@article {Fedorov2020, title = {Quantitative Imaging Informatics for Cancer Research}, journal = {JCO Clinical Cancer Informatics}, volume = {4}, year = {2020}, pages = {444-453.}, doi = {https://doi.org/10. 1200/CCI.19.00165}, author = {Andriy Fedorov and Reinhard Beichel and Jayashree Kalpathy-Cramer and David Clunie and Michael Onken and J{\"o}rg Riesmeier and Christian Herz and Christian Bauer and Andrew Beers and Jean-ChristopheC. Fillion-Robin and Andras Lasso and Csaba Pinter and Steve Pieper and Marco Nolden and Klaus Maier-Hein and Markus D. Herrmann and Joel Saltz and Fred Prior and Fiona M. Fennessy and John Buatti and Ron Kikinis} } @conference {BarrC2020, title = {Towards portable image guidance and automatic patient registration using an RGB-D camera and video projector}, booktitle = {Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions and Modeling}, volume = {11315}, year = {2020}, publisher = {SPIE}, organization = {SPIE}, address = {Houston, Texas, United States}, doi = {10.1117/12.2549723}, author = {Colton Barr and Andras Lasso and Mark Asselin and Steve Pieper and Faith C. Robertson and William B. Gormley and Gabor Fichtinger} } @article {Lasso2018, title = {Interaction with Volume-Rendered Three-Dimensional Echocardiographic Images in Virtual Reality}, journal = {J Am Soc Echocardiogr}, volume = {31}, number = {10}, year = {2018}, month = {10}, pages = {1158-60{\textendash}}, abstract = {

Three-dimensional (3D) imaging is increasingly important in echocardiography. However, viewing of 3D images on a flat, two-dimensional screen is a barrier to comprehension of latent information. There have been previous attempts to visualize the full 3D nature of the data, but they have not been widely adopted. For example, 3D printing offers realistic interaction but is time consuming, has limited means for the observer to move into or through the model, and is not yet practical for routine clinical use. Furthermore, the heart beats, and 3D printed models are static. Stereoscopic viewing on 2D screens (as at a movie theater) is possible but is expensive, may not provide an immersive experience, and does not have integrated 3D input devices (controllers).

}, author = {Andras Lasso and HannahH Nam and PatrickV Dinh and Csaba Pinter and Jean-ChristopheC. Fillion-Robin and Steve Pieper and Sankhesh Jhaveri and Jean-Baptiste Vimort and Ken Martin and Mark Asselin and FrancisX McGowan and Ron Kikinis and Gabor Fichtinger and MatthewA Jolley} } @article {Herz2017, title = {dcmqi: An Open Source Library for Standardized Communication of Quantitative Image Analysis Results Using DICOM}, journal = {Cancer Research}, volume = {77}, number = {21}, year = {2017}, month = {11/2017}, pages = {e87{\textendash}e90}, abstract = {

Quantitative analysis of clinical image data is an active area of research that holds promise for precision medicine, early assessment of treatment response, and objective characterization of the disease. Interoperability, data sharing, and the ability to mine the resulting data are of increasing importance, given the explosive growth in the number of quantitative analysis methods being proposed. The Digital Imaging and Communications in Medicine (DICOM) standard is widely adopted for image and metadata in radiology. dcmqi (DICOM for Quantitative Imaging) is a free, open source library that implements conversion of the data stored in commonly used research formats into the standard DICOM representation. dcmqi source code is distributed under BSD-style license. It is freely available as a precompiled binary package for every major operating system, as a Docker image, and as an extension to 3D Slicer. Installation and usage instructions are provided in the GitHub repository at https://github.com/qiicr/dcmqi. Cancer Res; 77(21); e87{\textendash}90. {\textcopyright}2017 AACR.

}, issn = {0008-5472}, doi = {10.1158/0008-5472.CAN-17-0336}, url = {http://cancerres.aacrjournals.org/content/77/21/e87}, author = {Christian Herz and Jean-ChristopheC. Fillion-Robin and Michael Onken and J{\"o}rg Riesmeier and Andras Lasso and Csaba Pinter and Gabor Fichtinger and Steve Pieper and Clunie, David and Ron Kikinis and Andriy Fedorov} } @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} } @conference {King2015, title = {An Immersive Virtual Reality Environment for Diagnostic Imaging}, booktitle = {Int Conf Med Image Comput Comput Assist Interv. MICCAI 2015}, year = {2015}, month = {10/2015}, abstract = {

Advancements in and adoption of consumer virtual reality are currently being propelled by numerous upcoming devices such as the Oculus Rift. Although applications are currently growing around the entertainment field, widespread adoption of virtual reality devices opens up the potential for other applications that may have been unfeasible with past implementations of virtual reality. A virtual reality environment may provide an equal or larger viewing volume than what is provided with the use of multiple conventional displays while remaining comparatively cheaper and more portable. A virtual reality application for the viewing of multiple image slices was designed using: the Oculus Rift head-mounted display, Unity, 3D Slicer, and a gamepad controller. A web server acquires data from volumes loaded within 3D Slicer and forwards it to a Unity application that proceeds to render a scene for the Oculus Rift head- mounted display. Users may interact with the images adjusting windowing and leveling using the handheld gamepad controller. Multiple images could also be brought closer to the user for detailed inspection. Application usage was demonstrated with the simultaneous visualization of concurrent slices of a serial CT scan of a patient with a lung nodule. The tasks of identifying the lesion and determining the malignancy status by monitoring the growth of the lesion over time were performed successfully. Also demonstrated was the studying of multiple- sclerosis lesion evolution by visualization of a large time-series MRI dataset. A virtual reality environment for the purpose of aiding diagnostic radiology has been created and demonstrated with potential use cases.

