@article {Lee2008a, title = {Prostate brachytherapy seed localization with Gaussian blurring and camera self-calibration}, journal = {Medical image computing and computer-assisted intervention (MICCAI)}, volume = {11}, number = {Pt 2}, year = {2008}, pages = {636{\textendash}643}, publisher = {Department of Electrical, Computer Engineering, Johns Hopkins University, Baltimore, MD, USA}, abstract = {

A tomosynthesis-based prostate brachytherapy seed localization method is described Gaussian-blurred images are computed from a limited number of X-ray images, and a 3-D volume is reconstructed by backprojection Candidate seed locations are extracted from the reconstructed volume, false positive seeds are removed by optimizing a local cost function In case where the estimated pose error is large, a self-calibration process corrects the estimation error of the intrinsic camera parameters, the translation of the pose in order to improve the reconstruction Simulation, phantom experiment results imply that the implanted seed locations can be estimated from four or five images depending on the number of seeds The algorithm was also validated using patient data, successfully localizing the implanted seeds

}, keywords = {Artifacts, Brachytherapy, Calibration, Computer-Assisted, Humans, instrumentation/methods/standards, Male, methods, methods/standards, Normal Distribution, Prostatic Neoplasms, Radiographic Image Interpretation, radiography/radiotherapy, Radiotherapy, Tomography, X-Ray Computed}, author = {Junghoon Lee and Xiaofeng Liu and Jerry L. Prince and Gabor Fichtinger} } @article {Hefny2008, title = {Quantification of edematic effects in prostate brachytherapy interventions}, journal = {Medical image computing and computer-assisted intervention (MICCAI)}, volume = {11}, number = {Pt 2}, year = {2008}, pages = {493{\textendash}500}, publisher = {School of Computing, Queen{\textquoteright}s University, Kingston, Ontario, Canada}, abstract = {

We present a quantitative model to analyze the detrimental effects of for edema on the quality of prostate brachytherapy implants We account for both tissue expansion, implant migration by mapping intra-operative ultrasound, post-implant CT We pre-process the ultrasound with a phase congruency filter, and map it to the volume CT using a B-spline deformable mutual information similarity metric To test the method, we implanted a standard training phantom with 48 seeds, imaged the phantom with ultrasound, CT, registered the two for ground truth Edema was simulated by distorting the CT volume by known transformations The objective was to match the distorted implant to the intra-operative ultrasound Performance was measured relative to ground truth We successfully mapped 100\% of deformed seeds to ground truth under edematic expansion up to 40\% of volume growth Seed matching performance was 98\% with random seed migration of 3mm superimposed on 10\% edematic volume growth This method promises to be clinically applicable as the first quantitative analysis tool to measure edematic implant deformations occurring between the operating room, post-operative CT imaging

}, keywords = {adverse effects, adverse effects/instrumentation, Algorithms, Automated, Brachytherapy, complications/radiotherapy, Computer-Assisted, Edema, etiology/radiography, Humans, Male, methods, Pattern Recognition, Prostatic Diseases, Prostatic Neoplasms, Prosthesis Implantation, Radiographic Image Enhancement, Radiographic Image Interpretation, Reproducibility of Results, Sensitivity, Specificity, Tomography, Treatment Outcome, X-Ray Computed}, author = {Mohamed Hefny and Purang Abolmaesumi and Zahra Karim-Aghaloo and David Gobbi and Randy Ellis and Gabor Fichtinger} } @article {Jain2007, title = {Intra-operative 3D guidance in prostate brachytherapy using a non-isocentric C-arm}, journal = {Medical image computing and computer-assisted intervention (MICCAI)}, volume = {10}, number = {Pt 2}, year = {2007}, pages = {9{\textendash}17}, publisher = {Johns Hopkins University, USA}, abstract = {

Intra-operative guidance in Transrectal Ultrasound (TRUS) guided prostate brachytherapy requires localization of inserted radioactive seeds relative to the prostate Seeds were reconstructed using a typical C-arm, and exported to a commercial brachytherapy system for dosimetry analysis Technical obstacles for 3D reconstruction on a non-isocentric C-arm included pose-dependent C-arm calibration; distortion correction; pose estimation of C-arm images; seed reconstruction;, C-arm to TRUS registration In precision-machined hard phantoms with 40-100 seeds, we correctly reconstructed 99 8\% seeds with a mean 3D accuracy of 0 68 mm In soft tissue phantoms with 45-87 seeds, clinically realistic 15 degrees C-arm motion, we correctly reconstructed 100\% seeds with an accuracy of 1 3 mm The reconstructed 3D seed positions were then registered to the prostate segmented from TRUS In a Phase-1 clinical trial, so far on 4 patients with 66-84 seeds, we achieved intra-operative monitoring of seed distribution, dosimetry We optimized the 100\% prescribed iso-dose contour by inserting an average of 3 75 additional seeds, making intra-operative dosimetry possible on a typical C-arm, at negligible additional cost to the existing clinical installation

