 5 (62\%; range 0 to 4) for the 3D group (p\<0.01). The average time to complete each plan was 156{\textpm}107s for the 2D group, and 84{\textpm}73s for the 3D group (p\<0.01).

Conclusions: The results show that 3D segmentation increases accuracy of surgical planning and decreases amount of time required. 3D segmentation is useful as a teaching tool as it reduces cognitive load required to mentally reconstruct 2D images, allowing the resident to focus on surgical planning. It improves understanding of spatial liver anatomy and serves as an adjunct to current 2D planning methods. This has the potential to be developed into a module for teaching liver surgery relevant to a competency-based curriculum.

}, author = {Yeo, Caitlin T and MacDonald, Andrew and Tamas Ungi and Andras Lasso and Diederick Jalink and Gabor Fichtinger and Nanji, Sulaiman} } @article {Holden2019a, title = {Machine learning methods for automated technical skills assessment with instructional feedback in ultrasound-guided interventions}, journal = {International Journal of Computer Assisted Radiology and Surgery}, year = {In Press}, doi = {https://doi.org/10.1007/s11548-019-01977-3}, author = {M. Holden and Sean Xia and H. Lia and Keri, Zsuzsanna and Colin Bell and Lindsey Patterson and Tamas Ungi and Gabor Fichtinger} } @article {Janssen2020a, title = {Navigated tissue characterization during skin cancer surgery}, journal = {Int J Comput Assist Radiol Surg}, year = {In Press}, month = {05/2020}, abstract = {

Purpose:\ Basal cell carcinoma (BCC) is the most commonly diagnosed skin cancer and is treated by surgical resection. Incomplete tumor removal requires surgical revision, leading to significant healthcare costs and impaired cosmesis. We investigated the clinical feasibility of a surgical navigation system for BCC surgery, based on molecular tissue characterization using rapid evaporative ionization mass spectrometry (REIMS).

Methods:\ REIMS enables direct tissue characterization by analysis of cell-specific molecules present within surgical smoke, produced during electrocautery tissue resection. A tissue characterization model was built by acquiring REIMS spectra of BCC, healthy skin and fat from ex vivo skin cancer specimens. This model was used for tissue characterization during navigated skin cancer surgery. Navigation was enabled by optical tracking and real-time visualization of the cautery relative to a contoured resection volume. The surgical smoke was aspirated into a mass spectrometer and directly analyzed with REIMS. Classified BCC was annotated at the real-time position of the cautery. Feasibility of the navigation system, and tissue classification accuracy for ex vivo and intraoperative surgery were evaluated.

Results:\ Fifty-four fresh excision specimens were used to build the ex vivo model of BCC, normal skin and fat, with 92\% accuracy. While 3 surgeries were successfully navigated without breach of sterility, the intraoperative performance of the ex vivo model was low (\< 50\%). Hypotheses are: (1) the model was trained on heterogeneous mass spectra that did not originate from a single tissue type, (2) during surgery mixed tissue types were resected and thus presented to the model, and (3) the mass spectra were not validated by pathology.

Conclusion:\ REIMS-navigated skin cancer surgery has the potential to detect and localize remaining tumor intraoperatively. Future work will be focused on improving our model by using a precise pencil cautery tip for burning localized tissue types, and having pathology-validated mass spectra.

}, keywords = {Intraoperative surgical resection margin assessment, Model-guided surgery, Optical navigation, Real-time tissue characterization}, doi = {10.1007/s11548-020-02200-4}, url = {https://doi.org/10.1007/s11548-020-02200-4}, author = {Natasja Janssen and Martin Kaufmann and Alice Santilli and Amoon Jamzad and Vanderbeck Kaitlin and Kevin Ren and Tamas Ungi and Parvin Mousavi and John Rudan and Doug McKay and Amy Wang and Gabor Fichtinger} } @article {Hu2022, title = {Real-time automatic tumor segmentation for ultrasound-guided breast-conserving surgery navigation}, journal = {International Journal of Computer Assisted Radiology and Surgery}, year = {In Press}, month = {05/2022}, doi = {10.1007/s11548-022-02658-4}, url = {https://doi.org/10.1007/s11548-022-02658-4}, author = {Zoe Hu and Paola V. Nasute Fauerbach and Chris Yeung and Tamas Ungi and John Rudan and C. Jay Engel and Parvin Mousavi and Gabor Fichtinger and Doris Jabs} } @article {Kitner2022a, title = {Automated Automatic catheter modelling in 3D transrectal ultrasound images from high-dose-rate prostate brachytherapy using a deep learning and feature extraction pipeline}, year = {2022}, month = {06/2022}, publisher = {Canadian Organization of Medical Physicists}, author = {Nicole Kitner and Jessica R. Rodgers and Tamas Ungi and Martin Korzeniowski and Timothy Olding and C.P. Joshi and Parvin Mousavi and Gabor Fichtinger} } @article {Kitner2022a, title = {Automated catheter localization in ultrasound images from High-dose-rate prostate brachytherapy using deep learning and feature extraction}, year = {2022}, month = {09/2022}, publisher = {Canadian Association for Radiation Oncologists}, author = {Nicole Kitner and Jessica R. Rodgers and Tamas Ungi and Timothy Olding and C.P. Joshi and Parvin Mousavi and Gabor Fichtinger and Martin Korzeniowski} } @article {Kitner2022a, title = {Automated Catheter Segmentation in 3D Ultrasound Images from High-Dose-Rate Prostate Brachytherapy}, year = {2022}, month = {02/2022}, publisher = {Imaging Network of Ontario}, address = {Online}, author = {Nicole Kitner and Jessica R. Rodgers and Tamas Ungi and Martin Korzeniowski and Timothy Olding and C.P. Joshi and Parvin Mousavi and Gabor Fichtinger} } @article {Connolly2022c, title = {Bridging 3D Slicer and ROS2 for Image-Guided Robotic Interventions}, journal = {Sensors}, volume = {22}, year = {2022}, month = {07/2022}, doi = {https://doi.org/10.3390/s22145336}, author = {Laura Connolly and Anton Degeut and Simon Leonard and Junichi Tokuda and Tamas Ungi and Axel Krieger and Peter Kazanzides and Parvin Mousavi and Gabor Fichtinger and Russell H. Taylor} } @article {Lee2022a, title = {Evaluating Faster R-CNN for cataract surgery tool detection using microscopy video}, year = {2022}, abstract = {

Introduction: Traditional methods of cataract surgery skill assessment rely on human expert supervision. This exposes the trainee to interobserver variability and inconsistent feedback. Alternative measures such as sensorbased instrument motion analysis promise objective assessment [1]. However, sensor-based systems are logistically complicated and expensive to obtain. Previous studies have demonstrated a strong correlation between sensor-based metrics and two-dimensional motion metrics obtained from object detection [2]. Reliable object detection is the foundation for computing such performance metrics. Therefore, the objective of this study is to evaluate the performance of an object detection network, namely Faster Region-Based Convolutional Neural Network (FRCNN), in recognition of cataract surgery tools in microscopy video. Methods: Microscope video was recorded for 25 trials of cataract surgery on an artificial eye. The trials were performed by a cohort consisting of one senior-surgeon and four junior-surgeons and manually annotated for bounding box locations of the cataract surgery tools (Figure 1) The surgical tools used included: forceps, diamond keratomes, viscoelastic cannulas, and cystotome needles. A FRCNN [3] was trained on a total of 130,614 frames for object detection. We used five-fold cross validation, using a leave-one-userout method. In this manner, all videos from one surgeon were reserved for testing and the frames from the remaining 20 videos were divided among training and validation. Network performance was evaluated via mean average precision (mAP), which is defined as the area under the precision/recall curve. Samples were considered correctly identified when the intersection over union (IoU) between the ground truth and predicted bounding boxes was greater than 0.5. Results: The overall mAP of the network was 0.63. Toolspecific mAPs ranged between 0.49 and 0.96 (Table 1). The high accuracy in detection of the cystotome needle is likely due to the distinct size and shape of the tool tip. The diamond keratome had the lowest mAP of any of the tools recognized, however this may be attributed to variations in the appearance of the tool tip (Figure 2). Conclusions: The FRCNN was able to recognize the surgical tools used in cataract surgery with reasonably high accuracy. Now that we know the network can sufficiently recognize the surgical tools, our next goal is to use this network to compute motion-based performance metrics. Future work seeks to validate these performance metrics against those obtained from sensor-based tracking and against expert evaluations. This serves as a first step towards providing consistent and accessible feedback for future trainees learning cataract surgery.\ 

}, author = {H.Y. Lee}, editor = {R. Hisey} } @conference {ODriscoll2022a, title = {Feasibility of object detection for skill assessment in central venous catheterization}, booktitle = {SPIE Medical Imaging}, year = {2022}, month = {02/2022}, publisher = {SPIE Medical Imaging}, organization = {SPIE Medical Imaging}, address = {San Diego}, abstract = {

Purpose: Computer-assisted surgical skill assessment methods have traditionally relied on tracking tool motion with physical sensors. These tracking systems can be expensive, bulky, and impede tool function. Recent advances in object detection networks have made it possible to quantify tool motion using only a camera. These advances open the door for a low-cost alternative to current physical tracking systems for surgical skill assessment. This study determines the feasibility of using metrics computed with object detection by comparing them to widely accepted metrics computed using traditional tracking methods in central venous catheterization. Methods: Both video and tracking data were recorded from participants performing central venous catheterization on a venous access phantom. A Faster Region-Based Convolutional Neural Network was trained to recognize the ultrasound probe and syringe on the video data. Tracking-based metrics were computed using the Perk Tutor extension of 3D Slicer. The path length and usage time for each tool were then computed using both the video and tracking data. The metrics from object detection and tracking were compared using Spearman rank correlation. Results: The path lengths had a rank correlation coefficient of 0.22 for the syringe (p\<0.03) and 0.35 (p\<0.001) for the ultrasound probe. For the usage times, the correlation coefficient was 0.37 (p\<0.001) for the syringe and 0.34 (p\<0.001) for the ultrasound probe. Conclusions: The video-based metrics correlated significantly with the tracked metrics, suggesting that object detection could be a feasible skill assessment method for central venous catheterization.

}, keywords = {central venous catheterization, computer-assisted skill assessment, Object detection}, author = {Olivia O{\textquoteright}Driscoll and R. Hisey and M. Holden and Daenis Camire and Jason Erb and Daniel Howes and Tamas Ungi and Gabor Fichtinger} } @article {ODriscoll2022b, title = {Feasibility of using object detection for performance assessment in central venous catherization}, year = {2022}, month = {02/2022}, author = {Olivia O{\textquoteright}Driscoll and R. Hisey and M. Holden and Daenis Camire and Jason Erb and Daniel Howes and Tamas Ungi and Gabor Fichtinger} } @article {Klosa2022b, title = {Identifying tissues for task recognition in training of open inguinal hernia repairs}, year = {2022}, month = {02/2022}, author = {Elizabeth Klosa and R. Hisey and Tahmina Nazari and Theo Wiggers and Boris Zevin and Tamas Ungi and Gabor Fichtinger} } @conference {connolly2022b, title = {An open-source testbed for developing image-guided robotic tumor-bed inspection}, booktitle = {Imaging Network of Ontario (ImNO) Symposium}, year = {2022}, author = {Laura Connolly and Amoon Jamzad and Arash Nikniazi and Rana Poushimin and Andras Lasso and Kyle R. Sunderland and Tamas Ungi and Jean Michel Nunzi and John Rudan and Gabor Fichtinger and Parvin Mousavi} } @conference {Klosa2022a, title = {Tissue segmentation for workflow recognition in open inguinal hernia repair training}, booktitle = {SPIE Medical Imaging}, year = {2022}, month = {02/2022}, publisher = {SPIE Medical Imaging}, organization = {SPIE Medical Imaging}, address = {San Diego}, abstract = {PURPOSE: As medical education adopts a competency-based training method, experts are spending substantial amounts of time instructing and assessing trainees{\textquoteright} competence. In this study, we look to develop a computer-assisted training platform that can provide instruction and assessment of open inguinal hernia repairs without needing an expert observer. We recognize workflow tasks based on the tool-tissue interactions, suggesting that we first need a method to identify tissues. This study aims to train a neural network in identifying tissues in a low-cost phantom as we work towards identifying the tool-tissue interactions needed for task recognition. METHODS: Eight simulated tissues were segmented throughout five videos from experienced surgeons who performed open inguinal hernia repairs on phantoms. A U-Net was trained using leave-one-user-out cross validation. The average F-score, false positive rate and false negative rate were calculated for each tissue to evaluate the U-Net{\textquoteright}s performance. RESULTS: Higher F-scores and lower false negative and positive rates were recorded for the skin, hernia sac, spermatic cord, and nerves, while slightly lower metrics were recorded for the subcutaneous tissue, Scarpa{\textquoteright}s fascia, external oblique aponeurosis and superficial epigastric vessels. CONCLUSION: The U-Net performed better in recognizing tissues that were relatively larger in size and more prevalent, while struggling to recognize smaller tissues only briefly visible. Since workflow recognition does not require perfect segmentation, we believe our U-Net is sufficient in recognizing the tissues of an inguinal hernia repair phantom. Future studies will explore combining our segmentation U-Net with tool detection as we work towards workflow recognition.}, keywords = {open inguinal hernia repair, segmentation, surgical training, U-Net, workflow recognition}, author = {Elizabeth Klosa and R. Hisey and Tahmina Nazari and Theo Wiggers and Boris Zevin and Tamas Ungi and Gabor Fichtinger} } @article {Hisey2021a, title = {Central Line Tutor: using computer vision workflow recognition in a central venous catheterization training system}, year = {2021}, month = {02/2021}, author = {R. Hisey and Daenis Camire and Jason Erb and Daniel Howes and Gabor Fichtinger and Tamas Ungi} } @article {Fichtinger2021, title = {Design of an Ultrasound-Navigated Prostate Cancer Biopsy System for Nationwide Implementation in Senegal}, journal = {Journal of Imaging}, volume = {7}, number = {8}, year = {2021}, month = {08/2021}, pages = {154}, abstract = {

This paper presents the design of NaviPBx, an ultrasound-navigated prostate cancer biopsy system. NaviPBx is designed to support an affordable and sustainable national healthcare program in Senegal. It uses spatiotemporal navigation and multiparametric transrectal ultrasound to guide biopsies. NaviPBx integrates concepts and methods that have been independently validated previously in clinical feasibility studies and deploys them together in a practical prostate cancer biopsy system. NaviPBx is based entirely on free open-source software and will be shared as a free open-source program with no restriction on its use. NaviPBx is set to be deployed and sustained nationwide through the Senegalese Military Health Service. This paper reports on the results of the design process of NaviPBx. Our approach concentrates on {\textquotedblleft}frugal technology{\textquotedblright}, intended to be affordable for low{\textendash}middle income (LMIC) countries. Our project promises the wide-scale application of prostate biopsy and will foster time-efficient development and programmatic implementation of ultrasound-guided diagnostic and therapeutic interventions in Senegal and beyond.

