@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} } @article {Pinter2020, title = {SlicerVR for Medical Intervention Training and Planning in Immersive Virtual Reality}, journal = {IEEE Transactions on Medical Robotics and Bionics}, volume = {2}, number = {2}, year = {2020}, month = {03/2020}, pages = {108-117}, abstract = {

Virtual reality (VR) provides immersive visualization that has proved to be useful in a variety of medical applications. Currently, however, no free open-source software platform exists that would provide comprehensive support for translational clinical researchers in prototyping experimental VR scenarios in training, planning or guiding medical interventions. By integrating VR functions in 3D Slicer, an established medical image analysis and visualization platform, SlicerVR enables virtual reality experience by a single click. It provides functions to navigate and manipulate the virtual scene, as well as various settings to abate the feeling of motion sickness. SlicerVR allows for shared collaborative VR experience both locally and remotely. We present illustrative scenarios created with SlicerVR in a wide spectrum of applications, including echocardiography, neurosurgery, spine surgery, brachytherapy, intervention training and personalized patient education. SlicerVR is freely available under BSD type license as an extension to 3D Slicer and it has been downloaded over 7,800 times at the time of writing this article.

}, doi = {10.1109/TMRB.2020.2983199}, author = {Csaba Pinter and Andras Lasso and Saleh Choueib and Mark Asselin and Jean-ChristopheC. Fillion-Robin and Jean-Baptiste Vimort and Ken Martin and MatthewA Jolley and Gabor Fichtinger} } @conference {Choueib2019b, title = {Assessment of immersive medical virtual reality visualization using 3D Slicer}, booktitle = {17th Annual Imaging Network Ontario Symposium (ImNO)}, year = {2019}, month = {03/2019}, address = {London, Ontario}, keywords = {3D Slicer, Planning, SlicerVR, training, Virtual Reality}, author = {Saleh Choueib and Csaba Pinter and Andras Lasso and Jean-ChristopheC. Fillion-Robin and Jean-Baptiste Vimort and Ken Martin and Gabor Fichtinger} } @conference {Choueib2019a, title = {Evaluation of 3D Slicer as a medical virtual reality visualization platform}, booktitle = {SPIE Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling}, volume = {10951}, number = {38}, year = {2019}, month = {03/2019}, publisher = {SPIE Medical Imaging}, organization = {SPIE Medical Imaging}, address = {San Diego, California }, keywords = {3D Slicer, Planning, SlicerVR, training, Virtual Reality}, author = {Saleh Choueib and Csaba Pinter and Andras Lasso and Jean-ChristopheC. Fillion-Robin and Jean-Baptiste Vimort and Ken Martin and Gabor Fichtinger} } @conference {Pinter2019a, title = {SlicerVR for image-guided therapy planning in immersive virtual reality}, booktitle = {The 12th Hamlyn Symposium on Medical Robotics, 23-26 June 2019, Imperial College, London, UK}, year = {2019}, month = {06/2019}, pages = {91-92}, address = {London, UK}, author = {Csaba Pinter and Andras Lasso and Mark Asselin and Jean-ChristopheC. Fillion-Robin and Jean-Baptiste Vimort and Ken Martin 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} }