Literature DB >> 25979040

Biomechanical modeling constrained surface-based image registration for prostate MR guided TRUS biopsy.

Wendy J M van de Ven1, Yipeng Hu2, Jelle O Barentsz1, Nico Karssemeijer1, Dean Barratt2, Henkjan J Huisman1.   

Abstract

PURPOSE: Adding magnetic resonance (MR)-derived information to standard transrectal ultrasound (TRUS) images for guiding prostate biopsy is of substantial clinical interest. A tumor visible on MR images can be projected on ultrasound (US) by using MR-US registration. A common approach is to use surface-based registration. The authors hypothesize that biomechanical modeling will better control deformation inside the prostate than a regular nonrigid surface-based registration method. The authors developed a novel method by extending a nonrigid surface-based registration algorithm with biomechanical finite element (FE) modeling to better predict internal deformations of the prostate.
METHODS: Data were collected from ten patients and the MR and TRUS images were rigidly registered to anatomically align prostate orientations. The prostate was manually segmented in both images and corresponding surface meshes were generated. Next, a tetrahedral volume mesh was generated from the MR image. Prostate deformations due to the TRUS probe were simulated using the surface displacements as the boundary condition. A three-dimensional thin-plate spline deformation field was calculated by registering the mesh vertices. The target registration errors (TREs) of 35 reference landmarks determined by surface and volume mesh registrations were compared.
RESULTS: The median TRE of a surface-based registration with biomechanical regularization was 2.76 (0.81-7.96) mm. This was significantly different than the median TRE of 3.47 (1.05-7.80) mm for regular surface-based registration without biomechanical regularization.
CONCLUSIONS: Biomechanical FE modeling has the potential to improve the accuracy of multimodal prostate registration when comparing it to a regular nonrigid surface-based registration algorithm and can help to improve the effectiveness of MR guided TRUS biopsy procedures.

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Year:  2015        PMID: 25979040     DOI: 10.1118/1.4917481

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  6 in total

1.  Deformable MR-CBCT prostate registration using biomechanically constrained deep learning networks.

Authors:  Yabo Fu; Tonghe Wang; Yang Lei; Pretesh Patel; Ashesh B Jani; Walter J Curran; Tian Liu; Xiaofeng Yang
Journal:  Med Phys       Date:  2020-11-27       Impact factor: 4.071

2.  Learning Non-rigid Deformations for Robust, Constrained Point-based Registration in Image-Guided MR-TRUS Prostate Intervention.

Authors:  John A Onofrey; Lawrence H Staib; Saradwata Sarkar; Rajesh Venkataraman; Cayce B Nawaf; Preston C Sprenkle; Xenophon Papademetris
Journal:  Med Image Anal       Date:  2017-04-12       Impact factor: 8.545

Review 3.  Focal therapy for prostate cancer: the technical challenges.

Authors:  Annette Haworth; Scott Williams
Journal:  J Contemp Brachytherapy       Date:  2017-08-30

4.  Personalized heterogeneous deformable model for fast volumetric registration.

Authors:  Weixin Si; Xiangyun Liao; Qiong Wang; Pheng Ann Heng
Journal:  Biomed Eng Online       Date:  2017-02-20       Impact factor: 2.819

5.  Retrospective comparison of direct in-bore magnetic resonance imaging (MRI)-guided biopsy and fusion-guided biopsy in patients with MRI lesions which are likely or highly likely to be clinically significant prostate cancer.

Authors:  Wulphert Venderink; Marloes van der Leest; Annemarijke van Luijtelaar; Wendy J M van de Ven; Jurgen J Fütterer; J P Michiel Sedelaar; Henkjan J Huisman
Journal:  World J Urol       Date:  2017-09-04       Impact factor: 4.226

6.  Population-based prediction of subject-specific prostate deformation for MR-to-ultrasound image registration.

Authors:  Yipeng Hu; Eli Gibson; Hashim Uddin Ahmed; Caroline M Moore; Mark Emberton; Dean C Barratt
Journal:  Med Image Anal       Date:  2015-10-31       Impact factor: 8.545

  6 in total

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