}, issn = {2313-433X}, doi = {10.3390/jimaging7080154}, url = {https://www.mdpi.com/2313-433X/7/8/154}, author = {Gabor Fichtinger and Parvin Mousavi and Tamas Ungi and Fenster, Aaron and Purang Abolmaesumi and Gernot Kronreif and Ruiz-Alzola, Juan and Ndoye, Alain and Diao, Babacar and Ron Kikinis} } @conference {CWu2021a, title = {Development of an open-source system for prostate biopsy training in Senegal}, booktitle = {SPIE Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling}, volume = {11598}, year = {2021}, month = {02/2021}, author = {Wu, Catherine O. and Diao, Babacar and Tamas Ungi and Alireza Sedghi and Ron Kikinis and Parvin Mousavi and Gabor Fichtinger} } @conference {ODriscoll2021a, title = {Object detection to compute performance metrics for skill assessment in central venous catheterization}, booktitle = {SPIE Medical Imaging}, year = {2021}, url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11598/1159816/Object-detection-to-compute-performance-metrics-for-skill-assessment-in/10.1117/12.2581889.short?SSO=1}, author = {Olivia O{\textquoteright}Driscoll and R. Hisey and Daenis Camire and Jason Erb and Daniel Howes and Gabor Fichtinger and Tamas Ungi} } @conference {Connolly2021, title = {An open-source platform for cooperative semi-autonomous robotic surgery}, booktitle = {IEEE International Conference on Autonomous Systems}, year = {2021}, month = {10/2021}, publisher = {IEEE}, organization = {IEEE}, address = {Montreal, Quebec}, doi = {https://doi.org/10.1109/ICAS49788.2021.9551149}, author = {Laura Connolly and Anton Degeut and Kyle R. Sunderland and Andras Lasso and Tamas Ungi and John Rudan and Russell H. Taylor and Parvin Mousavi and Gabor Fichtinger} } @article {Connolly2021b, title = {A platform for robot-assisted Intraoperative imaging in breast conserving surgery}, year = {2021}, month = {2021}, publisher = {Imaging Network of Ontario Symposium}, address = {Online}, author = {Laura Connolly and Kyle R. Sunderland and Andras Lasso and Anton Degeut and Tamas Ungi and John Rudan and Russell H. Taylor and Parvin Mousavi and Gabor Fichtinger} } @article {ODriscoll2021b, title = {Surgical tool tracking with object detection for performance assessment in central venous catheterization}, year = {2021}, author = {Olivia O{\textquoteright}Driscoll and R. Hisey and Daenis Camire and Jason Erb and Daniel Howes and Gabor Fichtinger and Tamas Ungi} } @article {Hisey2021b, title = {System for central venous catheterization training using computer vision-based workflow feedback}, journal = {IEEE Transactions on Biomedical Engineering}, year = {2021}, month = {10/2021}, author = {R. Hisey and Daenis Camire and Jason Erb and Daniel Howes and Gabor Fichtinger and Tamas Ungi} } @article {Poole2021, title = {Training in soft tissue resection using real-time visual computer navigation feedback from the Surgery Tutor: A randomized controlled trial}, journal = {Surgery}, year = {2021}, month = {12/2021}, abstract = {

Background In competency-based medical education, surgery trainees are often required to learn procedural skills in a simulated setting before proceeding to the clinical environment. The Surgery Tutor computer navigation platform allows for real-time proctor-less assessment of open soft tissue resection skills; however, the use of this platform as an aid in acquisition of procedural skills is yet to be explored. Methods In this prospective randomized controlled trial, 20 final year medical students were randomized to receive either training with real-time computer navigation feedback (Intervention, n\ = 10) or simulation training without navigation feedback (Control, n\ = 10) during resection of simulated non-palpable soft tissue tumors. Real-time computer navigation feedback allowed participants to visualize the position of their scalpel relative to the tumor. Computer navigation feedback was removed for postintervention assessment. Primary outcome was positive margin rate. Secondary outcomes were procedure time, mass of tissue excised, number of scalpel motions, and distance traveled by the scalpel. Results Training with real-time computer navigation resulted in a significantly lower positive margin rate as compared to training without navigation feedback (0\% vs 40\%, P\ = .025). All other performance metrics were not significantly different between the 2 groups. Participants in the intervention group displayed significant improvement in positive margin rate from baseline to final assessment (80\% vs 0\%, P\ \< .01), whereas participants in the Control group did not. Conclusion Real-time visual computer navigation feedback from the Surgery Tutor resulted in superior acquisition of procedural skills as compared to training without navigation feedback.

}, issn = {0039-6060}, doi = {https://doi.org/10.1016/j.surg.2021.11.037}, url = {https://www.sciencedirect.com/science/article/pii/S0039606021011855}, author = {Meredith Poole and Tamas Ungi and Gabor Fichtinger and Boris Zevin} } @conference {CBarr2021a, title = {Ultrasound Probe Pose Classification for Task Recognition in Central Venous Catheterization}, booktitle = {Imaging Network of Ontario Symposium}, year = {2021}, month = {02/2021}, author = {Colton Barr and R. Hisey and Tamas Ungi and Gabor Fichtinger} } @conference {CBarr2021b, title = {Ultrasound Probe Pose Classification for Task Recognition in Central Venous Catheterization}, booktitle = {43rd Conference of the IEEE Engineering Medicine and Biology Society}, year = {2021}, month = {10/2021}, abstract = {

Central Line Tutor is a system that facilitates real-time feedback during training for central venous catheterization. One limitation of Central Line Tutor is its reliance on expensive, cumbersome electromagnetic tracking to facilitate various training aids, including ultrasound task identification and segmentation of neck vasculature. The purpose of this study is to validate deep learning methods for vessel segmentation and ultrasound pose classification in order to mitigate the system{\textquoteright}s reliance on electromagnetic tracking. A large dataset of segmented and classified ultrasound images was generated from participant data captured using Central Line Tutor. A U-Net architecture was used to perform vessel segmentation, while a shallow Convolutional Neural Network (CNN) architecture was designed to classify the pose of the ultrasound probe. A second classifier architecture was also tested that used the U-Net output as the CNN input. The mean testing set Intersect over Union score for U-Net cross-validation was 0.746 {\textpm} 0.052. The mean test set classification accuracy for the CNN was 92.0\% {\textpm} 3.0, while the U-Net + CNN achieved 92.7\% {\textpm} 2.1\%. This study highlights the potential for deep learning on ultrasound images to replace the current electromagnetic tracking-based methods for vessel segmentation and ultrasound pose classification, and represents an important step towards removing the electromagnetic tracker altogether. Removing the need for an external tracking system would significantly reduce the cost of Central Line Tutor and make it far more accessible to the medical trainees that would benefit from it most.

}, author = {Colton Barr and R. Hisey and Tamas Ungi and Gabor Fichtinger} } @conference {KBarr2020, title = {Automated segmentation of computed tomography colonography images using a 3D U-Net}, booktitle = {SPIE Medical Imaging}, year = {2020}, month = {03/2020}, doi = {https://doi.org/10.1117/12.2549749}, author = {Keiran Barr and Jacob Laframboise and Tamas Ungi and Lawrence Hookey and Gabor Fichtinger} } @conference {Wu2020a, title = {Automatic segmentation of spinal ultrasound landmarks with U-net using multiple consecutive images for input}, booktitle = {SPIE Medical Imaging}, year = {2020}, doi = {10.1117/12.2549584}, author = {Victoria Wu and Tamas Ungi and Kyle R. Sunderland and Grace Pigeau and Abigael Schonewille and Gabor Fichtinger} } @article {Ungi2020, title = {Automatic spine ultrasound segmentation for scoliosis visualization and measurement}, journal = {IEEE Transactions on Biomedical Engineering}, volume = {67}, number = {11}, year = {2020}, month = {03/2020}, pages = {3234 - 3241}, abstract = {

Objective: Integrate tracked ultrasound and AI methods to provide a safer and more accessible alternative to X-ray for scoliosis measurement. We propose automatic ultrasound segmentation for 3-dimensional spine visualization and scoliosis measurement to address difficulties in using ultrasound for spine imaging. Methods: We trained a convolutional neural network for spine segmentation on ultrasound scans using data from eight healthy adult volunteers. We tested the trained network on eight pediatric patients. We evaluated image segmentation and 3-dimensional volume reconstruction for scoliosis measurement. Results: As expected, fuzzy segmentation metrics reduced when trained networks were translated from healthy volunteers to patients. Recall decreased from 0.72 to 0.64 (8.2\% decrease), and precision from 0.31 to 0.27 (3.7\% decrease). However, after finding optimal thresholds for prediction maps, binary segmentation metrics performed better on patient data. Recall decreased from 0.98 to 0.97 (1.6\% decrease), and precision from 0.10 to 0.06 (4.5\% decrease). Segmentation prediction maps were reconstructed to 3-dimensional volumes and scoliosis was measured in all patients. Measurement in these reconstructions took less than 1 minute and had a maximum error of 2.2{\textdegree} compared to X-ray. Conclusion: automatic spine segmentation makes scoliosis measurement both efficient and accurate in tracked ultrasound scans. Significance: Automatic segmentation may overcome the limitations of tracked ultrasound that so far prevented its use as an alternative of X-ray in scoliosis measurement.

}, doi = {10.1109/TBME.2020.2980540}, url = {https://ieeexplore.ieee.org/document/9034149}, author = {Tamas Ungi and Hastings Greer and Kyle R. Sunderland and Victoria Wu and Zachary M C Baum and Christopher Schlenger and Matthew Oetgen and Kevin Cleary and Stephen Aylward and Gabor Fichtinger} } @conference {Connolly2020a, title = {Classification of tumor signatures from electrosurgical vapors using mass spectrometry and machine learning: a feasibility study}, booktitle = {Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions and Modeling}, volume = {11315}, year = {2020}, month = {2020}, publisher = {SPIE}, organization = {SPIE}, address = {Houston, Texas, United States}, doi = {https://doi.org/10.1117/12.2549343}, author = {Laura Connolly and Amoon Jamzad and Martin Kaufmann and Rachel Rubino and Alireza Sedghi and Tamas Ungi and Mark Asselin and Scott Yam and John Rudan and Christopher Nicol and Gabor Fichtinger and Parvin Mousavi} } @conference {Wiercigroch2020a, title = {Force and torque feedback in endoscopic vessel harvesting}, booktitle = {Medical Imaging 2020: Image-Guided Procedures, Robotic Inverventions and Modeling}, volume = {11315}, year = {2020}, month = {2020}, publisher = {SPIE}, organization = {SPIE}, address = {Houston, Texas, United States}, doi = {https://doi.org/10.1117/12.2550160}, author = {Julia Wiercigroch and Keyvan Hashtrudi-Zaad and Tamas Ungi and Gianluigi Bisleri and Gabor Fichtinger} } @article {Laframboise2020a, title = {Open source platform for automated collection of training data to support video-based feedback in surgical simulators}, year = {2020}, publisher = {SPIE}, address = {Houston, United States}, abstract = {

Purpose: Surgical training could be improved by automatic detection of workflow steps. A platform to collect and organize tracking and video data would enable rapid development of deep learning solutions for surgical training. The purpose of this research is to demonstrate 3D Slicer / PLUS Toolkit as a platform for video annotation by identifying and annotating tools interacting with tissues in simulated hernia repair. Methods: Tracking data from an optical tracker and video data from a camera are collected by PLUS and 3D Slicer. To demonstrate the platform in use, we identify tissues during a surgical procedure using a neural network. The tracking data is used to identify what tool is in use. The solution is deployed with a custom Slicer module. Results: This platform allowed the collection and storage of enough tracked video data for training a convolutional neural network (CNN) to detect interactions with tissues and tools. The CNN was trained on this data and applied to new data with a testing accuracy of 98\%. The model{\textquoteright}s predictions can be weighted over several frames with a custom Slicer module to improve accuracy. Conclusion: We found the 3D Slicer and PLUS Toolkit platform to be a viable platform for training and deploying a solution that combines automatic video processing and optical tool tracking. We designed a proof of concept model to identify tissues with a trained CNN in real time along with tracking of surgical tools.

}, keywords = {3D Slicer, AI, annotation, collection, data, deep learning, PLUS, surgical training, video}, author = {Jacob Laframboise and Tamas Ungi and Kyle R. Sunderland and Boris Zevin and Gabor Fichtinger} } @article {Gauvin2019, title = {Real-time electromagnetic navigation for breast-conserving surgery using NaviKnife technology: A matched case-control study}, journal = {The Breast Journal}, volume = {26}, number = {3}, year = {2020}, month = {09/2019}, pages = {399-405}, doi = {10.1111/tbj.13480}, author = {Gabrielle Gauvin and Yeo, Caitlin T and Tamas Ungi and Shaila Merchant and Andras Lasso and Doris Jabs and Thomas Vaughan and John Rudan and Ross Walker and Gabor Fichtinger and C. Jay Engel} } @article {Hisey2020b, title = {Recognizing workflow tasks in central venous catheterization using convolutional neural networks and reinforcement learning}, year = {2020}, month = {06/2020}, pages = {94-95}, author = {R. Hisey and Brian Chen and Daenis Camire and Jason Erb and Daniel Howes and Gabor Fichtinger and Tamas Ungi} } @article {Hisey2020a, title = {Reinforcement learning approach for video-based task recognition in central venous catheterization}, year = {2020}, month = {06/2020}, author = {R. Hisey and Brian Chen and Tamas Ungi and Daenis Camire and Jason Erb and Daniel Howes and Gabor Fichtinger} } @article {Wu2020b, title = {Using multiple frame U-net for automated segmentation of spinal ultrasound images}, year = {2020}, url = {https://www.imno.ca/sites/default/files/ImNO2020Proceedings.pdf}, author = {Victoria Wu and Tamas Ungi and Kyle R. Sunderland and Grace Pigeau and Abigael Schonewille and Gabor Fichtinger} } @conference {CWu2020, title = {Workflow for creation and evaluation of virtual nephrolithotomy training models}, booktitle = {SPIE Medical Imaging Conference 2020}, volume = {11315}, year = {2020}, month = {03/2020}, doi = {10.1117/12.2549354}, author = {Wu, Catherine O. and Kyle R. Sunderland and Filippov, Mihail and Sainsbury, Ben and Gabor Fichtinger and Tamas Ungi} } @conference {Laframboise2019a, title = {Analyzing the curvature of the colon in different patient positions}, booktitle = {SPIE Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling}, volume = {10951}, year = {2019}, month = {03/2019}, address = {San Diego, California}, author = {Jacob Laframboise and Tamas Ungi and Andras Lasso and Mark Asselin and M. Holden and Tan, Pearl and Lawrence Hookey and Gabor Fichtinger} } @article {Baum2019b, title = {Assessment of intraoperative neurosurgical planning with the Microsoft HoloLens}, year = {2019}, publisher = {Imaging Network Ontario (ImNO)}, address = {London, Ontario}, author = {Zachary M C Baum and Sarah Ryan and Emily Rae and Andras Lasso and Tamas Ungi and Ron Levy and Gabor Fichtinger} } @article {Baum2020, title = {Augmented reality training platform for neurosurgical burr hole localization}, journal = {Journal of Medical Robotics Research}, volume = {4}, number = {3-4}, year = {2019}, month = {01/2020}, pages = {1942001-1 - 1942001-13}, type = {Special Issue}, abstract = {

Augmented reality (AR) is used in neurosurgery to visualize lesions and plan procedures pre-operatively and intra-operatively, though its use has not been widely adopted in simulation-based neurosurgical training for the same tasks. This work defines metrics to determine performance in drill position and angle identification for neurosurgical training. The metrics were validated intra-operatively and in a simulated training environment, demonstrating that trainees identify drill position and angle faster and more accurately with AR compared to standard techniques. Training using AR and the proposed metrics stands to add value to neurosurgical curricula development.

