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

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

Research output: Contribution to journalArticlepeer-review

62 Scopus citations

Abstract

Purpose: Transrectal ultrasound (TRUS)-guided random prostate biopsy is, in spite of its low sensitivity, the gold standard for the diagnosis of prostate cancer. The recent advent of PET imaging using a novel dedicated radiotracer, Ga-labeled prostate-specific membrane antigen (PSMA), combined with MRI provides improved pre-interventional identification of suspicious areas. This work proposes a multimodal fusion image-guided biopsy framework that combines PET-MRI images with TRUS, using automatic segmentation and registration, and offering real-time guidance. Methods: The prostate TRUS images are automatically segmented with a Hough transform-based random forest approach. The registration is based on the Coherent Point Drift algorithm to align surfaces elastically and to propagate the deformation field calculated from thin-plate splines to the whole gland. Results: The method, which has minimal requirements and temporal overhead in the existing clinical workflow, is evaluated in terms of surface distance and landmark registration error with respect to the clinical ground truth. Evaluations on agar–gelatin phantoms and clinical data of 13 patients confirm the validity of this approach. Conclusion: The system is able to successfully map suspicious regions from PET/MRI to the interventional TRUS image.

Original languageEnglish
Pages (from-to)1997-2007
Number of pages11
JournalInternational Journal of Computer Assisted Radiology and Surgery
Volume10
Issue number12
DOIs
StatePublished - 1 Dec 2015

Keywords

  • Coherent point drift
  • Hough forest segmentation
  • Multimodal deformable registration
  • Prostate biopsy

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