Postimplant Dosimetry of Permanent Prostate Brachytherapy: Comparison of MRI-Only and CT-MRI Fusion-Based Workflows.


Journal

International journal of radiation oncology, biology, physics
ISSN: 1879-355X
Titre abrégé: Int J Radiat Oncol Biol Phys
Pays: United States
ID NLM: 7603616

Informations de publication

Date de publication:
01 01 2020
Historique:
received: 24 07 2019
revised: 11 09 2019
accepted: 07 10 2019
pubmed: 19 10 2019
medline: 14 2 2020
entrez: 19 10 2019
Statut: ppublish

Résumé

The current magnetic resonance imaging-computed tomography (MRI-CT) fusion-based workflow for postimplant dosimetry of low-dose-rate (LDR) prostate brachytherapy takes advantage of the superior soft tissue contrast of MRI, but still relies on CT for seed visualization and detection. Recently an MR-only workflow has been proposed that employs standard MR sequences and visualizes conventional implanted seed with positive contrast solely through MR postprocessing. In this work, the novel MR-only based workflow is compared with the clinical CT-MRI fusion approach. Twenty-four prostate patients with a total of 1775 implanted LDR seeds were scanned using a 3-dimensional multiecho gradient echo sequence on a 3 Tesla MR scanner within 30 days after implantation. Quantitative susceptibility mapping was used for seed visualization. Seeds were automatically segmented and localized on the quantitative susceptibility mapping using convolutional neural network and k-means clustering, respectively. To assess the MR-only seed localization error, CT and MR-derived seed positions were coregistered, and ultimately, the resulting dose-volume histograms were compared. The MR-based seed visualization, segmentation, and localization generated comparable results to the CT-MR registration approach. The accuracy of the MRI-only based seed identification was 99.1%. After a rigid registration between the MR and CT-derived seed centroids, the average localization error was 0.8 ± 0.8 mm. The average prostate D The MRI-only LDR postimplant dosimetry is feasible and has very good potential to eliminate the need for CT-based seed identification.

Identifiants

pubmed: 31627178
pii: S0360-3016(19)33882-9
doi: 10.1016/j.ijrobp.2019.10.009
pii:
doi:

Substances chimiques

Gases 0
Iodine Radioisotopes 0

Types de publication

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

Langues

eng

Sous-ensembles de citation

IM

Pagination

206-215

Informations de copyright

Copyright © 2019 Elsevier Inc. All rights reserved.

Auteurs

Reyhaneh Nosrati (R)

Department of Physics, Ryerson University, Toronto, Canada; Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Canada. Electronic address: reyhaneh.nosrati@childrens.harvard.edu.

Matthew Wronski (M)

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

Chia-Lin Tseng (CL)

Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, Canada.

Hans Chung (H)

Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, Canada.

Ana Pejović-Milić (A)

Department of Physics, Ryerson University, Toronto, Canada.

Gerard Morton (G)

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

Greg J Stanisz (GJ)

Department of Physics, Ryerson University, Toronto, Canada; Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Canada.

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Classifications MeSH