}, keywords = {Algorithms, Brachytherapy, Computer-Assisted, Equipment Design, Equipment Failure Analysis, Humans, Imaging, instrumentation/methods, Interventional, Intraoperative Care, Male, methods, Prostatic Neoplasms, Prosthesis Implantation, Radiographic Image Enhancement, Radiographic Image Interpretation, Radiography, radiography/radiotherapy, Radiotherapy, Reproducibility of Results, Sensitivity, Specificity, Three-Dimensional, Tomography, X-Ray Computed}, author = {Ameet K. Jain and Anton Degeut and Iulian Iordachita and Gouthami Chintalapani and Jack Blevins and Le Yi and Elwood Armour and E. Clif Burdette and Danny Y Song and Gabor Fichtinger} } @article {Liu2007, title = {Prostate implant reconstruction with discrete tomography}, journal = {Medical image computing and computer-assisted intervention (MICCAI)}, volume = {10}, number = {Pt 1}, year = {2007}, pages = {734{\textendash}742}, publisher = {Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA}, abstract = {

We developed a discrete tomography method for prostate implant reconstructions using only a limited number of X-ray projection images A 3D voxel volume is reconstructed by back-projection, using distance maps generated from the projection images The true seed locations are extracted from the voxel volume while false positive seeds are eliminated using a novel optimal geometry coverage model The attractive feature of our method is that it does not require exact seed segmentation of the X-ray images, it yields near 100\% correct reconstruction from only six images with an average reconstruction accuracy of 0 86 mm (std = 0 46mm)

}, keywords = {Brachytherapy, Computer-Assisted, Humans, Imaging, instrumentation/methods, Male, methods, Prostatic Neoplasms, Prosthesis Implantation, Radiographic Image Enhancement, Radiographic Image Interpretation, radiography/radiotherapy, Three-Dimensional}, author = {Xiaofeng Liu and Ameet K. Jain and Gabor Fichtinger} } @article {Jain2006a, title = {C-arm tracking and reconstruction without an external tracker}, journal = {Medical image computing and computer-assisted intervention (MICCAI)}, volume = {9}, number = {Pt 1}, year = {2006}, pages = {494{\textendash}502}, publisher = {Department of Computer Science, Johns Hopkins University, USA}, abstract = {

For quantitative C-arm fluoroscopy, we have developed a unified mathematical framework to tackle the issues of intra-operative calibration, pose estimation, correspondence, reconstruction, without the use of optical/electromagnetic trackers or precision-made fiducial fixtures Our method uses randomly distributed unknown points in the imaging volume, either naturally present or induced by randomly sticking beads or other simple markers in the image pace After these points are segmented, a high dimensional non-linear optimization computes all unknown parameters for calibration, C-arm pose, correspondence, reconstruction Preliminary phantom experiments indicate an average C-arm tracking accuracy of 0 9 degrees, a 3D reconstruction error of 0 8 mm, with an 80 region of convergence for both the AP, lateral axes The method appears to be sufficiently accurate for many clinical applications, and appealing since it works without any external instrumentation, does not interfere with the workspace

}, keywords = {Algorithms, Calibration, Computer-Assisted, Equipment Design, Equipment Failure Analysis, Fluoroscopy, Imaging, instrumentation/methods, methods, Radiographic Image Enhancement, Radiographic Image Interpretation, Reproducibility of Results, Sensitivity, Specificity, Three-Dimensional}, author = {Ameet K. Jain and Gabor Fichtinger} } @article {Xu2006a, title = {Lung deformation estimation and four-dimensional CT lung reconstruction}, journal = {Journal of Academic Radiology}, volume = {13}, number = {9}, year = {2006}, month = {Sep}, pages = {1082{\textendash}1092}, publisher = {Philips Research North America, 345 Scarborough Road, Briarcliff Manor, NY 10510, USA sheng xu@philips com}, abstract = {

