Literature DB >> 23860771

TRUS-MRI image registration: a paradigm shift in the diagnosis of significant prostate cancer.

F Cornud1, L Brolis, N Barry Delongchamps, D Portalez, B Malavaud, R Renard-Penna, P Mozer.   

Abstract

Accuracy of multiparametric MRI has greatly improved the ability of localizing tumor foci of prostate cancer. This property can be used to perform a TRUS-MR image registration, new technological advance, which allows for an overlay of an MRI onto a TRUS image to target a prostate biopsy toward a suspicious area Three types of registration have been developed: cognitive-based, sensor-based, and organ-based registration. Cognitive registration consists of aiming a suspicious area during biopsy with the knowledge of the lesion location identified on multiparametric MRI. Sensor-based registration consists of tracking in real time the TRUS probe with a magnetic device, achieving a global positioning system which overlays in real-time prostate image on both modalities. Its main limitation is that it does not take into account prostate and patient motion during biopsy. Two systems (Artemis and Uronav) have been developed to partially circumvent this drawback. Organ-based registration (Koelis) does not aim to track the TRUS probe, but the prostate itself to compute in a 3D acquisition the TRUS prostate shape, allowing for a registration with the corresponding 3D MRI shape. This system is not limited by prostate/patient motion and allows for a deformation of the organ during registration. Pros and cons of each technique and the rationale for a targeted biopsy only policy are discussed.

Entities:  

Mesh:

Year:  2013        PMID: 23860771     DOI: 10.1007/s00261-013-0018-4

Source DB:  PubMed          Journal:  Abdom Imaging        ISSN: 0942-8925


  11 in total

1.  Prostate cancer: Can image-guided biopsy findings evaluate risk of ECE?

Authors:  Daniel Portalez; Bernard Malavaud
Journal:  Nat Rev Urol       Date:  2015-05-05       Impact factor: 14.432

2.  Precision of MRI/ultrasound-fusion biopsy in prostate cancer diagnosis: an ex vivo comparison of alternative biopsy techniques on prostate phantoms.

Authors:  N Westhoff; F P Siegel; D Hausmann; M Polednik; J von Hardenberg; M S Michel; M Ritter
Journal:  World J Urol       Date:  2016-11-09       Impact factor: 4.226

3.  Free-hand transperineal targeted prostate biopsy with real-time fusion imaging of multiparametric magnetic resonance imaging and transrectal ultrasound: single-center experience in China.

Authors:  Qing Zhang; Wei Wang; Rong Yang; Gutian Zhang; Bing Zhang; Weiping Li; Haifeng Huang; Hongqian Guo
Journal:  Int Urol Nephrol       Date:  2015-03-29       Impact factor: 2.370

Review 4.  Prognostic Utility of PET in Prostate Cancer.

Authors:  Hossein Jadvar
Journal:  PET Clin       Date:  2015-01-22

5.  Hybrid 2D-3D ultrasound registration for navigated prostate biopsy.

Authors:  Sonia-Yuki Selmi; Emmanuel Promayon; Jocelyne Troccaz
Journal:  Int J Comput Assist Radiol Surg       Date:  2018-03-20       Impact factor: 2.924

6.  Real-time cancer diagnosis during prostate biopsy: ex vivo evaluation of full-field optical coherence tomography (FFOCT) imaging on biopsy cores.

Authors:  Jonathan Lopater; Pierre Colin; Frédéric Beuvon; Mathilde Sibony; Eugénie Dalimier; François Cornud; Nicolas Barry Delongchamps
Journal:  World J Urol       Date:  2015-06-23       Impact factor: 4.226

7.  Evaluation of the 'Prostate Interdisciplinary Communication and Mapping Algorithm for Biopsy and Pathology' (PIC-MABP).

Authors:  Daniel Junker; Thomas R W Herrmann; Markus Bader; Jasmin Bektic; Gregor Henkel; Stephan Kruck; Markus Sandbichler; David Schilling; Georg Schäfer; Udo Nagele
Journal:  World J Urol       Date:  2015-07-01       Impact factor: 4.226

Review 8.  Accurate validation of ultrasound imaging of prostate cancer: a review of challenges in registration of imaging and histopathology.

Authors:  Rogier R Wildeboer; Ruud J G van Sloun; Arnoud W Postema; Christophe K Mannaerts; Maudy Gayet; Harrie P Beerlage; Hessel Wijkstra; Massimo Mischi
Journal:  J Ultrasound       Date:  2018-07-30

Review 9.  Quick guide on radiology image pre-processing for deep learning applications in prostate cancer research.

Authors:  Samira Masoudi; Stephanie A Harmon; Sherif Mehralivand; Stephanie M Walker; Harish Raviprakash; Ulas Bagci; Peter L Choyke; Baris Turkbey
Journal:  J Med Imaging (Bellingham)       Date:  2021-01-06

Review 10.  The current and future role of magnetic resonance imaging in prostate cancer detection and management.

Authors:  Jan Philipp Radtke; Dogu Teber; Markus Hohenfellner; Boris A Hadaschik
Journal:  Transl Androl Urol       Date:  2015-06
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