Documentation/Nightly/Extensions/SlicerProstate

For the latest Slicer documentation, visit the read-the-docs.

Introduction and Acknowledgements

Extension: SlicerProstate Acknowledgments: This work is supported in part by the National Cancer Institute and the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health through the following grants: Quantitative MRI of prostate cancer as a biomarker and guide for treatment, Quantitative Imaging Network (U01 CA151261, PI Fennessy) Enabling technologies for MRI-guided prostate interventions (R01 CA111288, PI Tempany) The National Center for Image-Guided Therapy (P41 EB015898, PI Tempany) Advancement and Validation of Prostate Diffusion and Spectroscopic MRI (R01 CA160902, PI Maier) Quantitative Image Informatics for Cancer Research (QIICR) (U24 CA180918, PIs Kikinis and Fedorov). Contributors: Andrey Fedorov (SPL), Andras Lasso (Queen's University), Alireza Mehrtash (Brigham and Women's Hospital) Contact: Andrey Fedorov, <email>fedorov@bwh.harvard.edu</email> License: Slicer License Quantitative Image Informatics for Cancer Research   Surgical Planning Laboratory (SPL)   National Center for Image Guided Therapy (NCIGT)

Extension Description

SlicerProstate extension hosts various modules to facilitate

• processing and management of prostate image data
• utilizing prostate images in image-guided interventions
• development of the imaging biomarkers of the prostate cancer

While the main motivation for developing the functionality contained in this extension was prostate cancer imaging applications, they can also be applied in different contexts.

Modules

• Distance Map Based Registration: module that can be used for deformable registration between prostate gland segmentations in MR and TRUS
• Segmentation Smoothing: module to smooth the staircase aliasing effect commonly found in prostate segmentations done in typical MRI data
• QuadEdgeSurfaceMesher: module to reduce the number of triangles and smooth the surface recovered with the Marching Cubes algorithm
• DWModeling: module to fit commonly used models to Diffusion Weighted MRI of the prostate

References

[1] Fedorov A, Khallaghi S, Antonio Sánchez C, Lasso A, Fels S, Tuncali K, Neubauer Sugar E, Kapur T, Zhang C, Wells W, Nguyen PL, Abolmaesumi P, Tempany C. (2015) Open-source image registration for MRI–TRUS fusion-guided prostate interventions. Int J CARS: 1–10. Available: http://link.springer.com/article/10.1007/s11548-015-1180-7.

[2] Fedorov A, Nguyen PL, Tuncali K, Tempany C. (2015). Annotated MRI and ultrasound volume images of the prostate. Zenodo. http://doi.org/10.5281/zenodo.16396

[3] Kobus T., Fedorov A., Tempany C.M., Mulkern R.V., Dunne R., Maier S.E. Bi-exponential Diffusion Analysis in Normal Prostate and Prostate Cancer: Transition Zone and Peripheral Zone Considerations. Proc. of ISMRM 2015. http://www.spl.harvard.edu/abstracts/item/view/168

Information for Developers

• SlicerProstate organization page on github: https://github.com/SlicerProstate