Surgical Planning Laboratory - Brigham & Women's Hospital - Boston, Massachusetts USA - a teaching affiliate of Harvard Medical School

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Open Source Platform for Transperineal In-bore MRI-guided Targeted Prostate Biopsy

Institution:
Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Publisher:
IEEE Engineering in Medicine and Biology Society
Publication Date:
May-2019
Journal:
IEEE Trans Biomed Eng
Citation:
IEEE Trans Biomed Eng. 2019 May 23.
PubMed ID:
31135342
Appears in Collections:
NCIGT, SLICER, SPL
Sponsors:
P41 EB015898/EB/NIBIB NIH HHS/United States
U24 CA180918/CA/NCI NIH HHS/United States
Generated Citation:
Herz C., MacNeil K., Behringer P.A., Tokuda J., Mehrtash A., Mousavi P., Kikinis R., Fennessy F.M., Tempany C.M., Tuncali K., Fedorov A. Open Source Platform for Transperineal In-bore MRI-guided Targeted Prostate Biopsy. IEEE Trans Biomed Eng. 2019 May 23. PMID: 31135342.
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OBJECTIVE: Accurate biopsy sampling of the suspected lesions is critical for the diagnosis and clinical management of prostate cancer (PCa). Transperineal in-bore MRI-guided prostate biopsy (tpMRgBx) is a targeted biopsy technique that was shown to be safe, efficient and accurate. Our goal was to develop an open source software platform to support evaluation, refinement and translation of this biopsy approach. METHODS: We developed SliceTracker, a 3D Slicer extension to support tpMRgBx. We followed modular design of the implementation to enable customization of the interface, and interchange of image segmentation and registration components to assess their effect on the processing time, precision and accuracy of the biopsy needle placement. The platform and supporting documentation were developed to enable the use of software by an operator with minimal technical training to facilitate translation. Retrospective evaluation studied registration accuracy, effect of the prostate segmentation approach, and re-identification time of biopsy targets. Prospective evaluation focused on the total procedure time and biopsy targeting error (BTE). RESULTS: Evaluation utilized data from 73 retrospective and 10 prospective tpMRgBx cases. Mean Landmark Registration Error (LRE) for retrospective evaluation was 1.88 ±2.63 mm and was not sensitive to the approach used for prostate gland segmentation. Prospectively, we observed target re-identification time of 4.60 ±2.40 min, and BTE of 2.40 ±0.98 mm. CONCLUSION: SliceTracker is modular and extensible open source platform for supporting image processing aspects of the tpMRgBx procedure. It has been successfully utilized to support clinical research procedures at our site.