Open Source Platform for Transperineal In-Bore MRI-Guided Targeted Prostate Biopsy

• Published in: IEEE transactions on biomedical engineering 2020-02, Vol.67 (2), p.565-576
• Main Authors: Herz, Christian, MacNeil, Kyle, Behringer, Peter A., Tokuda, Junichi, Mehrtash, Alireza, Mousavi, Parvin, Kikinis, Ron, Fennessy, Fiona M., Tempany, Clare M., Tuncali, Kemal, Fedorov, Andriy
• Format: Article
• Language: English
• Subjects: Biopsy; Computer programs; Evaluation; Glands; Humans; Identification methods; Image Interpretation, Computer-Assisted; Image processing; image registration; Image segmentation; Image-Guided Biopsy - methods; Image-guided interventions; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Medical imaging; Modular design; Needles; Open source software; Perineum - diagnostic imaging; Perineum - surgery; Prostate - diagnostic imaging; Prostate cancer; Prostatic Neoplasms - diagnostic imaging; Prostatic Neoplasms - pathology; Registration; Software; software evaluation; Target recognition; targeted biopsy; Three-dimensional displays; Translation
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2019.2918731

Objective: Accurate biopsy sampling of the suspected lesions is critical for the diagnosis and clinical management of prostate cancer. 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 ten prospective tpMRgBx cases. Mean landmark registration error 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.