}, keywords = {augmented reality, competency-based medical education, holography, neurosurgery, surgical planning, visualization}, doi = {https://doi.org/10.1142/S2424905X19420017}, author = {Zachary M C Baum and Andras Lasso and Sarah Ryan and Tamas Ungi and Emily Rae and Boris Zevin and Ron Levy and Gabor Fichtinger} } @article {Hisey2019, title = {Comparison of convolutional neural networks for central venous catheterization tool detection}, year = {2019}, month = {03/2019}, address = {Toronto, Ontario}, author = {R. Hisey and Tamas Ungi and Daenis Camire and Jason Erb and Daniel Howes and Gabor Fichtinger} } @conference {Lund2019a, title = {Controlling virtual views in navigated breast conserving surgery}, booktitle = {SPIE Medical Imaging 2019}, year = {2019}, abstract = {

PURPOSE: Lumpectomy is the resection of a tumor in the breast while retaining as much healthy tissue as possible.
Navigated lumpectomy seeks to improve on the traditional technique by employing computer guidance to achieve the
complete excision of the cancer with optimal retention of healthy tissue. Setting up navigation in the OR relies on the
manual interactions of a trained technician to align three-dimensional virtual views to the patient{\textquoteright}s physical position
and maintain their alignment throughout surgery. This work develops automatic alignment tools to improve the
operability of navigation software for lumpectomies.
METHODS: Preset view buttons were developed to refine view setup to a single interaction. These buttons were
tested by measuring the reduction in setup time and the number of manual interactions avoided through their use. An
auto-center feature was created to ensure that three-dimensional models of anatomy and instruments were in the center
of view throughout surgery. Recorded data from 32 lumpectomy cases were replayed and the number of auto-center
view shifts was counted from the first cautery incision until the completion of the excision of cancerous tissue.
RESULTS: View setup can now be performed in a single interaction compared to an average of 13 interactions
(taking 83 seconds) when performed manually. The auto-center feature was activated an average of 33 times in the
cases studied (n=32).
CONCLUSION: The auto-center feature enhances the operability of the surgical navigation system, reducing the
number of manual interactions required by a technician during the surgery. This feature along with preset camera view
options are instrumental in the shift towards a completely surgeon-operable navigated lumpectomy system.

}, author = {Shaun Lund and Thomas Vaughan and Tamas Ungi and Andras Lasso and Mark Asselin and Caitlin Yeo and C. Jay Engel and Gabor Fichtinger} } @article {Wu2019b, title = {Detection of Spinal Ultrasound Landmarks Using Convolutional Neural Networks}, volume = {14}, year = {2019}, publisher = {Int J CARS}, address = {Rennes, France}, doi = {https://doi.org/10.1007/s11548-019-01969-3}, author = {Victoria Wu and Mark Asselin and Tamas Ungi and Gabor Fichtinger} } @article {Lasso2019, title = {Enhance medical software applications with immersive virtual reality experience}, year = {2019}, month = {10/2019}, address = {Toronto, ON, Canada}, author = {Andras Lasso and Csaba Pinter and Saleh Choueib and Tamas Ungi and Gabor Fichtinger} } @conference {Connolly2019a, title = {Mechanically-Controlled Spectroscopic Imaging for Tissue Classification}, booktitle = {SPIE Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling}, volume = {10951}, year = {2019}, month = {03/2019}, address = {San Diego, California}, doi = {https://doi.org/10.1117/12.2512481}, author = {Laura Connolly and Tamas Ungi and Andras Lasso and Thomas Vaughan and Mark Asselin and Parvin Mousavi and Scott Yam and Gabor Fichtinger} } @article {House2019a, title = {Monitoring volume changes for breast reconstruction surgery using three dimensional optical surface scanning}, year = {2019}, address = {St. Johns, Newfoundland}, author = {Rachael House and M. Kunz and Chantal Valiquette and Andras Lasso and Tamas Ungi and John Rudan and Glykeria Martou and Gabor Fichtinger} } @conference {Asselin2019a, title = {Navigated real-time molecular analysis in the operating theatre, demonstration of concept}, booktitle = {SPIE Medical Imaging 2019}, year = {2019}, abstract = {

PURPOSE: In the operating theatre surgeons are accustomed to using spatially navigated tools in conjunction with
standard clinical imaging during a procedure. This gives them a good idea where they are in the patients{\textquoteright} anatomy but
doesn{\textquoteright}t provide information about the type of tissue they are dissecting. In this paper we demonstrate an integrated
system consisting of a spatially navigated surgical electrocautery combined with real-time molecular analysis of the
dissected tissue using mass spectrometry.
METHODS: Using the 3D Slicer software package, we have integrated a commercially available neurosurgical
navigation system with an intra-operative mass spectrometer (colloquially referred to as the intelligent knife, or iKnife)
that analyzes the charged ions in the smoke created during cauterization. We demonstrate this system using a simulated
patient comprised of an MRI scan from a brain cancer patient deformably registered to a plastic skull model. On the
skull model we placed porcine and bovine tissues to simulate cancerous and healthy tissue, respectively. We built a
PCA/LDA model to distinguish between these tissue types. The tissue classifications were displayed in a spatially
localized manner in the pre-operative imaging, in both 2D and 3D views.
RESULTS: We have demonstrated the feasibility of performing spatially navigated intra-operative analysis of tissues by
mass spectrometry. We show that machine learning can classify our sample tissues, with an average computed
confidence of 99.37 \% for porcine tissue and 99.36\% for bovine tissue.
CONCLUSION: In this paper we demonstrate a proof of concept system for navigated intra-operative molecular
analysis. This system may enable intra-operative awareness of spatially localized tissue classification during dissection,
information that is especially useful in tumor surgeries where margins may not be visible to the unassisted eye.
Keywords: image guided therapy, intra-operative mass spectrometry, iKnife, 3D Slicer, open-source, rapid evaporative
ionization mass spectrometry (REIMS)

}, author = {Mark Asselin and Martin Kaufmann and Julia Wiercigroch and Tamas Ungi and Andras Lasso and John Rudan and Gabor Fichtinger} } @article {Vaughan2019a, title = {Needle Navigation and Catheter Reconstruction for Breast Brachytherapy Using Open Source Software}, journal = {Acta Polytechnica Hungarica}, volume = {16}, number = {8}, year = {2019}, month = {09/2019}, pages = {99-118}, abstract = {

Abstract: Interstitial breast brachytherapy is a method to deliver radiation therapy directly to the site of cancer. It is a challenging procedure because of issues in localizing the seroma, needles, and catheters within the soft tissue. In this paper we present two open-source technologies based on electromagnetic tracking: a navigation system to help target needles using a tracked needle guide, and software for electromagnetic reconstruction of catheter paths. These technologies were validated phantom studies. We found that the navigation system helped a radiation oncologist to target needles more accurately than under ultrasound guidance (60 needles under each condition, 3.8 vs 3.3 mm placement error, p = 0.04) and that reconstructed catheter paths were accurate within 0.6 mm to those determined on CT scans (144 catheters were compared to the 1.2 mm voxel size of CT scans, p \< 0.001). We conclude that these technologies accurately localize anatomy and instruments in our study.

}, author = {Thomas Vaughan and H Brastianos and Tamas Ungi and Andras Lasso and Conrad Falkson and Gabor Fichtinger} } @article {Laframboise2019b, title = {Quantifying the effect of patient position on the curvature of colons}, year = {2019}, publisher = {Imaging Network Ontario (ImNO)}, address = {London, Ontario}, keywords = {colonoscopy, CT, curvature, curves}, author = {Jacob Laframboise and Tamas Ungi and Andras Lasso and Mark Asselin and M. Holden and Tan, Pearl and Lawrence Hookey and Gabor Fichtinger} } @conference {Perrin2019a, title = {Reproducibility of freehand calibrations for ultrasound-guided needle navigation}, booktitle = {SPIE Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling}, volume = {10951}, year = {2019}, address = {San Diego, California}, author = {Sydney Perrin and Zachary M C Baum and Mark Asselin and Underwood, Grace and Saleh Choueib and H. Lia and Tamas Ungi and Andras Lasso and Gabor Fichtinger} } @article {Isen2019a, title = {Retraining MobileNet with highly variable data for tool detection in central venous catheterization}, year = {2019}, publisher = {Imaging Network Ontario (ImNO)}, address = {London, Ontario}, author = {Jonah Isen and R. Hisey and Tamas Ungi and Gabor Fichtinger} } @conference {Baum2019a, title = {Step-wise identification of ultrasound-visible anatomical landmarks for 3D visualization of scoliotic spine}, booktitle = {SPIE Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling}, volume = {10951}, year = {2019}, month = {03/2019}, address = {San Diego, California}, author = {Zachary M C Baum and Ben Church and Andras Lasso and Tamas Ungi and Christopher Schlenger and Daniel P. Borschneck and Parvin Mousavi and Gabor Fichtinger} } @article {Lia2019a, title = {Usability and accuracy of an electromagnetically tracked partial nephrectomy navigation system}, year = {2019}, month = {03/2019}, author = {H. Lia and Zachary M C Baum and Thomas Vaughan and Tamas Ungi and McGregor, T. and Gabor Fichtinger} } @article {Wu2019a, title = {Using Deep Learning for Transverse Process Detection in Spinal Ultrasounds}, year = {2019}, publisher = {Imaging Network Ontario (ImNO)}, address = {London, Ontario}, author = {Victoria Wu and Tamas Ungi and Gabor Fichtinger} } @article {Isen2019b, title = {Utilizing a convolutional neural network for tool detection in central venous catheterization}, year = {2019}, publisher = {Int J CARS}, address = {Rennes, France}, author = {Jonah Isen and R. Hisey and Tamas Ungi and Gabor Fichtinger} } @conference {Wiercigroch2019, title = {Validation of a low-cost adjustable, handheld needle guide for spine interventions}, booktitle = {SPIE Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling}, volume = {10951}, year = {2019}, month = {03/2019}, address = {San Diego, California}, keywords = {3D printing, MR-guided, needle guidance, spinal injection}, author = {Wiercigroch, Julia and Zachary M C Baum and Tamas Ungi and Jan Fritz and Gabor Fichtinger} } @article {Hisey2018b, title = {Assessment of the use of webcam based workflow detection for providing real-time feedback in central venous catheterization training}, year = {2018}, author = {R. Hisey and Tamas Ungi and M. Holden and Zachary M C Baum and Keri, Zsuzsanna and Caitlin McCallum and Daniel Howes and Gabor Fichtinger} } @article {Holden2018b, title = {Configurable Overall Skill Assessment in Ultrasound-Guided Needle Insertion}, year = {2018}, month = {03/2018}, address = {Toronto, Canada}, author = {M. Holden and H. Lia and Sean Xia and Keri, Zsuzsanna and Tamas Ungi and Gabor Fichtinger} } @article {Holden2018a, title = {Objective assessment of colonoscope manipulation skills in colonoscopy training}, journal = {International Journal of Computer Assisted Radiology and Surgery}, volume = {13}, number = {1}, year = {2018}, month = {01/2018}, pages = {105-114}, doi = {https://doi.org/10.1007/s11548-017-1676-4}, author = {M. Holden and Chang Wang and Kyle MacNeil and Ben Church and Lawrence Hookey and Gabor Fichtinger and Tamas Ungi} } @article {Pinter2018a, title = {Real-time transverse process delineation in tracked ultrasound for scoliosis measurement}, year = {2018}, month = {03/2018}, address = {Toronto, Canada}, author = {Csaba Pinter and Bryan Travers and Zachary M C Baum and Tamas Ungi and Andras Lasso and Ben Church and Gabor Fichtinger} } @conference {Pinter2018, title = {Real-time transverse process detection in ultrasound}, booktitle = {SPIE Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling}, year = {2018}, month = {03/2018}, address = {Houston, Texas}, author = {Csaba Pinter and Bryan Travers and Zachary M C Baum and Shahrokh Kamali and Tamas Ungi and Andras Lasso and Ben Church and Gabor Fichtinger} } @conference {Hisey2018, title = {Real-time workflow detection using webcam video for providing real-time feedback in central venous catheterization training}, booktitle = {SPIE Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling}, year = {2018}, author = {R. Hisey and Tamas Ungi and M. Holden and Zachary M C Baum and Keri, Zsuzsanna and Caitlin McCallum and Daniel Howes and Gabor Fichtinger} } @article {Holden2018c, title = {Self-guided training for deep brain stimulation planning using objective assessment}, journal = {International Journal of Computer Assisted Radiology and Surgery}, volume = {13}, number = {7}, year = {2018}, month = {07/2018}, pages = {1129-1139}, doi = {https://doi.org/10.1007/s11548-018-1753-3}, author = {M. Holden and Yulong Zhao and Claire Haegelen and Caroline Essert and Sara Fernanez-Vidal and Eric Bardinet and Tamas Ungi and Gabor Fichtinger and Pierre Jannin} } @conference {Asselin2018a, title = {Towards webcam-based tracking for interventional navigation}, booktitle = {SPIE Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling}, year = {2018}, month = {03/2018}, address = {Houston, Texas}, author = {Mark Asselin and Andras Lasso and Tamas Ungi and Gabor Fichtinger} } @conference { Asselin2018c, title = {A Training Tool for Ultrasound-Guided Central Line Insertion with Webcam-Based Position Tracking}, booktitle = {Simulation, Image Processing, and Ultrasound Systems for Assisted Diagnosis and Navigation}, year = {2018}, abstract = {

PURPOSE: This paper describes an open-source ultrasound-guided central line insertion training system. Modern clinical guidelines are increasingly recommending ultrasound guidance for this procedure due to the decrease in morbidity it provides. However, there are no adequate low-cost systems for helping new clinicians train their inter-hand coordination for this demanding procedure. METHODS: This paper details a training platform which can be recreated with any standard ultrasound machine using inexpensive components. We describe the hardware, software, and calibration procedures with the intention that a reader can recreate this system themselves. RESULTS: The reproducibility and accuracy of the ultrasound calibration for this system was examined. We found that across the ultrasound image the calibration error was less than 2\ mm. In a small feasibility study, two participants performed 5 needle insertions each with an average of slightly above 2\ mm error. CONCLUSION: We conclude that the accuracy of the system is sufficient for clinician training.