}, author = {Franklin King and Jagadeesan Jayender and Steve Pieper and Tina Kapur and Andras Lasso and Gabor Fichtinger} } @conference {Tokuda2009, title = {OpenIGTLink: An Open Network Protocol for Image Guided Therapy Environment}, booktitle = {International Journal of Medical Robotics and Computer Assisted Surgery}, volume = {na}, year = {2009}, pages = {na}, author = {Junichi Tokuda and Luis Ibanez and Gregory Fischer and Patrick Cheng and Haiying Liu and Jack Blevins and Jumpei Arata and Xenophon Papademetris and Ziv Yaniv and Tina Kapur and Steve Pieper and E. Clif Burdette and Gabor Fichtinger and Clare M Tempany and Nobuhiko Hata} } @article {DiMaio2007a, title = {Robot-assisted needle placement in open MRI: system architecture, integration and validation}, journal = {Journal of Computer Aided Surgery}, volume = {12}, number = {1}, year = {2007}, month = {Jan}, pages = {15{\textendash}24}, publisher = {Brigham, Women{\textquoteright}s Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA simond@bwh harvard edu}, abstract = {

In prostate cancer treatment, there is a move toward targeted interventions for biopsy, therapy, which has precipitated the need for precise image-guided methods for needle placement This paper describes an integrated system for planning, performing percutaneous procedures with robotic assistance under MRI guidance A graphical planning interface allows the physician to specify the set of desired needle trajectories, based on anatomical structures, lesions observed in the patient{\textquoteright}s registered pre-operative, pre-procedural MR images, immediately prior to the intervention in an open-bore MRI scanner All image-space coordinates are automatically computed, and are used to position a needle guide by means of an MRI-compatible robotic manipulator, thus avoiding the limitations of the traditional fixed needle template Automatic alignment of real-time intra-operative images aids visualization of the needle as it is manually inserted through the guide Results from in-scanner phantom experiments are provided

}, keywords = {Biopsy, diagnosis, Humans, instrumentation/methods, Magnetic Resonance Imaging, Male, Needle, Neuronavigation, Prostatic Neoplasms, Robotics}, doi = {10 1080/10929080601168254}, url = {http://dx doi org/10 1080/10929080601168254}, author = {Simon DiMaio and Steve Pieper and Kiyoyuki Chinzei and Nobuhiko Hata and Steven Haker and Daniel Kacher and Gabor Fichtinger and Clare M Tempany and Ron Kikinis} } @article {DiMaio2006b, title = {Robot-assisted needle placement in open-MRI: system architecture, integration and validation}, journal = {Studies in health technology and informatics}, volume = {119}, year = {2006}, pages = {126{\textendash}131}, publisher = {Brigham, Women{\textquoteright}s Hospital, Harvard Medical School, USA simond@bwh harvard edu}, abstract = {

This work describes an integrated system for planning, performing percutaneous procedures-such as prostate biopsy-with robotic assistance under MRI-guidance The physician interacts with a planning interface in order to specify the set of desired needle trajectories, based on anatomical structures, lesions observed in the patient{\textquoteright}s MR images All image-space coordinates are automatically computed, and used to position a needle guide by means of an MRI-compatible robotic manipulator, thus avoiding the limitations of the traditional fixed needle template Direct control of real-time imaging aids visualization of the needle as it is manually inserted through the guide Results from in-scanner phantom experiments are provided

}, keywords = {Biopsy, Computer Systems, diagnosis, Humans, instrumentation, Magnetic Resonance Imaging, Male, Needle, Prostatic Neoplasms, Robotics}, author = {Simon DiMaio and Steve Pieper and Kiyoyuki Chinzei and Nobuhiko Hata and Emese Balogh and Gabor Fichtinger and Clare M Tempany and Ron Kikinis} } @conference {Boctor2004a, title = {CISUS: an integrated 3D ultrasound system for IGT using a modular tracking API}, booktitle = {Medical Imaging 2004: Visualization, Image-Guided Procedures, and Display}, volume = {5367}, number = {1}, year = {2004}, pages = {247-256}, publisher = {SPIE}, organization = {SPIE}, doi = {10 1117/12 537776}, url = {http://link aip org/link/?PSI/5367/247/1}, author = {Emad Boctor and Anand Viswanathan and Steve Pieper and Michael Choti and Russell H. Taylor and Ron Kikinis and Gabor Fichtinger}, editor = {Robert L. Galloway, Jr.} } @conference {DiMaio2004, title = {Robot assisted percutaneous intervention in open-MRI}, booktitle = {5th Interventional MRI Symposium}, year = {2004}, author = {Simon DiMaio and Steve Pieper and Kiyoyuki Chinzei and Gabor Fichtinger and Ron Kikinis} }