Impact of bias field correction on 0.35 T pelvic MR images: evaluation on generative adversarial network-based OARs’ auto-segmentation and visual grading assessment

Impact of bias field correction on 0.35 T pelvic MR images: evaluation on generative adversarial network-based OARs’ auto-segmentation and visual grading assessment

PurposeMagnetic resonance imaging (MRI)-guided radiotherapy enables adaptive treatment plans based on daily anatomical changes and accurate organ visualization. However, the bias field artifact can compromise image quality, affecting diagnostic accuracy and quantitative analyses. This study aims to...

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Bibliographic Details
Main Authors: Marica Vagni, Huong Elena Tran, Francesco Catucci, Giuditta Chiloiro, Andrea D’Aviero, Alessia Re, Angela Romano, Luca Boldrini, Maria Kawula, Elia Lombardo, Christopher Kurz, Guillaume Landry, Claus Belka, Luca Indovina, Maria Antonietta Gambacorta, Davide Cusumano, Lorenzo Placidi
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-03-01
Series:Frontiers in Oncology
Subjects:
N4ITK algorithm
bias field artifact
visual grading assessment
generative adversarial networks
0.35 T MRIgRT
prostate cancer
Online Access:https://www.frontiersin.org/articles/10.3389/fonc.2024.1294252/full

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https://www.frontiersin.org/articles/10.3389/fonc.2024.1294252/full

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