}, doi = {10.1007/978-3-030-01045-4_2}, url = {http://dx.doi.org/10.1007/978-3-030-01045-4_2}, author = {Mark Asselin and Tamas Ungi and Andras Lasso and Gabor Fichtinger} } @article {Baum2018b, title = {Ultrasound-based vertebral landmark localization using deformable spine models}, year = {2018}, publisher = {Imaging Network Ontario (ImNO)}, address = {Toronto, Canada}, author = {Zachary M C Baum and Tamas Ungi and Andras Lasso and Ben Church and Christopher Schlenger and Gabor Fichtinger} } @article {Yeo2017, title = {Utility of 3D Reconstruction of 2D Liver Computed Tomography/Magnetic Resonance Images as a Surgical Planning Tool for Residents in Liver Resection Surgery}, journal = {Journal of Surgical Education}, volume = {75}, number = {3}, year = {2018}, month = {08/2017}, pages = {792-797}, abstract = {

Objective

A fundamental aspect of surgical planning in liver resections is the identification of key vessel tributaries to preserve healthy liver tissue while fully resecting the tumor(s). Current surgical planning relies primarily on the surgeon{\textquoteright}s ability to mentally reconstruct 2D computed tomography/magnetic resonance (CT/MR) images into 3D and plan resection margins. This creates significant cognitive load, especially for trainees, as it relies on image interpretation, anatomical and surgical knowledge, experience, and spatial sense. The purpose of this study is to determine if 3D reconstruction of preoperative CT/MR images will assist resident-level trainees in making appropriate operative plans for liver resection surgery.

Design

Ten preoperative patient CT/MR images were selected. Images were case-matched, 5 to 2D planning and 5 to 3D planning. Images from the 3D group were segmented to create interactive digital models that the resident can manipulate to view the tumor(s) in relation to landmark hepatic structures. Residents were asked to evaluate the images and devise a surgical resection plan for each image. The resident alternated between 2D and 3D planning, in a randomly generated order. The primary outcome was the accuracy of resident{\textquoteright}s plan compared to expert opinion. Time to devise each surgical plan was the secondary outcome. Residents completed a prestudy and poststudy questionnaire regarding their experience with liver surgery and the 3D planning software.

Setting and Participants

Senior level surgical residents from the Queen{\textquoteright}s University General Surgery residency program were recruited to participate.

Results

A total of 14 residents participated in the study. The median correct response rate was 2 of 5 (40\%; range: 0-4) for the 2D group, and 3 of 5 (60\%; range: 1-5) for the 3D group (p \< 0.01). The average time to complete each plan was 156 {\textpm} 107 seconds for the 2D group, and 84 {\textpm} 73 seconds for the 3D group (p \< 0.01). A total 13 of 14 residents found the 3D model easier to use than the 2D. Most residents noticed a difference between the 2 modalities and found that the 3D model improved their confidence with the surgical plan proposed.

Conclusions

The results of this study show that 3D reconstruction for liver surgery planning increases accuracy of resident surgical planning and decreases amount of time required. 3D reconstruction would be a useful model for improving trainee understanding of liver anatomy and surgical resection, and would serve as an adjunct to current 2D planning methods. This has the potential to be developed into a module for teaching liver surgery in a competency-based medical curriculum.

}, doi = {http://dx.doi.org/10.1016/j.jsurg.2017.07.031}, author = {Yeo, Caitlin T and MacDonald, Andrew and Tamas Ungi and Andras Lasso and Diederick Jalink and Boris Zevin and Gabor Fichtinger and Nanji, Sulaiman} } @conference {Baum2018a, title = {Visual aid for identifying vertebral landmarks in ultrasound}, booktitle = {SPIE Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling}, volume = {10576}, year = {2018}, month = {03/2018}, pages = {105760Z}, address = {Houston, Texas}, doi = {10.1117/12.2292820}, author = {Zachary M C Baum and Tamas Ungi and Andras Lasso and Ben Church and Christopher Schlenger and Gabor Fichtinger} } @article {Harish2017b, title = {An application of redundant sensors for intraoperative electromagnetic tracking error monitoring}, year = {2017}, address = {London, ON, Canada}, author = {Vinyas Harish and E Bibic and Andras Lasso and M. Holden and Thomas Vaughan and Zachary M C Baum and Tamas Ungi and Gabor Fichtinger} } @article {Holden2017a, title = {Assessing Technical Competency in Simulated Colonoscopy Using Joint Motion Analysis}, year = {2017}, month = {03/2017}, address = {London, Ontario}, author = {M. Holden and Chang Wang and Kyle MacNeil and Ben Church and Lawrence Hookey and Gabor Fichtinger and Tamas Ungi} } @article {Brastianos2016c, title = {Demonstration of Catheter Insertion Using Electromagnetic Guidance in Breast Brachytherapy}, volume = {Radiother Oncol 123}, year = {2017}, month = {05/2017}, pages = {S91}, author = {Harry Brastianos and Thomas Vaughan and Andras Lasso and Mary Westerland and James Gooding and Tamas Ungi and Gabor Fichtinger and Conrad Falkson} } @article {Vaughan2017b, title = {Electromagnetically-generated catheter paths for breast brachytherapy}, year = {2017}, author = {Thomas Vaughan and Harry Brastianos and Andras Lasso and Tamas Ungi and Conrad Falkson and Gabor Fichtinger} } @article {Baum2017b, title = {Evaluation of a mobile, real-time, tracked augmented reality display for surgical navigation}, year = {2017}, publisher = {Imaging Network Ontario (ImNO)}, address = {London, Canada}, author = {Zachary M C Baum and Tamas Ungi and Andras Lasso and Gabor Fichtinger} } @article {Moult2017, title = {Improved temporal calibration of tracked ultrasound: an open-source solution}, journal = {Journal of Medical Robotics Research}, volume = {2}, number = {04}, year = {2017}, month = {07/2017}, pages = {1750008}, abstract = {

In tracked ultrasound systems, temporal misalignment between image and tracker data results in incorrect image pose. We present a fully automatic temporal calibration. We image a flat plate in water with a tracked probe undergoing periodic uniaxial freehand translation. Using robust line detection scheme, we compute temporal misalignment as difference between probe and corresponding image position. From 240 sequences, standard deviation was under 5ms for standard imaging parameters. Source code is available in Public Library for Ultrasound Research, PLUS (www.plustoolkit.org).

}, author = {Eric Moult and Andras Lasso and Tamas Ungi and Csaba Pinter and Mattea Welch and Gabor Fichtinger} } @conference {Kamali2017a, title = {Localization of the transverse processes in ultrasound for spinal curvature measurement}, booktitle = {SPIE Medical Imaging}, volume = {10135}, year = {2017}, month = {02/2017}, pages = {101350I-101350I-7}, abstract = {

PURPOSE: In scoliosis monitoring, tracked ultrasound has been explored as a safer imaging alternative to traditional radiography. The use of ultrasound in spinal curvature measurement requires identification of vertebral landmarks such as transverse processes, but as bones have reduced visibility in ultrasound imaging, skeletal landmarks are typically segmented manually, which is an exceedingly laborious and long process. We propose an automatic algorithm to segment and localize the surface of bony areas in the transverse process for scoliosis in ultrasound.METHODS: The algorithm uses cascade of filters to remove low intensity pixels, smooth the image and detect bony edges. By applying first differentiation, candidate bony areas are classified. The average intensity under each area has a correlation with the possibility of a shadow, and areas with strong shadow are kept for bone segmentation. The segmented images are used to reconstruct a 3-D volume to represent the whole spinal structure around the transverse processes. RESULTS: A comparison between the manual ground truth segmentation and the automatic algorithm in 50 images showed 0.17 mm average difference. The time to process all 1,938 images was about 37 Sec. (0.0191 Sec. / Image), including reading the original sequence file.CONCLUSION: Initial experiments showed the algorithm to be sufficiently accurate and fast for segmentation transverse processes in ultrasound for spinal curvature measurement. An extensive evaluation of the method is currently underway on images from a larger patient cohort and using multiple observers in producing ground truth segmentation.

}, doi = {10.1117/12.2256007}, url = {http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=2615387}, author = {Shahrokh Kamali and Tamas Ungi and Andras Lasso and Christina Yan and Lougheed, Matthew and Gabor Fichtinger} } @conference {Harish2017a, title = {Monitoring electromagnetic tracking error using redundant sensors}, booktitle = {SPIE Medical Imaging 2017}, year = {2017}, publisher = {SPIE Society for Optics and Photonics}, organization = {SPIE Society for Optics and Photonics}, address = {Orlando, FL, USA}, abstract = {

PURPOSE: The intraoperative measurement of tracking error is crucial to ensure the reliability of electromagnetically navigated procedures. For intraoperative use, methods need to be quick to set up, easy to interpret, and not interfere with the ongoing procedure. Our goal was to evaluate the feasibility of using redundant electromagnetic sensors to alert users to tracking error in a navigated intervention setup. METHODS: Electromagnetic sensors were fixed to a rigid frame around a region of interest and on surgical tools. A software module was designed to detect tracking error by comparing real-time measurements of the differences between inter-sensor distances and angles to baseline measurements. Once these measurements were collected, a linear support vector machine-based classifier was used to predict tracking errors from redundant sensor readings. RESULTS: Measuring the deviation in the reported inter-sensor distance and angle between the needle and cautery served as a valid indicator for electromagnetic tracking error. The highest classification accuracy, 86\%, was achieved based on readings from the cautery when the two sensors on the cautery were close together. The specificity of this classifier was 93\% and the sensitivity was 82\%. CONCLUSION: Placing redundant electromagnetic sensors in a workspace seems to be feasible for the intraoperative detection of electromagnetic tracking error in controlled environments. Further testing should be performed to optimize the measurement error threshold used for classification in the support vector machine, and improve the sensitivity of our method before application in real procedures.\ 

}, author = {Vinyas Harish and Eden Bibic and Andras Lasso and M. Holden and Thomas Vaughan and Zachary M C Baum and Tamas Ungi and Gabor Fichtinger} } @conference {Holden2017b, title = {Overall Proficiency Assessment in Point-of-Care Ultrasound Interventions: The Stopwatch is not Enough}, booktitle = {Point-of-Care Ultrasound Workshop}, volume = {LNCS 10549}, year = {2017}, month = {09/2017}, pages = {146-153}, publisher = {Springer}, organization = {Springer}, address = {Quebec City, QC}, author = {M. Holden and Keri, Zsuzsanna and Tamas Ungi and Gabor Fichtinger} } @article {Bibic2017a, title = {PLUS Model Catalog: A library of 3D-printable models}, year = {2017}, publisher = {Imaging Network Ontario (ImNO)}, address = {London, Canada}, author = {Eden Bibic and Zachary M C Baum and Vinyas Harish and Tamas Ungi and Andras Lasso and Gabor Fichtinger} } @article {Yan2017b, title = {Quantification of tumor localization needle displacement prior to tumor excision in navigated lumpectomy}, year = {2017}, author = {Christina Yan and Tamas Ungi and Gabrielle Gauvin and Doris Jabs and Andras Lasso and C. Jay Engel and John Rudan and Gabor Fichtinger} } @article {Baum2017c, title = {Real-Time, Tracked, Mobile Augmented Reality Display for Surgical Navigation: Usability Study on Simulated Patients}, year = {2017}, publisher = {National Center for Image Guided Therapy}, address = {Bethesda, USA}, author = {Zachary M C Baum and Tamas Ungi and Andras Lasso and Gabor Fichtinger} } @article {Holden2017c, title = {Reducing Cost and Complexity in Computer-Assisted Training for Lumpectomy}, year = {2017}, month = {11/2017}, address = {Kingston, Canada}, author = {M. Holden and Keri, Zsuzsanna and Tamas Ungi and Caitlin Yeo and Justine Ring and Gabor Fichtinger and Boris Zevin} } @article {Church2017b, title = {Scoliosis visualization using transverse process landmarks}, year = {2017}, keywords = {scoliosis, Spine, Ultrasound, visualization}, author = {Ben Church}, editor = {Andras Lasso and Christopher Schlenger and Daniel P. Borschneck and Parvin Mousavi and Gabor Fichtinger and Tamas Ungi} } @conference {Underwood2017, title = {Skull registration for prone patient position using tracked ultrasound}, booktitle = {SPIE Medical Imaging 2017}, year = {2017}, author = {Underwood, Grace and Tamas Ungi and Zachary M C Baum and Andras Lasso and Gernot Kronreif and Gabor Fichtinger} } @conference {Yan2017a, title = {Study into the displacement of tumor localization needle during navigated breast cancer surgery}, booktitle = {SPIE Medical Imaging}, year = {2017}, publisher = {SPIE}, organization = {SPIE}, address = {Orlando, Florida, USA}, author = {Christina Yan and Tamas Ungi and Gabrielle Gauvin and Doris Jabs and Andras Lasso and C. Jay Engel and John Rudan and Gabor Fichtinger} } @conference {Lia2017, title = {Training with Perk Tutor improves ultrasound-guided in-plane needle insertion skill}, booktitle = {SPIE Medical Imaging}, volume = {10135}, year = {2017}, address = {Orlando, FL}, abstract = {

PURPOSE: The open-source Perk Tutor training platform has been shown to improve trainee performance in interventions that require ultrasound guidance. Our goal was to determine if needle coordination of medical trainees can be improved by training with Perk Tutor compared to training with ultrasound only.\ 

