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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

dc.contributor.authorMarica Vagni
dc.contributor.authorHuong Elena Tran
dc.contributor.authorFrancesco Catucci
dc.contributor.authorGiuditta Chiloiro
dc.contributor.authorAndrea D’Aviero
dc.contributor.authorAlessia Re
dc.contributor.authorAngela Romano
dc.contributor.authorLuca Boldrini
dc.contributor.authorMaria Kawula
dc.contributor.authorElia Lombardo
dc.contributor.authorChristopher Kurz
dc.contributor.authorGuillaume Landry
dc.contributor.authorClaus Belka
dc.contributor.authorClaus Belka
dc.contributor.authorClaus Belka
dc.contributor.authorLuca Indovina
dc.contributor.authorMaria Antonietta Gambacorta
dc.contributor.authorDavide Cusumano
dc.contributor.authorDavide Cusumano
dc.contributor.authorLorenzo Placidi
dc.contributor.otherDipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
dc.contributor.otherDipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
dc.contributor.otherMater Olbia Hospital, Olbia, Italy
dc.contributor.otherDipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
dc.contributor.otherMater Olbia Hospital, Olbia, Italy
dc.contributor.otherMater Olbia Hospital, Olbia, Italy
dc.contributor.otherDipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
dc.contributor.otherDipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
dc.contributor.otherDepartment of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
dc.contributor.otherDepartment of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
dc.contributor.otherDepartment of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
dc.contributor.otherDepartment of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
dc.contributor.otherDepartment of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
dc.contributor.otherGerman Cancer Consortium (DKTK), Partner Site Munich, A Partnership Between DKFZ and LMU University Hospital Munich, Munich, Germany
dc.contributor.otherBavarian Cancer Research Center (BZKF), Munich, Germany
dc.contributor.otherDipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
dc.contributor.otherDipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
dc.contributor.otherDipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
dc.contributor.otherMater Olbia Hospital, Olbia, Italy
dc.contributor.otherDipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
dc.date.accessioned2025-10-09T05:31:30Z
dc.date.available2025-10-09T05:31:30Z
dc.date.issued01-03-2024
dc.identifier.urihttps://www.frontiersin.org/articles/10.3389/fonc.2024.1294252/full
dc.identifier.urihttp://digilib.fisipol.ugm.ac.id/repo/handle/15717717/41124
dc.description.abstractPurposeMagnetic 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 assess the impact of bias field correction on 0.35 T pelvis MRIs by evaluating clinical anatomy visualization and generative adversarial network (GAN) auto-segmentation performance.Materials and methods3D simulation MRIs from 60 prostate cancer patients treated on MR-Linac (0.35 T) were collected and preprocessed with the N4ITK algorithm for bias field correction. A 3D GAN architecture was trained, validated, and tested on 40, 10, and 10 patients, respectively, to auto-segment the organs at risk (OARs) rectum and bladder. The GAN was trained and evaluated either with the original or the bias-corrected MRIs. The Dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (HD95th) were computed for the segmented volumes of each patient. The Wilcoxon signed-rank test assessed the statistical difference of the metrics within OARs, both with and without bias field correction. Five radiation oncologists blindly scored 22 randomly chosen patients in terms of overall image quality and visibility of boundaries (prostate, rectum, bladder, seminal vesicles) of the original and bias-corrected MRIs. Bennett’s S score and Fleiss’ kappa were used to assess the pairwise interrater agreement and the interrater agreement among all the observers, respectively.ResultsIn the test set, the GAN trained and evaluated on original and bias-corrected MRIs showed DSC/HD95th of 0.92/5.63 mm and 0.92/5.91 mm for the bladder and 0.84/10.61 mm and 0.83/9.71 mm for the rectum. No statistical differences in the distribution of the evaluation metrics were found neither for the bladder (DSC: p = 0.07; HD95th: p = 0.35) nor for the rectum (DSC: p = 0.32; HD95th: p = 0.63). From the clinical visual grading assessment, the bias-corrected MRI resulted mostly in either no change or an improvement of the image quality and visualization of the organs’ boundaries compared with the original MRI.ConclusionThe bias field correction did not improve the anatomy visualization from a clinical point of view and the OARs’ auto-segmentation outputs generated by the GAN.
dc.language.isoEN
dc.publisherFrontiers Media S.A.
dc.subject.lccNeoplasms. Tumors. Oncology. Including cancer and carcinogens
dc.titleImpact of bias field correction on 0.35 T pelvic MR images: evaluation on generative adversarial network-based OARs’ auto-segmentation and visual grading assessment
dc.typeArticle
dc.description.keywordsN4ITK algorithm
dc.description.keywordsbias field artifact
dc.description.keywordsvisual grading assessment
dc.description.keywordsgenerative adversarial networks
dc.description.keywords0.35 T MRIgRT
dc.description.keywordsprostate cancer
dc.description.doi10.3389/fonc.2024.1294252
dc.title.journalFrontiers in Oncology
dc.identifier.e-issn2234-943X
dc.identifier.oaioai:doaj.org/journal:103dfb2a13814a68a1541bed4c8db6b3


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