Modality-agnostic, patient-specific digital twins modeling temporally varying digestive motion
Rattachement africain : us, nl. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
Abstract Objective . Clinical implementation of deformable image registration (DIR) requires voxel-based spatial accuracy metrics such as manually identified landmarks, which are challenging to implement for highly mobile gastrointestinal (GI) organs. To address this, patient-specific digital twins (DTs) modeling temporally varying motion were created to assess the accuracy of DIR methods. Approach . A total of 21 motion phases simulating digestive GI motion as 4D image sequences were generated from static 3D patient scans using published analytical GI motion models through a multi-step semi-automated pipeline. Eleven datasets, including six T2-weighted FSE MRI (T2w MRI), two T1-weighted 4D golden-angle stack-of-stars, and three contrast-enhanced computed tomography scans were analyzed. The motion amplitudes of the DTs were assessed against real patient stomach motion amplitudes extracted from independent 4D MRI datasets using hierarchical motion reconstruction. The patient-specific DTs were then used to assess six different DIR methods using target registration error, Dice similarity coefficient (DSC), and the 95th percentile Hausdorff distance using summary metrics and voxel-level granular visualizations. Finally, for a subset of T2w MRI scans collected from patients treated with magnetic resonance-guided radiation therapy, dose distributions were warped and accumulated to assess dose warping errors (DWEs), including evaluations of DIR performance in both low- and high-dose regions for patient-specific error estimation. Main results . Our proposed pipeline synthesized patient-specific DTs modeling realistic GI motion, achieving mean and maximum motion amplitudes and a mean log Jacobian determinant within 0.8 mm and 0.01, respectively, similar to published real-patient gastric motion data. It also enables the extraction of detailed quantitative DIR performance metrics and supports rigorous validation of dose mapping accuracy prior to clinical implementation. Significance . The developed pipeline enables rigorously testing DIR tools for dynamic, anatomically complex regions facilitating granular spatial and dosimetric accuracies.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Modality-agnostic, patient-specific digital twins modeling temporally varying digestive motion
- Date Crossref
- 06/01/2026
- Éditeur
- IOP Publishing
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Memorial Sloan Kettering Cancer Center pays non établi dans la noticeÉtablissement de santé
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Cornell University Computer and Information Science pays non établi dans la noticeUniversité ou école supérieure
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University Medical Center Utrecht pays non établi dans la noticeÉtablissement de santé
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The University of Texas MD Anderson Cancer Center Department of Radiation Physics pays non établi dans la noticeÉtablissement de santé
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Duke University Carl E. Ravin Advanced Imaging Laboratories and Center for Virtual Imaging Trials pays non établi dans la noticeUniversité ou école supérieure
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Advanced Imaging Research (United States) pays non établi dans la noticeEntreprise
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University of Michigan Department of Radiation Oncology pays non établi dans la noticeUniversité ou école supérieure
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Michigan Medicine pays non établi dans la noticeÉtablissement de santé
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Department of Medical Physics pays non établi dans la noticeInstitution
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University Medical Centre Utrecht Department of Radiotherapy pays non établi dans la noticeUniversité ou école supérieure
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School of Electrical and Computer Engineering pays non établi dans la noticeUniversité ou école supérieure
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Dept of Medical Physics pays non établi dans la noticeInstitution
Memorial Sloan Kettering Cancer Center, Computer and Information Science — Cornell University et University Medical Center Utrecht, avec 9 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.