METHODS: Twenty participants with no previous experience were randomized into two groups; the Perk Tutor group and the Control group. The Perk Tutor group had access to the 3D visualization while the Control group used ultrasound only during their training. Performance was analyzed, measured and compared by Perk Tutor with regards to four needle coordination metrics. None of the groups had access to 3D visualization during performance testing.\ 

RESULTS: The needle tracking measurements showed, for the Perk Tutor group, lower average distance between the needle tip and ultrasound (1.2 [0.9 {\textendash} 2.8] mm vs 2.7 [2.3 {\textendash} 4.0] mm, respectively; P = 0.023) and lower maximum distance between the needle tip and ultrasound (2.2 [1.9 {\textendash} 3.2] mm vs 4.6 [3.9 {\textendash} 6.2] mm, respectively; P = 0.013). There was no significant difference in average needle to ultrasound plane angle and maximum needle to ultrasound plane distance. All participants were successful in the procedure.\ 

CONCLUSION: The Perk Tutor group had significantly reduced distance from the needle tip to the ultrasound plane. Training with Perk Tutor can improve trainees{\textquoteright} needle and ultrasound coordination.\ 

}, author = {H. Lia and Keri, Zsuzsanna and M. Holden and Vinyas Harish and Mitchell, Christopher H and Tamas Ungi and Gabor Fichtinger} } @conference {Baum2017a, title = {Usability of a real-time tracked augmented reality display system in musculoskeletal injections}, booktitle = {SPIE Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling}, year = {2017}, month = {03/2017}, doi = {10.1117/12.2255897}, author = {Zachary M C Baum and Tamas Ungi and Andras Lasso and Gabor Fichtinger} } @article {Vaughan2017a, title = {Virtual views controlled by surgical tools for computer assisted interventions}, year = {2017}, author = {Thomas Vaughan and Tamas Ungi and Andras Lasso and Gabrielle Gauvin and C. Jay Engel and John Rudan and Gabor Fichtinger} } @conference {Church2017, title = {Visualization of scoliotic spine using ultrasound-accessible skeletal landmarks}, booktitle = {SPIE Medical Imaging 2017}, year = {2017}, keywords = {scoliosis, Spine, Ultrasound, visualization}, author = {Ben Church and Andras Lasso and Christopher Schlenger and Daniel P. Borschneck and Parvin Mousavi and Gabor Fichtinger and Tamas Ungi} } @conference {Heffernan2016, title = {Accuracy of lesion boundary tracking in navigated breast tumor excision}, booktitle = {SPIE Medical Imaging 2016}, volume = {9786}, year = {2016}, month = {03/2016}, pages = {97860Y-1-6}, address = {San Diego, CA, USA}, doi = {10.1117/12.2217017}, url = {http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=2505913\&resultClick=1}, author = {Emily Heffernan and Tamas Ungi and Thomas Vaughan and Padina Pezeshki and Andras Lasso and Gabrielle Gauvin and John Rudan and C. Jay Engel and Evelyn Morin and Gabor Fichtinger} } @article {Gauvin2016a, title = {Breast-Conserving Surgery using NaviKnife Technology: Pilot Study on Non-Palpable Tumors}, year = {2016}, month = {08/2016}, author = {Gabrielle Gauvin and Tamas Ungi and Andras Lasso and Yeo, Caitlin T and Gabor Fichtinger and Doris Jabs and Ross Walker and Shaila Merchant and John Rudan and C. Jay Engel} } @article {Vaughan2016a, title = {Catheter targeting under electromagnetic guidance in breast brachytherapy {\textendash} a demonstration of concept}, volume = {Int J CARS}, number = {Suppl 1}, year = {2016}, month = {05/2016}, pages = {S60-62}, keywords = {Brachytherapy, breast}, author = {Thomas Vaughan and Harry Brastianos and Andras Lasso and Mary Westerland and Tamas Ungi and Conrad Falkson and Gabor Fichtinger} } @article {Holden2016a, title = {Central Venous Catheterization Curriculum Development via Objective Performance Metrics}, year = {2016}, month = {Submitted}, address = {Toronto, Canada}, author = {M. Holden and Mark Woodcroft and Tim Chaplin and Louise Rang and Melanie Jaeger and Nici Rocca and McGraw, Robert C and Gabor Fichtinger and Tamas Ungi} } @article {Tabanfar2016, title = {Clinical feasibility of pediatric scoliosis monitoring using portable ultrasound}, year = {2016}, author = {Reza Tabanfar and Christina Yan and Kempston, M and Daniel P. Borschneck and Tamas Ungi and Gabor Fichtinger} } @conference {Yan2016a, title = {Comparison of portable and conventional ultrasound imaging in spinal curvature measurement}, booktitle = {SPIE Medical Imaging}, year = {2016}, publisher = {SPIE Medical Imaging 2016}, organization = {SPIE Medical Imaging 2016}, address = {San Diego, CA, United States.}, abstract = {

PURPOSE: In scoliosis monitoring, tracked ultrasound has been explored as a safer imaging alternative to traditional radiography. The use of ultrasound in spinal curvature measurement requires identification of vertebral landmarks, but bones have reduced visibility in ultrasound imaging and high quality ultrasound machines are often expensive and not portable. In this work, we investigate the image quality and measurement accuracy of a low cost and portable ultrasound machine in comparison to a standard ultrasound machine in scoliosis monitoring. \ 


METHODS: \ Two different kinds of ultrasound machines were tested on three human subjects, using the same position tracker and software. Spinal curves were measured in the same reference coordinate system using both ultrasound machines. Lines were defined by connecting two symmetric landmarks identified on the left and right transverse process of the same vertebrae, and spinal curvature was defined as the transverse process angle between two such lines, projected on the coronal plane. \ \ 

RESULTS: \ Three healthy volunteers were scanned by both ultrasound configurations. Three experienced observers localized transverse processes as skeletal landmarks and obtained transverse process angles in images obtained from both ultrasounds. The mean difference per transverse process angle measured was 3.00 {\textpm} 2.1{\textdegree}. 94\% of transverse processes visualized in the Sonix Touch were also visible in the Telemed. \ Inter-observer error in the Telemed was 4.5{\textdegree} and 4.3{\textdegree} in the Sonix Touch. \ \ 

CONCLUSION: Price, convenience and accessibility suggest the Telemed to be a viable alternative in scoliosis monitoring, however further improvements in measurement protocol and image noise reduction must be completed before implementing the Telemed in the clinical setting. \ 

}, author = {Christina Yan and Reza Tabanfar and Michael Kempston and Daniel P. Borschneck and Tamas Ungi and Gabor Fichtinger} } @article {McGraw2015a, title = {Development and evaluation of a simulation-based curriculum for ultrasound guided central venous catheterization}, journal = {Canadian Journal of Emergency Medicine}, volume = {18}, number = {6}, year = {2016}, month = {11/2016}, pages = {405-413}, doi = {10.1017/cem.2016.329}, author = {McGraw, Robert C and Tim Chaplin and Conor McKaigney and Louise Rang and Melanie Jaeger and Damian Redfearn and Colleen Davison and Tamas Ungi and M. Holden and Yeo, Caitlin T and Keri, Zsuzsanna and Gabor Fichtinger} } @article {Vaughan2016b, title = {Electromagnetically-guided catheter insertions in breast phantoms for brachytherapy}, year = {2016}, month = {Submitted}, keywords = {Brachytherapy, breast}, author = {Thomas Vaughan and Harry Brastianos and Andras Lasso and Mary Westerland and Tamas Ungi and Conrad Falkson and Gabor Fichtinger} } @article {Kapur2016, title = {Increasing the impact of medical image computing using community-based open-access hackathons: The NA-MIC and 3D Slicer experience}, journal = {Medical Image Analysis}, volume = {33}, year = {2016}, month = {10/2016}, pages = {176-180}, abstract = {

The National Alliance for Medical Image Computing (NA-MIC) was launched in 2004 with the goal of investigating and developing an open source software infrastructure for the extraction of information and knowledge from medical images using computational methods. Several leading research and engineering groups participated in this effort that was funded by the US National Institutes of Health through a variety of infrastructure grants. This effort transformed 3D Slicer from an internal, Boston-based, academic research software application into a professionally maintained, robust, open source platform with an international leadership and developer and user communities. Critical improvements to the widely used underlying open source libraries and tools{\textemdash}VTK, ITK, CMake, CDash, DCMTK{\textemdash}were an additional consequence of this effort. This project has contributed to close to a thousand peer-reviewed publications and a growing portfolio of US and international funded efforts expanding the use of these tools in new medical computing applications every year. In this editorial, we discuss what we believe are gaps in the way medical image computing is pursued today; how a well-executed research platform can enable discovery, innovation and reproducible science ({\textquotedblleft}Open Science{\textquotedblright}); and how our quest to build such a software platform has evolved into a productive and rewarding social engineering exercise in building an open-access community with a shared vision.

}, keywords = {3D Slicer, Hackathon, Medical image computing, NA-MIC, Open access, Open science, Open source, Project week, Reproducible research}, doi = {http://doi.org/10.1016/j.media.2016.06.035}, author = {Tina Kapur and Steve Pieper and Andras Lasso and Tamas Ungi and Csaba Pinter and Gabor Fichtinger and Ron Kikinis} } @article {Baksh2016a, title = {An inexpensive system for competency-based pericardiocentesis training}, year = {2016}, publisher = { Imaging Network Ontario (ImNO)}, address = {Toronto, Canada}, keywords = {Pericardiocentesis, Phantoms}, author = {A Baksh and Vinyas Harish and Tamas Ungi and R Pal and Gabor Fichtinger} } @conference {Ungi2016c, title = {Intraoperative user interface for navigated breast tumor surgery}, booktitle = {Computer Assisted Radiology and Surgery, 30th International Congress}, volume = {11}, year = {2016}, month = {05/2016}, pages = {S108-9}, publisher = {Springer}, organization = {Springer}, address = {Heidelberg, Germany}, doi = {10.1007/s11548-016-1412-5}, author = {Tamas Ungi and Thomas Vaughan and Gabrielle Gauvin and Padina Pezeshki and Andras Lasso and C. Jay Engel and John Rudan and Gabor Fichtinger} } @article {Harish2016b, title = {A low-cost system for image-guided computer-navigated pericardiocentesis training}, volume = {Int J CARS (2016) 11 (Suppl 1)}, year = {2016}, pages = {S112}, publisher = {CARS}, address = {Heidelberg, Germany}, keywords = {Pericardiocentesis, Phantoms}, author = {Vinyas Harish and A Baksh and Tamas Ungi and R Pal and Gabor Fichtinger} } @conference {Harish2016a, title = {Measurement of electromagnetic tracking error in a navigated breast surgery setup}, booktitle = {SPIE Medical Imaging 2016}, volume = {9786}, year = {2016}, publisher = {SPIE Medical Imaging}, organization = {SPIE Medical Imaging}, address = {San Diego, CA, United States, Feb. 29, 2016}, abstract = {

The measurement of tracking error is crucial to ensure the safety and feasibility of electromagnetically tracked, image-guided procedures. Measurement should occur in a clinical environment because electromagnetic field distortion depends on positioning relative to the field generator and metal objects. However, we could not find an accessible and open-source system for calibration, error measurement, and visualization. We developed such a system and tested it in a navigated breast surgery setup.A pointer tool was designed for concurrent electromagnetic and optical tracking. Software modules were developed for automatic calibration of the measurement system, real-time error visualization, and analysis. The system was taken to an operating room to test for field distortion in a navigated breast surgery setup. Positional and rotational electromagnetic tracking errors were then calculated using optical tracking as a ground truth. Our system is quick to set up and can be rapidly deployed. The process from calibration to visualization also only takes a few minutes. Field distortion was measured in the presence of various surgical equipment. Positional and rotational error in a clean field was approximately 0.90 mm and 0.31{\textdegree}. The presence of a surgical table, an electrosurgical cautery, and anesthesia machine increased the error by up to a few tenths of a millimetre and tenth of a degree.In a navigated breast surgery setup, measurement and visualization of tracking error defines a safe working area in the presence of surgical equipment. Our system is available as an extension for the open-source 3D Slicer platform.

}, author = {Vinyas Harish and A Baksh and Tamas Ungi and Andras Lasso and Zachary M C Baum and G Gauvin and C. Jay Engel and John Rudan and Gabor Fichtinger} } @article {Harish2016c, title = {Monitoring electromagnetic tracking error in computer-navigated breast cancer surgery}, year = {2016}, publisher = {Imaging Network Ontario (ImNO)}, address = {Toronto, Canada}, keywords = {Breast cancer, EM Tracking, Lumpectomy}, author = {Vinyas Harish and A Baksh and Tamas Ungi and Andras Lasso and Zachary M C Baum and G Gauvin and C. Jay Engel and John Rudan and Gabor Fichtinger} } @article {Marker2016, title = {MR-guided perineural injection of the ganglion impar: technical considerations and feasibility}, journal = {Skeletal Radiology}, year = {2016}, month = {01/2016}, abstract = {

Objective

Perineural ganglion impar injections are used in the management of pelvic pain syndromes; however, there is no consensus regarding the optimal image guidance. Magnetic resonance imaging (MRI) provides high soft tissue contrast and the potential to directly visualize and target the ganglion. The purpose of this study was to assess the feasibility of MR-guided percutaneous perineural ganglion impar injections.

Materials and methods

Six MR-guided ganglion impar injections were performed in six human cadavers. Procedures were performed with a clinical 1.5-Tesla MRI system through a far lateral transgluteus approach. Ganglion impar visibility, distance from the sacrococcygeal joint, number of intermittent MRI control steps required to place the needle, target error between the intended and final needle tip location, inadvertent punctures of non-targeted vulnerable structures, injectant distribution, and procedure time were determined.

Results

The ganglion impar was seen on MRI in 4/6 (66\ \%) of cases and located 0.8\ mm cephalad to 16.3\ mm caudad (average 1.2\ mm caudad) to the midpoint of the sacrococcygeal joint. Needle placement required an average of three MRI control steps (range, 2{\textendash}6). The average target error was 2.2 {\textpm} 2.1\ mm. In 6/6 cases (100\ \%), there was appropriate periganglionic distribution and filling of the presacrococcygeal space. No punctures of non-targeted structures occurred. The median procedure time was 20\ min (range, 12{\textendash}29\ min).

Conclusion

Interventional MRI can visualize and directly target the ganglion impar for accurate needle placement and successful periganglionic injection with the additional benefit of no ionizing radiation exposure to patient and staff. Our results support clinical evaluation.