RATIONALE AND OBJECTIVES: Four-dimensional (4D) computed tomography (CT) can be used in radiation treatment planning to account for respiratory motion Current 4D CT techniques have limitations in either spatial or temporal resolution In addition, most of these techniques rely on auxiliary surrogates to relate the time of the CT scan to the patient{\textquoteright}s respiratory phase We propose a 4D CT method for lung applications to overcome these problems MATERIALS AND METHODS: A set of axial scans are taken at multiple table positions to obtain a series of two-dimensional images while the patient is breathing freely Each two-dimensional image is registered to a reference CT volume The deformation of the image with respect to the volume is used to synchronize the image with the respiratory cycle assuming that there is no phase variation along the craniocaudal direction The reconstructed 4D dataset is a series of deformable transformations of the reference volume RESULTS: A synthetic 4D dataset showed that the registration error is less than 5\% of the image deformation A swine study showed that the algorithm can generate better image quality than the image sorting method A respiratory-gated 4D dataset showed that the algorithm{\textquoteright}s result is consistent with the ground truth CONCLUSION: The algorithm can reconstruct good quality 4D images without external surrogates even if the CT scans are acquired under irregular respiratory motion The algorithm may allow for reduced radiation dose to the patient with a limited loss of image quality Although the phase variation exists along the craniocaudal direction, the 4D reconstruction is reasonably accurate

}, keywords = {Algorithms, Artifacts, Computer-Assisted, Humans, Imaging, Information Storage, Lung, methods, Movement, Phantoms, Radiographic Image Enhancement, Radiographic Image Interpretation, Radiography, Reproducibility of Results, Respiratory Mechanics, Retrieval, Sensitivity, Specificity, Three-Dimensional, Tomography, X-Ray Computed}, doi = {10 1016/j acra 2006 05 004}, url = {http://dx doi org/10 1016/j acra 2006 05 004}, author = {Sheng Xu and Russell H. Taylor and Gabor Fichtinger and Kevin Cleary} } @article {Jain2005, title = {C-arm calibration-is it really necessary?}, journal = {Medical image computing and computer-assisted intervention (MICCAI)}, volume = {8}, number = {Pt 1}, year = {2005}, pages = {639{\textendash}646}, abstract = {

C-arm fluoroscopy is modelled as a perspective projection, the parameters of which are estimated through a calibration procedure It has been universally accepted that precise intra-procedural calibration is a prerequisite for accurate quantitative C-arm fluoroscopy guidance Calibration, however, significantly adds to system complexity, which is a major impediment to clinical practice We challenge the status quo by questioning the assumption that precise intra-procedural calibration is really necessary We derived theoretical bounds for the sensitivity of 3D measurements to mis-calibration Experimental results corroborated the theory in that mis-calibration in the focal spot by as much as 50 mm still allows for tracking with an accuracy of 0 5 mm in translation, 0 65 degrees in rotation, and such mis-calibration does not impose any additional error on the reconstruction of small objects

}, keywords = {Algorithms, Calibration, Computer-Assisted, Equipment Failure Analysis, Fluoroscopy, Imaging, instrumentation/methods/standards, methods/standards, Radiographic Image Enhancement, Radiographic Image Interpretation, Reproducibility of Results, Sensitivity, Specificity, Three-Dimensional, United States}, author = {Ameet K. Jain and Ryan Kon and Yu Zhou and Gabor Fichtinger} } @article {Jain2005a, title = {FTRAC}, journal = {Journal of Medical Physics}, volume = {32}, number = {10}, year = {2005}, month = {Oct}, pages = {3185{\textendash}3198}, abstract = {

C-arm fluoroscopy is ubiquitous in contemporary surgery, but it lacks the ability to accurately reconstruct three-dimensional (3D) information A major obstacle in fluoroscopic reconstruction is discerning the pose of the x-ray image, in 3D space Optical/magnetic trackers tend to be prohibitively expensive, intrusive, cumbersome in many applications We present single-image-based fluoroscope tracking (FTRAC) with the use of an external radiographic fiducial consisting of a mathematically optimized set of ellipses, lines, and points This is an improvement over contemporary fiducials, which use only points The fiducial encodes six degrees of freedom in a single image by creating a unique view from any direction A nonlinear optimizer can rapidly compute the pose of the fiducial using this image The current embodiment has salient attributes: small dimensions (3 x 3 x 5 cm); need not be close to the anatomy of interest;, accurately segmentable We tested the fiducial, the pose recovery method on synthetic data, also experimentally on a precisely machined mechanical phantom Pose recovery in phantom experiments had an accuracy of 0 56 mm in translation, 0 33 degrees in orientation Object reconstruction had a mean error of 0 53 mm with 0 16 mm STD The method offers accuracies similar to commercial tracking systems, and appears to be sufficiently robust for intraoperative quantitative C-arm fluoroscopy Simulation experiments indicate that the size can be further reduced to 1 x 1 X 2 cm, with only a marginal drop in accuracy

