Clinical evaluation of an MRI-to-ultrasound deformable image registration algorithm for prostate brachytherapy.


Journal

Brachytherapy
ISSN: 1873-1449
Titre abrégé: Brachytherapy
Pays: United States
ID NLM: 101137600

Informations de publication

Date de publication:
Historique:
received: 16 05 2018
revised: 16 07 2018
accepted: 08 08 2018
pubmed: 6 10 2018
medline: 26 4 2019
entrez: 6 10 2018
Statut: ppublish

Résumé

Identifying dominant intraprostatic lesions (DILs) on transrectal ultrasound (TRUS) images during prostate high-dose-rate brachytherapy (HDR-BT) treatment planning is challenging. Multiparametric MRI (mpMRI) is the tool of choice for DIL identification; however, the geometry of the prostate on mpMRI and on the TRUS may differ significantly, requiring image registration. This study evaluates the efficacy of an in-house software for MRI-to-TRUS DIL registration (MR2US) and compares its results to rigid and B-Spline deformable registration. Ten patients with intermediate-risk prostate cancer, each with mpMRI and TRUS data sets, were included in this study. Five radiation oncologists (ROs) with expertise in TRUS-based HDR-BT were asked to cognitively contour the DIL onto the TRUS image using mpMRI as reference. The contours were analyzed for concordance using simultaneous truth and performance level estimation algorithm. Similarity indices, DIL volumes, and distance between centroid positions were measured to compare the consensus contours against the contours from ROs and the automated algorithms; registration time between all contouring methods was recorded. MR2US registration had the highest dice coefficients among all patients with a mean of 0.80 ± 0.13 in comparison to rigid (0.65 ± 0.20) and B-Spline (0.51 ± 0.30). The distance between centroid positions between simultaneous truth and performance level estimation contour and MR2US, rigid, and B-Spline contours were 5 ± 2, 7 ± 5, and 18 ± 11 mm, respectively. The average registration time was significantly shorter for MR2US (11 ± 2 s) and rigid algorithm (7 ± 1 s) compared to ROs (227 ± 27 s) and B-Spline (199 ± 38 s). The efficacy of integrating an MRI-delineated DIL into a TRUS-based BT workflow has been validated in this study. The MR2US software is fast and accurate enough to be used for DIL identification in prostate HDR-BT.

Identifiants

pubmed: 30287271
pii: S1538-4721(18)30434-3
doi: 10.1016/j.brachy.2018.08.006
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

95-102

Informations de copyright

Copyright © 2018 American Brachytherapy Society. Published by Elsevier Inc. All rights reserved.

Auteurs

Amani Shaaer (A)

Department of Physics, Ryerson University, Toronto, Ontario, Canada.

Melanie Davidson (M)

Department of Medical Physics, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada.

Mark Semple (M)

Department of Medical Physics, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.

Alexandru Nicolae (A)

Department of Medical Physics, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.

Lucas Castro Mendez (LC)

Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada; Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.

Hans Chung (H)

Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada; Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.

Andrew Loblaw (A)

Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada; Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.

Chia-Lin Tseng (CL)

Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada; Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.

Gerard Morton (G)

Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada; Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.

Ananth Ravi (A)

Department of Medical Physics, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada. Electronic address: ananth.ravi@sunnybrook.ca.

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