}, url = {http://dx.doi.org/10.1007/s00256-016-2333-7}, author = {David R. Marker and Paweena U-Thainual and Tamas Ungi and Aaron J Flammang and Gabor Fichtinger and Iulian Iordachita and John A Carrino and Jan Fritz} } @article {Ungi2016a, title = {Open-source platforms for navigated image-guided interventions}, journal = {Medical Image Analysis}, volume = {33}, year = {2016}, month = {10/2016}, pages = {181-6}, abstract = {

Navigation technology is changing the clinical standards in medical interventions by making existing procedures more accurate, and new procedures possible. Navigation is based on preoperative or intraoperative imaging combined with 3-dimensional position tracking of interventional tools registered to the images. Research of navigation technology in medical interventions requires significant engineering efforts. The difficulty of developing such complex systems has been limiting the clinical translation of new methods and ideas. A key to the future success of this field is to provide researchers with platforms that allow rapid implementation of applications with minimal resources spent on reimplementing existing system features. A number of platforms have been already developed that can share data in real time through standard interfaces. Complete navigation systems can be built using these platforms using a layered software architecture. In this paper, we review the most popular platforms, and show an effective way to take advantage of them through an example surgical navigation application.

}, doi = {10.1016/j.media.2016.06.011}, author = {Tamas Ungi and Andras Lasso and Gabor Fichtinger} } @article {Baum2016b, title = {Real-time self-calibration of a handheld augmented reality overlay system}, year = {2016}, publisher = {Imaging Network Ontario (ImNO)}, address = {Toronto, Canada}, author = {Zachary M C Baum and Andras Lasso and Tamas Ungi and Gabor Fichtinger} } @conference {Baum2016a, title = {Real-time self-calibration of a tracked augmented reality display}, booktitle = {SPIE Medical Imaging 2016}, volume = {9786}, year = {2016}, publisher = {SPIE Medical Imaging}, organization = {SPIE Medical Imaging}, address = {San Diego, CA, United States, Feb. 29, 2016}, abstract = {

PURPOSE: Augmented reality systems have been proposed for image-guided needle interventions but they have not become widely used in clinical practice due to restrictions such as limited portability, low display refresh rates, and tedious calibration procedures. We propose a handheld tablet-based self-calibrating image overlay system.

METHODS: A modular handheld augmented reality viewbox was constructed from a tablet computer and a semi-transparent mirror. A consistent and precise self-calibration method, without the use of any temporary markers, was designed to achieve an accurate calibration of the system. Markers attached to the viewbox and patient are simultaneously tracked using an optical pose tracker to report the position of the patient with respect to a displayed image plane that is visualized in real-time. The software was built using the open-source 3D Slicer application platform{\textquoteright}s SlicerIGT extension and the PLUS toolkit.

RESULTS: The accuracy of the image overlay with image-guided needle interventions yielded a mean absolute position error of 0.99 mm (95th percentile 1.93 mm) in-plane of the overlay and a mean absolute position error of 0.61 mm (95th percentile 1.19 mm) out-of-plane. This accuracy is clinically acceptable for tool guidance during various procedures, such as musculoskeletal injections.

CONCLUSION: A self-calibration method was developed and evaluated for a tracked augmented reality display. The results show potential for the use of handheld image overlays in clinical studies with image-guided needle interventions.\ 

}, author = {Zachary M C Baum and Andras Lasso and Tamas Ungi and Gabor Fichtinger} } @conference {Gauvin2016a, title = {Safety and Feasibility Study for Real-Time Electromagnetic Navigation in Breast-Conserving Surgery}, booktitle = {Computer Assisted Radiology and Surgery, 30th International Congress}, volume = {11}, year = {2016}, month = {05/2016}, pages = {S106-7}, publisher = {Springer}, organization = {Springer}, address = {Heidelberg, Germany}, doi = {10.1007/s11548-016-1412-5}, author = {Gabrielle Gauvin and Tamas Ungi and Andras Lasso and Ross Walker and John Rudan and Gabor Fichtinger and C. Jay Engel} } @article {MacNeil2016a, title = {System for objectively evaluating colonoscopy procedural skills using motion analysis}, year = {2016}, month = {Submitted}, address = {Toronto, Canada}, author = {Kyle MacNeil and Chang Wang and M. Holden and Tamas Ungi and Gabor Fichtinger and Lawrence Hookey} } @article {Yan2016, title = {Vertebral landmark visualization with portable ultrasound imaging in scoliosis monitoring}, year = {2016}, author = {Christina Yan and Reza Tabanfar and Kempston, M and Daniel P. Borschneck and Tamas Ungi and Gabor Fichtinger} } @conference {Carter2016a, title = {Visual feedback mounted on surgical tool: proof of concept}, booktitle = {SPIE Medical Imaging 2016}, volume = {9786}, year = {2016}, month = {In Press}, address = {San Diego, CA, USA}, author = {Kaci Carter and Thomas Vaughan and M. Holden and Gabrielle Gauvin and Padina Pezeshki and Andras Lasso and Tamas Ungi and Evelyn Morin and John Rudan and C. Jay Engel and Gabor Fichtinger} } @article {Clinkard2015a, title = {Assessment of Lumbar Puncture Skill in Experts and Nonexperts Using Checklists and Quantitative Tracking of Needle Trajectories: Implications for Competency-Based Medical Education}, journal = {Teaching and Learning in Medicine}, volume = {27}, number = {1}, year = {2015}, month = {01/2015}, pages = {51-56}, doi = {10.1080/10401334.2014.979184}, author = {David Clinkard and Eric Moult and M. Holden and Colleen Davison and Tamas Ungi and Gabor Fichtinger and McGraw, Robert C} } @article {Ungi2015c, title = {Clinical Translation of Real Time Cautery Navigation for Breast Surgery}, year = {2015}, month = {06/2015}, pages = {19-20}, isbn = {978-0-9563776-6-1}, author = {Tamas Ungi and Gabrielle Gauvin and Andras Lasso and Yeo, Caitlin T and John Rudan and C. Jay Engel and Gabor Fichtinger} } @article {Keri2015a, title = {Computerized training system for ultrasound-guided lumbar puncture on abnormal spine models: a randomized controlled trial}, journal = {Canadian Journal of Anesthesia}, volume = {62}, year = {2015}, month = {07/2015}, pages = {777-784}, url = {http://www.ncbi.nlm.nih.gov/pubmed/25804431}, author = {Keri, Zsuzsanna and Devin Sydor and Tamas Ungi and M. Holden and McGraw, Robert C and Parvin Mousavi and Daniel P. Borschneck and Gabor Fichtinger and Melanie Jaeger} } @article {Carter2015b, title = {Current sensing for navigated electrosurgery }, year = {2015}, month = {03/2015}, pages = {37}, author = {Kaci Carter and Andras Lasso and Tamas Ungi and Evelyn Morin and Gabor Fichtinger} } @article {AbouSeido2015, title = {Data Management Workflow And Architecture For Simulation-Based Medical Training}, volume = {10}, year = {2015}, month = {06/2015}, pages = {s78}, keywords = {AbouSeido2015}, doi = {10.1007/s11548-015-1213-2}, author = {Nisrin Abou-Seido and Tamas Ungi and Andras Lasso and McGraw, Robert C and Gabor Fichtinger} } @article {Clinkard2015b, title = {The Development and Validation of Hand Motion Analysis to Evaluate Competency in Central Line Catheterization}, journal = {Academic Emergency Medicine}, volume = {22}, number = {2}, year = {2015}, month = {02/2015}, pages = {212-218}, doi = {10.1111/acem.12590}, author = {David Clinkard and M. Holden and Tamas Ungi and David Messenger and Gabor Fichtinger and McGraw, Robert C} } @conference {Lougheed2015, title = {Evaluation metrics for bone segmentation in ultrasound}, booktitle = {SPIE Medical Imaging 2015}, volume = {9415}, year = {2015}, publisher = {SPIE}, organization = {SPIE}, address = {Orlando,FL, United States, Feb. 23, 2015}, author = {Lougheed, Matthew and Gabor Fichtinger and Tamas Ungi} } @article {Yeo2015a, title = {Examination of learning trajectories for simulated lumbar puncture training using hand motion analysis}, journal = {Academic Emergency Medicine}, volume = {22}, number = {10}, year = {2015}, month = {10/2015}, pages = {1187-1195}, doi = {10.1111/acem.12753}, author = {Yeo, Caitlin T and Colleen Davison and Tamas Ungi and M. Holden and Gabor Fichtinger and McGraw, Robert C} } @article {Vaughan2015, title = {Hole filling with oriented sticks in ultrasound volume reconstruction}, journal = {J. Med. Imag.}, volume = {2}, number = {3}, year = {2015}, author = {Thomas Vaughan and Andras Lasso and Tamas Ungi and Gabor Fichtinger} } @conference {Harish2015, title = {Intraoperative visualization and assessment of electromagnetic tracking error}, booktitle = {SPIE Medical Imaging 2015}, volume = {9415}, year = {2015}, pages = {94152H-94152H-6}, address = {Orlando,FL, United States, Feb. 23, 2015}, abstract = {

Electromagnetic tracking allows for increased flexibility in designing image-guided interventions, however it is well understood that electromagnetic tracking is prone to error. Visualization and assessment of the tracking error should take place in the operating room with minimal interference with the clinical procedure. The goal was to achieve this ideal in an open-source software implementation in a plug and play manner, without requiring programming from the user. We use optical tracking as a ground truth. An electromagnetic sensor and optical markers are mounted onto a stylus device, pivot calibrated for both trackers. Electromagnetic tracking error is defined as difference of tool tip position between electromagnetic and optical readings. Multiple measurements are interpolated into the thin-plate B-spline transform visualized in real time using 3D Slicer. All tracked devices are used in a plug and play manner through the open-source SlicerIGT and PLUS extensions of the 3D Slicer platform. Tracking error was measured multiple times to assess reproducibility of the method, both with and without placing ferromagnetic objects in the workspace. Results from exhaustive grid sampling and freehand sampling were similar, indicating that a quick freehand sampling is sufficient to detect unexpected or excessive field distortion in the operating room. The software is available as a plug-in for the 3D Slicer platforms. Results demonstrate potential for visualizing electromagnetic tracking error in real time for intraoperative environments in feasibility clinical trials in image-guided interventions.

}, doi = {10.1117/12.2082330}, url = {http://dx.doi.org/10.1117/12.2082330}, author = {Vinyas Harish and Tamas Ungi and Andras Lasso and MacDonald, Andrew and Nanji, Sulaiman and Gabor Fichtinger} } @conference {Meyer2015, title = {Live ultrasound volume reconstruction using scout scanning}, booktitle = {SPIE Medical Imaging 2015}, volume = {9415}, year = {2015}, pages = {94152A-94152A-9}, abstract = {

Ultrasound-guided interventions often necessitate scanning of deep-seated anatomical structures that may be hard to visualize. Visualization can be improved using reconstructed 3D ultrasound volumes. High-resolution 3D reconstruction of a large area during clinical interventions is challenging if the region of interest is unknown. We propose a two-stage scanning method allowing the user to perform quick low-resolution scouting followed by high-resolution live volume reconstruction. Scout scanning is accomplished by stacking 2D tracked ultrasound images into a low-resolution volume. Then, within a region of interest defined in the scout scan, live volume reconstruction can be performed by continuous scanning until sufficient image density is achieved. We implemented the workflow as a module of the open-source 3D Slicer application, within the SlicerIGT extension and building on the PLUS toolkit. Scout scanning is performed in a few seconds using 3 mm spacing to allow region of interest definition. Live reconstruction parameters are set to provide good image quality (0.5 mm spacing, hole filling enabled) and feedback is given during live scanning by regularly updated display of the reconstructed volume. Use of scout scanning may allow the physician to identify anatomical structures. Subsequent live volume reconstruction in a region of interest may assist in procedures such as targeting needle interventions or estimating brain shift during surgery.

}, doi = {10.1117/12.2081488}, url = {http://dx.doi.org/10.1117/12.2081488}, author = {Meyer, Amelie and Andras Lasso and Tamas Ungi and Gabor Fichtinger} } @article {Ungi2015b, title = {Navigated breast tumor excision using electromagnetically tracked ultrasound and surgical instruments}, journal = {IEEE Transactions on Biomedical Engineering}, year = {2015}, month = {08/2015 }, abstract = {

OBJECTIVE:

Lumpectomy, breast conserving tumor excision, is the standard surgical treatment in early stage breast cancer. A common problem with lumpectomy is that the tumor may not be completely excised, and additional surgery becomes necessary. We investigated if a surgical navigation system using intraoperative ultrasound improves the outcomes of lumpectomy, and if such a system can be implemented in the clinical environment.

METHODS:

Position sensors were applied on the tumor localization needle, the ultrasound probe, and the cautery, and three-dimensional navigation views were generated using real time tracking information. The system was tested against standard wire-localization procedures on phantom breast models by eight surgical residents. Clinical safety and feasibility was tested in six palpable tumor patients undergoing lumpectomy by two experienced surgical oncologists.

RESULTS:

Navigation resulted in significantly less tissue excised compared to control procedures (10.3 {\textpm} 4.4 vs. 18.6 {\textpm} 8.7 g, p = 0.01) and lower number of tumorpositive margins (1/8 vs. 4/8) in the phantom experiments. Excision-tumor distance was also more consistently outside the tumor margins with navigation in phantoms. The navigation system has been successfully integrated in an operating room, and user experience was rated positively by surgical oncologists.

CONCLUSION:

Electromagnetic navigation may improve the outcomes of lumpectomy by making the tumor excision more accurate.

SIGNIFICANCE:

Breast cancer is the most common cancer in women, and lumpectomy is its first choice treatment. Therefore, improvement of lumpectomy outcomes have a significant impact on a large patient population.