}, keywords = {Algorithms, Computer-Assisted, Fluoroscopy, Imaging, Information Storage, instrumentation/methods, methods, Phantoms, Radiographic Image Enhancement, Radiographic Image Interpretation, Reproducibility of Results, Retrieval, Sensitivity, Specificity, Three-Dimensional}, author = {Ameet K. Jain and Tabish Mustufa and Yu Zhou and E. Clif Burdette and Gregory Chirikjian and Gabor Fichtinger} } @article {Xu2005b, title = {Lung deformation estimation and four-dimensional CT lung reconstruction}, journal = {Medical image computing and computer-assisted intervention (MICCAI)}, volume = {8}, number = {Pt 2}, year = {2005}, pages = {312{\textendash}319}, abstract = {

Four-dimensional (4D) computed tomography (CT) image acquisition is a useful technique in radiation treatment planning, interventional radiology in that it can account for respiratory motion of lungs Current 4D lung reconstruction techniques have limitations in either spatial or temporal resolution In addition, most of these techniques rely on auxiliary surrogates to relate the time of CT scan to the patient{\textquoteright}s respiratory phase In this paper, we propose a novel 4D CT lung reconstruction, deformation estimation algorithm Our algorithm is purely image based The algorithm can reconstruct high quality 4D images even if the original images are acquired under irregular respiratory motion The algorithm is validated using synthetic 4D lung data Experimental results from a swine study data are also presented

}, keywords = {Algorithms, Animals, Artificial Intelligence, Biological, Computer Simulation, Computer-Assisted, Elasticity, Humans, Imaging, Lung, methods, Models, physiology/radiography, Radiographic Image Enhancement, Radiographic Image Interpretation, Reproducibility of Results, Respiratory Mechanics, Sensitivity, Specificity, Swine, Three-Dimensional, Tomography, X-Ray Computed}, author = {Sheng Xu and Russell H. Taylor and Gabor Fichtinger and Kevin Cleary} } @article {Jain2005e, title = {Matching and reconstruction of brachytherapy seeds using the Hungarian algorithm (MARSHAL)}, journal = {Journal of Medical Physics}, volume = {32}, number = {11}, year = {2005}, month = {Nov}, pages = {3475{\textendash}3492}, abstract = {

Intraoperative dosimetric quality assurance in prostate brachytherapy critically depends on discerning the three-dimensional (3D) locations of implanted seeds The ability to reconstruct the implanted seeds intraoperatively will allow us to make immediate provisions for dosimetric deviations from the optimal implant plan A method for seed reconstruction from segmented C-arm fluoroscopy images is proposed The 3D coordinates of the implanted seeds can be calculated upon resolving the correspondence of seeds in multiple x-ray images We formalize seed-matching as a combinatorial optimization problem, which has salient features: (a) extensively studied solutions by the computer science community; (b) proof for the nonexistence of any polynomial time exact algorithm;, (c) a practical pseudo-polynomial algorithm that mostly runs in O(N3) time using any number of images We prove that two images are insufficient to correctly match the seeds, while a third image renders the matching problem to be of nonpolynomial complexity We utilize the special structure of the problem, propose a pseudopolynomial time algorithm Using three presegmented images, matching, reconstruction of brachytherapy seeds using the Hungarian algorithm achieved complete matching in simulation experiments;, 98 5\% in phantom experiments 3D reconstruction error for correctly matched seeds has a mean of 0 63 mm, and 0 9 mm for incorrectly matched seeds The maximum seed reconstruction error in each implant was typically around 1 32 mm Both on synthetic data, in phantom experiments, matching rate, reconstruction error achieved using presegmented images was found to be sufficient for prostate brachytherapy The algorithm is extendable to deal with arbitrary number of images without any loss in speed or accuracy The algorithm is sufficiently generic to provide a practical solution to any correspondence problem, across different imaging modalities, features

}, keywords = {Algorithms, Automated, Brachytherapy, Calibration, Computer Simulation, Computer-Assisted, Humans, Image Interpretation, Imaging, Male, methods, Models, Numerical Analysis, Pattern Recognition, Phantoms, Prostatic Neoplasms, Radiographic Image Enhancement, Radiographic Image Interpretation, Radiometry, Radiotherapy, Radiotherapy Dosage, Radiotherapy Planning, Reproducibility of Results, Statistical, Theoretical, Three-Dimensional, Time Factors, Ultrasonography}, author = {Ameet K. Jain and Yu Zhou and Tabish Mustufa and E. Clif Burdette and Gregory Chirikjian and Gabor Fichtinger} }