Prostate Fusion Biopsy

Transrectal ultrasound (TRUS) is regularly used to guide prostate biopsies, which constitute the current gold standard for cancer diagnosis. Unfortunately, TRUS suffers from low sensitivity, leading to a high rate of false negative results. Therefore, MR images – and more recently also Ga-labelled PSMA (Prostate Specific Membrane Antigen) PET – are used to identify suspicious areas in the prostate. The challenge is now to fuse pre-interventional PET/MRI with interventional TRUS both accurately enough to allow targeting with the biopsy needle and fast enough not to impede clinical routine with too much (time-consuming) user interaction.

In this project, we proposed a novel method to first automatically segment the prostate in TRUS using a Hough transform based random forest approach. Then, a elastic surface registration is performed to fuse (PET/)MRI with TRUS, relying on the Coherent Point Drift algorithm. The minimal computation time for the entire pipeline (less than five minutes) allows implementation in the clinical routine.

Prostate Fusion Biopsy Workflow

For biopsy guidance, the prostate is segmented in both TRUS and MRI images, and their surfaces are registered for a fusion visualization.

Prostate image fusion, including registration and often segmentation, remains to be a very active topic in the research community. For details on our method as well as the impressive work of other groups around the world, please refer to our publications and the references therein:

Preconditioned Intensity-Based Prostate Registration using Statistical Deformation Models

Zettinig, Oliver; Rackerseder, Julia; Lentes, Beatrice; Maurer, Tobias; Westenfelder, Kay; Eiber, Matthias; Frisch, Benjamin; Navab, Nassir

Preconditioned Intensity-Based Prostate Registration using Statistical Deformation Models Inproceedings

In: 2017 IEEE International Symposium on Biomedical Imaging (ISBI), pp. 853–857, IEEE 2017.

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Challenges in Multimodal Image-guided Targeted Prostate Biopsy

Shah, Amit; Zettinig, Oliver; Storz, Enno; Maurer, Tobias; Eiber, Matthias; Navab, Nassir; Frisch, Benjamin

Challenges in Multimodal Image-guided Targeted Prostate Biopsy Inproceedings

In: Hamlyn Symposium on Medical Robotics, London, UK, pp. 21–22, 2015.

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Multimodal image-guided prostate fusion biopsy based on automatic deformable registration

Zettinig, Oliver; Shah, Amit; Hennersperger, Christoph; Eiber, Matthias; Kroll, Christine; Kübler, Hubert; Maurer, Tobias; Milletari, Fausto; Rackerseder, Julia; zu Berge, Christian Schulte; Storz, Enno; Frisch, Benjamin; Navab, Nassir

Multimodal image-guided prostate fusion biopsy based on automatic deformable registration Journal Article

In: International Journal of Computer Assisted Radiology and Surgery, 10 (12), pp. 1997–2007, 2015.

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PET/MRI/TRUS image fusion guided prostate biopsy: development of a research platform and initial clinical results

Frisch, Benjamin; Storz, Enno; Zettinig, Oliver; Shah, Amit; Kübler, Hubert; Navab, Nassir; Wester, Hans-Jürgen; Schwaiger, Markus; Eiber, Matthias; Maurer, Tobias

PET/MRI/TRUS image fusion guided prostate biopsy: development of a research platform and initial clinical results Journal Article

In: Journal of Nuclear Medicine, 56 (supplement 3), pp. 510, 2015.

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PSMA-PET/MRI-guided transrectal fusion biopsy for the detection of prostate cancer

Storz, Enno; Shah, Amit; Zettinig, Oliver; Eiber, Matthias; Wester, Hans-Jürgen; Kübler, Hubert; Gschwend, Jürgen E.; Schwaiger, Markus; Frisch, Benjamin; Maurer, Tobias

PSMA-PET/MRI-guided transrectal fusion biopsy for the detection of prostate cancer Journal Article

In: European Urology Supplements, 2 (14), pp. e217, 2015.

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An open source multimodal image-guided prostate biopsy framework

Shah, Amit; Zettinig, Oliver; Maurer, Tobias; Precup, Cristina; zu Berge, Christian Schulte; Weiss, Jakob; Frisch, Benjamin; Navab, Nassir

An open source multimodal image-guided prostate biopsy framework Inproceedings

In: Clinical Image-Based Procedures. Translational Research in Medical Imaging, pp. 1–8, Springer LNCS, 2014.

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