}, issn = {0018-9294}, doi = {10.1109/TBME.2015.2466591}, url = {http://www.ncbi.nlm.nih.gov/pubmed/26302503}, author = {Tamas Ungi and Gabrielle Gauvin and Andras Lasso and Yeo, Caitlin T and Padina Pezeshki and Thomas Vaughan and Kaci Carter and John Rudan and C. Jay Engel and Gabor Fichtinger} } @article {Holden2015a, title = {Objective Evaluation Of Sonographic Skill In Focussed Assessment With Sonography For Trauma Examinations}, volume = {10}, year = {2015}, month = {06/2015}, pages = {S79-S80}, address = {Barcelona, Spain}, doi = {10.1007/s11548-015-1213-2}, author = {M. Holden and Tamas Ungi and Conor McKaigney and Colin Bell and Louise Rang and Gabor Fichtinger} } @conference {Carbajal2015, title = {Portable Optically Tracked Ultrasound System for Scoliosis Measurement}, booktitle = {Recent Advances in Computational Methods and Clinical Applications for Spine Imaging (MICCAI 2014)}, volume = {20}, year = {2015}, month = {02/2015}, pages = {37-46}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Cambridge, MA}, abstract = {

Monitoring spinal curvature in adolescent kyphoscoliosis requires regular radiographic examinations; however, the applied ionizing radiation increases the risk of cancer. Ultrasound imaging is favorable over X-ray because it does not emit ionizing radiation. It has been shown in the past that tracked ultrasound can be used to localize vertebral transverse processes as landmarks along the spine to measure curvature angles. Tests have been performed with spine phantoms, but scanning protocol, tracking system, data acquisition and processing time has not been considered in human subjects yet. In this paper, a portable optically tracked ultrasound system for scoliosis measurement is presented. It provides a simple way to acquire data in the clinical environment with the aim of comparing results to current X-ray-based measurement. The workflow of the procedure was tested on volunteers. The customized open-source software is shared with the community as part of our effort to make a clinically practical system.

}, isbn = {978-3-319-14147-3}, doi = {10.1007/978-3-319-14148-0_4}, url = {http://link.springer.com/chapter/10.1007/978-3-319-14148-0_4}, author = {Guillermo Carbajal and {\'A}lvaro G{\'o}mez and Gabor Fichtinger and Tamas Ungi} } @conference {Ungi2015a, title = {Real Time Navigation In Breast Tumor Surgery}, booktitle = {Computer Assisted Radiology and Surgery, 29th International Congress}, volume = {10}, year = {2015}, month = {06/2015}, pages = {S59-60}, doi = {10.1007/s11548-015-1213-2}, author = {Tamas Ungi and Gabrielle Gauvin and Yeo, Caitlin T and C. Jay Engel and John Rudan and Gabor Fichtinger} } @article {Carter2015a, title = {Visual feedback mounted on surgical tool}, year = {2015}, month = {03/2015}, pages = {36}, author = {Kaci Carter and Andras Lasso and Tamas Ungi and Evelyn Morin and Gabor Fichtinger} } @conference {Anand2014b, title = {Design and Development of a Mobile Image Overlay System for Image-Guided Needle Interventions}, booktitle = {36th Annual International Conference of IEEE EMBS}, year = {2014}, month = {Accepted}, publisher = {IEEE EMBS}, organization = {IEEE EMBS}, address = {Chicago, IL, USA}, author = {Manjunath Anand and Franklin King and Tamas Ungi and Andras Lasso and John Rudan and Jagadeesan Jayender and Jan Fritz and John A Carrino and Ferenc Jolesz and Gabor Fichtinger} } @article {Anand2014a, title = {Design of mobile image overlay system for image-guided interventions}, year = {2014}, month = {03/2014}, publisher = {ImNO}, address = {Toronto, Canada. March 24-25}, author = {Manjunath Anand and Franklin King and Tamas Ungi and Andras Lasso and John Rudan and Jagadeesan Jayender and Jan Fritz and Ferenc Jolesz and John A Carrino and Gabor Fichtinger} } @conference {Kotwicz2014, title = {Feasibility of a touch-free user interface for ultrasound snapshot-guided nephrostomy}, booktitle = {SPIE Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling}, volume = {9036}, year = {2014}, month = {03/2014}, pages = {90362F}, publisher = {SPIE}, organization = {SPIE}, address = {San Diego, California, USA}, doi = {10.1117/12.2043564}, author = {Simon Kotwicz Herniczek and Andras Lasso and Tamas Ungi and Gabor Fichtinger} } @article {Holden2014a, title = {Feasibility of Real-Time Workflow Segmentation for Tracked Needle Interventions}, journal = {IEEE Transactions on Biomedical Engineering}, volume = {61}, number = {6}, year = {2014}, month = {06/2014}, pages = {1720-1728}, issn = {0018-9294}, doi = {10.1109/TBME.2014.2301635}, author = {M. Holden and Tamas Ungi and Derek Sargent and McGraw, Robert C and Elvis C. S. Chen and Sugantha Ganapathy and T. M. Peters and Gabor Fichtinger} } @article {Fritz2014, title = {MR-Guided Vertebroplasty With Augmented Reality Image Overlay Navigation}, journal = {Cardiovasc Intervent Radiol}, year = {2014}, month = {04/2014}, doi = {10.1007/s00270-014-0885-2}, author = {Jan Fritz and Paweena U-Thainual and Tamas Ungi and Aaron J Flammang and Sudhir Kathuria and Gabor Fichtinger and Iulian Iordachita and John A Carrino} } @article {Keri2014a, title = {A novel technology for teaching ultrasound-guided lumbar puncture with Perk Tutor}, year = {2014}, month = {03/2014}, address = {Toronto, Ontario, March 23, 2014}, author = {Keri, Zsuzsanna and Devin Sydor and Tamas Ungi and M. Holden and McGraw, Robert C and Daniel P. Borschneck and Melanie Jaeger and Gabor Fichtinger} } @article {Fuoco2014, title = {Percutaneous nephrostomy for dummies: electromagnetic needle guidance with tracked ultrasound snapshots in a simulation model}, year = {2014}, address = {Orlando, FL, USA, May 16-21}, author = {Darren Beiko and Michael Fuoco and Tamas Ungi and Robert Siemens and Gabor Fichtinger} } @article {Lasso2014a, title = {PLUS: Open-source toolkit for ultrasound-guided intervention systems}, journal = {IEEE Transactions on Biomedical Engineering}, volume = {61}, number = {10}, year = {2014}, month = {10/2014}, pages = {2527-2537}, issn = {0018-9294}, doi = {10.1109/TBME.2014.2322864}, author = {Andras Lasso and Tamas Heffter and Adam Rankin and Csaba Pinter and Tamas Ungi and Gabor Fichtinger} } @article {Gauvin2014b, title = {Real-time electromagnetic navigation for breast tumor resection: proof of concept}, year = {2014}, month = {06/2014}, address = {Kingston, ON, Canada}, author = {Gabrielle Gauvin and Yeo, Caitlin T and Tamas Ungi and Gabor Fichtinger and John Rudan and C. Jay Engel} } @article {Gauvin2014c, title = {Real-Time Electromagnetic Navigation for Breast Tumor Resection: Proof of Concept}, year = {2014}, month = {09/2014}, address = {Vancouver, BC, Canada}, author = {Gabrielle Gauvin and Yeo, Caitlin T and Tamas Ungi and C. Jay Engel and Gabor Fichtinger and John Rudan} } @conference {Gauvin2014d, title = {Real-time electromagnetic navigation for breast tumor resection: proof of concept}, booktitle = {The Hamlyn Symposium on Medical Robotics}, year = {2014}, month = {07/2014}, pages = {39-40}, address = {London, UK}, author = {Gabrielle Gauvin and Yeo, Caitlin T and Tamas Ungi and Gabor Fichtinger and John Rudan and C. Jay Engel} } @article {Gauvin2014a, title = {Real-time EM navigated breast conserving surgery: phantom and cadaver experiments}, year = {2014}, month = {09/2014}, address = {Cambridge, MA, USA}, author = {Gabrielle Gauvin and Yeo, Caitlin T and Tamas Ungi and Gabor Fichtinger and Nanji, Sulaiman and John Rudan and C. Jay Engel} } @article {Ungi2014a, title = {Spinal curvature measurement by tracked ultrasound snapshots}, journal = {Ultrasound in Medicine and Biology}, volume = {40}, year = {2014}, month = {02/2014}, pages = {447-54}, author = {Tamas Ungi and Franklin King and Michael Kempston and Keri, Zsuzsanna and Andras Lasso and Parvin Mousavi and John Rudan and Daniel P. Borschneck and Gabor Fichtinger} } @article {Ungi2014b, title = {Tracked ultrasonography snapshots enhance needle guidance for percutaneous renal access: a pilot study}, journal = {J Endourol}, volume = {28}, number = {9}, year = {2014}, month = {05/2014}, pages = {1040-5}, author = {Tamas Ungi and Darren Beiko and Michael Fuoco and Franklin King and M. Holden and Gabor Fichtinger and Robert Siemens} } @inbook {Ungi2014c, title = {Tracked Ultrasound in Navigated Spine Interventions}, booktitle = {Spinal Imaging and Image Analysis}, year = {2014}, month = {12/2014}, pages = {469-94}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, chapter = {15}, address = {Cham, Switzerland}, abstract = {

Ultrasound is an increasingly popular imaging modality in image-guided interventions, due to its safety, accessibility, and low cost. But ultrasound imaging\ has a steep learning curve, and requires significant coordination skills from the operator. It is difficult to interpret cross-sectional anatomy in arbitrary angles, and\ even more challenging to orient a needle with respect to the ultrasound plane. Position tracking technology is a promising augmentation method to ultrasound\ imaging. Both the ultrasound transducer and the needle can be tracked, enabling computer-assisted navigation applications in ultrasound-guided spinal interventions.\ Furthermore, the patient can also be tracked, which enables fusion of other imaging modalities with ultrasound. In this chapter, we first present the technical\ background of tracked ultrasound. We will review how to build research systems from commercially available components and open-source software. Then we will\ review some spine-related applications of tracked ultrasound modality, including procedural skills training, needle navigation for anesthesia, surgical navigation, and\ other potential applications.

}, doi = {10.1007/978-3-319-12508-4_15}, url = {http://link.springer.com/chapter/10.1007/978-3-319-12508-4_15}, author = {Tamas Ungi and Andras Lasso and Gabor Fichtinger} } @article {Ungi2014c, title = {Tracked ultrasound snapshots improve the performance of novices in simulated percutaneous nephrostomy}, year = {2014}, month = {03/2014}, address = {Toronto, Ontario, March 23, 2014}, author = {Gabor Fichtinger and Robert Siemens and M. Holden and Tamas Ungi and Darren Beiko and Michael Fuoco and Franklin King} } @article {Fritz2013a, title = {Augmented reality visualisation using an image overlay system for MR-guided interventions: technical performance of spine injection procedures in human cadavers at 1.5 Tesla}, journal = {European Radiology}, volume = {23}, year = {2013}, month = {01/2013}, pages = {235-245}, issn = {0938-7994}, doi = {10.1007/s00330-012-2569-0}, url = {http://www.ncbi.nlm.nih.gov/pubmed/22797956}, author = {Jan Fritz and Paweena U-Thainual and Tamas Ungi and Aaron J Flammang and Gabor Fichtinger and Iulian Iordachita and John A Carrino} } @article {Fritz2013b, title = {Augmented Reality Visualization Using Image Overlay Technology for MR-Guided Interventions: Cadaveric Bone Biopsy at 1.5 T}, journal = {Investigative Radiology}, volume = {48}, number = {6}, year = {2013}, month = {06/2013}, keywords = {augmented reality, image overlay, interventional MR imaging, MR-guided, MRI guidance, MRI-guided bone biopsy, navigation, osscous biopsy}, url = {http://www.ncbi.nlm.nih.gov/pubmed/23328911}, author = {Jan Fritz and Paweena U-Thainual and Tamas Ungi and Aaron J Flammang and McCarthy, EF. and Gabor Fichtinger and Iulian Iordachita and John A Carrino} } @conference {Welch2013b, title = {Freehand ultrasound calibration: phantom versus tracked pointer}, booktitle = {SPIE}, year = {2013}, month = {02/2013}, address = {Lake Buena Vista, Florida, United States}, keywords = {N-Wire Phantom, Tracked Pointer Calibration, Ultrasound Calibration}, author = {Mattea Welch and Jennifer Andrea and Tamas Ungi and Gabor Fichtinger} } @article {Anand2013a, title = {Mobile Image Overlay System for Image Guided Interventions}, year = {2013}, month = {03/2013}, address = {Washington DC, USA, March 21-23, 2013.}, keywords = {image guided interventions, mobile image overlay, needle placement interventions, percutaneous interventions, static image overlay}, author = {Manjunath Anand and Tamas Ungi and Andras Lasso and Paweena U-Thainual and Jagadeesan Jayender and Jan Fritz and John A Carrino and Ferenc Jolesz and Gabor Fichtinger} } @article {Anand2013c, title = {MOBILE IMAGE OVERLAY SYSTEM (MIOS) FOR IMAGE GUIDED INTERVENTIONS}, year = {2013}, month = {06/2013}, address = {Faculty of Health Sciences, Queen{\textquoteright}s University}, author = {Manjunath Anand and Tamas Ungi and Andras Lasso and Paweena U-Thainual and Jagadeesan Jayender and Jan Fritz and John A Carrino and Ferenc Jolesz and Gabor Fichtinger} } @article {U-Thainual2013a, title = {MR Image Overlay Adjustable Plane System for Musculoskeletal Interventions}, year = {2013}, address = {Toronto, Ontario, Canada, February 4-5, 2013}, keywords = {adjustable plane overlay system, augmented reality system, image overlay, MRI-guided}, author = {Paweena U-Thainual and Jan Fritz and Tamas Ungi and John A Carrino and Gabor Fichtinger and Iulian Iordachita} } @article {U-Thainual2013b, title = {MR image overlay guidance: system evaluation for preclinical use}, journal = {International Journal of Computer Assisted Radiology and Surgery}, volume = {8}, year = {2013}, month = {05/2013}, pages = {365-378}, issn = {1861-6410}, doi = {10.1007/s11548-012-0788-0}, author = {Paweena U-Thainual and Jan Fritz and Choladawan Moonjaita and Tamas Ungi and Aaron J Flammang and John A Carrino and Gabor Fichtinger and Iulian Iordachita} } @article {Bartha2013a, title = {Open Source Ultrasound Simulation Using Surface Meshes}, year = {2013}, month = {02/2013}, address = {Toronto, Canada, February 4th-5th}, keywords = {PLUS toolkit, surface mesh, training, ultrasound simulation}, author = {Laura Bartha and Andras Lasso and Csaba Pinter and Tamas Ungi and Gabor Fichtinger} } @conference {Bartha2013c, title = {Open-Source Surface Mesh-Based Ultrasound Simulator}, booktitle = {CARS 2013}, volume = {8}, year = {2013}, month = {06/2013}, pages = {156}, author = {Laura Bartha and Andras Lasso and Csaba Pinter and Tamas Ungi and Gabor Fichtinger} } @article {Bartha2013b, title = {Open-source surface mesh-based ultrasound-guided spinal intervention simulator}, journal = {International Journal of Computer Assisted Radiology and Surgery}, volume = {8}, year = {2013}, month = {06/2013}, pages = {1043-51}, doi = {10.1007/s11548-013-0901-z}, author = {Laura Bartha and Andras Lasso and Csaba Pinter and Tamas Ungi and Keri, Zsuzsanna and Gabor Fichtinger} } @article {Fuoco2013, title = {Percutaneous nephrostomy made easy: electromagnetic needle guidance with tracked ultrasound snapshots in a simulation model}, volume = {J Endourol 27(Suppl 1)}, year = {2013}, month = {10/2013}, pages = {A100}, address = {New Orleans, USA, October 22-26, 2013}, author = {Michael Fuoco and Tamas Ungi and Robert Siemens and Gabor Fichtinger and Darren Beiko} } @article {Welch2013a, title = {Perk Tutor Improves Ultrasound-Guided Facet Joint Injection Training}, year = {2013}, month = {02/2013}, address = {Toronto, Canada, February 4th-5th}, keywords = {Facet joint injections, Perk Tutor}, author = {Mattea Welch and Eric Moult and Tamas Ungi and McGraw, Robert C and Gabor Fichtinger} } @article {Ungi2013b, title = {Perk Tutor: Open-source platform for ultrasound-guided needle intervention training}, year = {2013}, month = {02/2013}, address = {Toronto, ON, Canada}, author = {Tamas Ungi and Eric Moult and Andras Lasso and McGraw, Robert C and Gabor Fichtinger} } @article {Bartha2013, title = {The Perk Tutor Training Platform Integrated with Simulated Ultrasound}, year = {2013}, month = {03/2013}, author = {Laura Bartha and Andras Lasso and Csaba Pinter and Tamas Ungi and Gabor Fichtinger} } @article {Lasso2013a, title = {Prototyping Clinical Applications with the Public Library for Ultrasound (PLUS) Toolkit and 3D Slicer}, volume = {6}, year = {2013}, month = {03/2013}, pages = {34}, address = {Crystal City, VA, USA}, url = {http://www.ncigt.org/publications/item/view/2334}, author = {Andras Lasso and Tamas Ungi and Csaba Pinter and Tamas Heffter and Adam Rankin and Gabor Fichtinger} } @article {Ungi2013c, title = {Prototyping image-guided therapy applications using the SlicerIGT platform}, volume = {6}, year = {2013}, month = {03/2013}, pages = {61}, address = {Crystal City, VA, USA}, url = {http://www.spl.harvard.edu/publications/item/view/2334}, author = {Tamas Ungi and Junichi Tokuda and Adam Rankin and Andras Lasso and Gabor Fichtinger} } @article {Holden2013a, title = {Real-Time Workflow Segmentation for Needle-Based Interventions}, year = {2013}, month = {02/2013}, address = {Toronto, ON, Canada}, author = {M. Holden and Tamas Ungi and Sargent, D. and McGraw, Robert C and Elvis C. S. Chen and Sugantha Ganapathy and T. M. Peters and Gabor Fichtinger} } @article {Ungi2013a, title = {SlicerIGT: Open-source platform for image-guided needle interventions}, year = {2013}, month = {02/2013}, address = {Toronto, ON, Canada}, author = {Tamas Ungi and Andras Lasso and Csaba Pinter and Adam Rankin and Gabor Fichtinger} } @article {Moult2013b, title = {Temporal calibration of tracked ultrasound}, year = {2013}, month = {02/2013}, address = {Toronto, ON, Canada}, author = {Eric Moult and Tamas Ungi and Csaba Pinter and Mattea Welch and Andras Lasso and Gabor Fichtinger} } @article {Ungi2013e, title = {Ultrasound Snapshots in Percutaneous Pedicle Screw Placement Navigation: A Feasibility Study}, journal = {Clinical Orthopaedics and Related Research}, year = {2013}, month = {2013}, author = {Tamas Ungi and Eric Moult and Joseph H. Schwab and Gabor Fichtinger} } @article {Moult2013a, title = {Ultrasound-guided facet joint injection training using Perk Tutor}, journal = {International Journal of Computer Assisted Radiology and Surgery}, volume = {8}, year = {2013}, month = {09/2013}, pages = {831-6}, doi = {10.1007/s11548-012-0811-5}, url = {http://link.springer.com/article/10.1007\%2Fs11548-012-0811-5}, author = {Eric Moult and Tamas Ungi and Mattea Welch and J. Lu and McGraw, Robert C and Gabor Fichtinger} } @conference {Anand2013b, title = {Workspace Analysis and Calibration Method for Mobile Image Overlay System used for Image-Guided Interventions}, booktitle = {The Hamlyn Symposium on Medical Robotics - 2013}, year = {2013}, month = {06/2013}, author = {Manjunath Anand and Tamas Ungi and Andras Lasso and Paweena U-Thainual and Jagadeesan Jayender and Jan Fritz and John A Carrino and Ferenc Jolesz and Gabor Fichtinger} } @article {Fritz2012c, title = {Augmented Reality Visualization Using Image-Overlay for MR-guided Interventions: Performance Assessment of Paravertebral Sympathetic Perineural Injections in Cadavers at 1.5 Tesla}, year = {2012}, month = {06/2012}, pages = {S509-S511}, address = {Pisa, Italy, June 27-30}, author = {Jan Fritz and Paweena U-Thainual and Tamas Ungi and Aaron J Flammang and Gabor Fichtinger and Iulian Iordachita and John A Carrino} } @article {Fritz2012a, title = {Augmented Reality Visualization Using Image-Overlay for MR-Guided Interventions: Accuracy for Lumbar Spinal Procedures with a 1.5-Tesla MRI scanner.}, journal = {American Journal of Roentgenology}, volume = {198}, year = {2012}, month = {03/2012}, pages = {W266-73}, author = {Jan Fritz and Paweena U-Thainual and Tamas Ungi and Aaron J Flammang and Nathan Bongjoon Cho and Gabor Fichtinger and Iulian Iordachita and John A Carrino} } @article {Fritz2012b, title = {Augmented Reality Visualization with Use of Image Overlay Technology for MR Imaging{\textendash}guided Interventions: Assessment of Performance in Cadaveric Shoulder and Hip Arthrography at 1.5 T}, journal = {Radiology}, volume = {265}, number = {1}, year = {2012}, month = {10/2012}, pages = {254-259}, doi = {10.1148/radiol.12112640}, author = {Jan Fritz and Paweena U-Thainual and Tamas Ungi and Aaron J Flammang and Gabor Fichtinger and Iulian Iordachita and John A Carrino} } @conference {Lasso2012d, title = {Implementation of the PLUS open-source toolkit for translational research of ultrasound-guided intervention systems}, booktitle = {Medical Image Computing and Computer-Assisted Intervention (MICCAI 2012) - Systems and Architectures for Computer Assisted Interventions}, year = {2012}, month = {10/2012}, pages = {1-12}, publisher = {The MIDAS Journal}, organization = {The MIDAS Journal}, address = {Nice, France}, url = {http://hdl.handle.net/10380/3367}, author = {Andras Lasso and Tamas Heffter and Csaba Pinter and Tamas Ungi and Gabor Fichtinger} } @article {U-Thainual2012a, title = {MR Image Overlay Guidance: System Evaluation for Clinical Use}, year = {2012}, month = {06/2012}, pages = {S179-S180}, address = {Pisa, Italy, June 27-30}, keywords = {augmented reality, image overlay, needle guidance, percutaneous interventions}, author = {Paweena U-Thainual and Jan Fritz and Choladawan Moonjaita and Tamas Ungi and Aaron J Flammang and John A Carrino and Gabor Fichtinger and Iulian Iordachita} } @conference {Ungi2012d, title = {Open-source training platform for ultrasound-guided needle insertions}, booktitle = {Medical Image Computing and Computer-Assisted Intervention (MICCAI 2012) - Systems and Architectures for Computer Assisted Interventions}, year = {2012}, month = {10/2012}, address = {Nice, France}, author = {Tamas Ungi and Andras Lasso and Gabor Fichtinger} } @article {Ungi2012c, title = {Perk Tutor: An open-source training platform for ultrasound-guided needle insertions}, journal = {IEEE Trans Biomed Eng}, volume = {59}, year = {2012}, month = {12/2012}, pages = {3475-3481}, issn = {0018-9294}, doi = {10.1109/TBME.2012.2219307}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6304910}, author = {Tamas Ungi and Sargent, D. and Eric Moult and Andras Lasso and Csaba Pinter and McGraw, Robert C and Gabor Fichtinger} } @article {Lasso2012a, title = {PLUS: An open-source toolkit for ultrasound-guided intervention systems development}, year = {2012}, month = {02/2012}, address = {Toronto, ON, Canada}, author = {Andras Lasso and Tamas Heffter and Csaba Pinter and Tamas Ungi and Thomas K. Chen and Alexis Boucharin and Gabor Fichtinger} } @article {Ungi2012b, title = {Spinal Needle Navigation by Tracked Ultrasound Snapshots}, journal = {IEEE Trans Biomed Eng}, volume = {59}, year = {2012}, month = {10/2012}, pages = {2766-2772}, author = {Tamas Ungi and Purang Abolmaesumi and Rayhan Jalal and Mattea Welch and Irene Ayukawa and Simrin Nagpal and Andras Lasso and Melanie Jaeger and Daniel P. Borschneck and Gabor Fichtinger and Parvin Mousavi} } @conference {Holden2012a, title = {Surgical Motion Characterization in Simulated Needle-Insertion Procedures}, booktitle = {SPIE Medical Imaging 2012}, year = {2012}, month = {02/2012}, pages = {8316-31}, address = {San Diego, California, USA}, keywords = {Lumbar Puncture, Markov Models, Needle trajectories, Simulated procedures, Task segmentation}, doi = {http://dx.doi.org/10.1117/12.911003}, author = {M. Holden and Tamas Ungi and Sargent, D. and McGraw, Robert C and Gabor Fichtinger} } @article {Ungi2012a, title = {Ultrasound-guided spinal needle placement on open source platform}, year = {2012}, month = {02/2012}, address = {Toronto, ON, Canada}, author = {Tamas Ungi and Andras Lasso and Mattea Welch and Rayhan Jalal and Simrin Nagpal and Melanie Jaeger and Daniel P. Borschneck and Parvin Mousavi and Gabor Fichtinger} } @conference {Ungi2011b, title = {Augmented Reality Needle Guidance Improves Facet Joint Injection Training}, booktitle = {SPIE Medical Imaging}, volume = {7964}, number = {1}, year = {2011}, month = {03/2011}, pages = {79642E 1-7}, publisher = {SPIE}, organization = {SPIE}, address = {Lake Buena Vista, Florida, USA}, doi = {10.1117/12.877830}, url = {http://link.aip.org/link/?PSI/7964/79642E/1}, author = {Tamas Ungi and Yeo, Caitlin T and Paweena U-Thainual and McGraw, Robert C and Gabor Fichtinger} } @conference {Ungi2011a, title = {Augmented reality training to improve needle-based percutaneous interventions}, booktitle = {ImNO2011 - Imaging Network Ontario Symposium}, year = {2011}, month = {01/2011}, address = {Toronto, ON, Canada, Jan 31-Feb 1}, author = {Tamas Ungi and Paweena U-Thainual and Andras Lasso and Yeo, Caitlin T and McGraw, Robert C and Gabor Fichtinger} } @article {Fritz2011, title = {Augmented reality visualization using image-overlay for MR-guided interventions: assessment of accuracy for lumbar spinal procedures with a 1.5-Tesla MRI scanner}, year = {2011}, month = {09/2011}, address = {Munich, Germany}, author = {Jan Fritz and Paweena U-Thainual and Tamas Ungi and Aaron J Flammang and Nathan Bongjoon Cho and Gabor Fichtinger and Iulian Iordachita and John A Carrino} } @conference {Fritz2011a, title = {Augmented Reality Visualization Using Image-overlay for MR-guided Interventions: Assessment of Performance in Cadaveric Shoulder and Hip Arthrography}, booktitle = {RSNA 97th Scientific Assembly and Annual Meeting}, year = {2011}, month = {07/2012}, address = {Chicago, IL}, doi = {10.1148/radiol.12112640}, url = {http://radiology.rsna.org/content/early/2012/07/24/radiol.12112640.abstract}, author = {Jan Fritz and Paweena U-Thainual and Tamas Ungi and Aaron J Flammang and Nathan Bongjoon Cho and Gabor Fichtinger and Iulian Iordachita and John A Carrino} } @article {Yeo2011, title = {The Effect of Augmented Reality Training on Percutaneous Needle Placement in Spinal Facet Joint Injections}, journal = {IEEE Transactions on Biomedical Engineering}, volume = {58}, year = {2011}, month = {07/2011}, pages = {2031-7}, keywords = {augmented reality, surgical training}, issn = {0018-9294}, doi = {10.1109/TBME.2011.2132131}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5739103\&tag=1}, author = {Yeo, Caitlin T and Tamas Ungi and Paweena U-Thainual and Andras Lasso and McGraw, Robert C and Gabor Fichtinger} } @article {U-Thainual2011, title = {MR Image Overlay Adjustable Plane System for Percutaneous Musculoskeletal Interventions}, volume = {4}, year = {2011}, month = {10/2011}, address = {Arlington VA, October 12-13, 2011}, author = {Paweena U-Thainual and Tamas Ungi and John A Carrino and Gabor Fichtinger and Iulian Iordachita} } @article {Ungi2011c, title = {Perk Station image overlay improves training of facet joint injections}, volume = {4}, year = {2011}, month = {10/2011}, pages = {118}, address = {Arlington VA, October 12-13, 2011}, author = {Tamas Ungi and Paweena U-Thainual and Yeo, Caitlin T and Andras Lasso and McGraw, Robert C and Gabor Fichtinger} } @article {Lasso2011c, title = {PLUS: An open-source toolkit for developing ultrasound-guided intervention systems}, volume = {4}, year = {2011}, month = {10/2011}, pages = {103}, address = {Arlington VA, October 12-13, 2011}, author = {Andras Lasso and Tamas Heffter and Csaba Pinter and Tamas Ungi and Thomas K. Chen and Alexis Boucharin and Gabor Fichtinger} } @article {U-Thainual2010, title = {Evaluation of accuracy and clinical feasibility of the MR-compatible image overlay}, year = {2010}, address = {Leipzig, Germany, September 24-25, 2010}, keywords = {augmented reality, Image overlay system, MRI-guided}, author = {Paweena U-Thainual and Jan Fritz and Nathan Bongjoon Cho and Tamas Ungi and Segundo, AJM and John A Carrino and Gabor Fichtinger and Lewin, JS and Iulian Iordachita} }