Algorithms, models and methods for images and signals of the human brain = Algorithmes, modèles et méthodes pour les images et les signaux du cerveau humain ICM Paris (ARAMIS), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière AP-HP, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière AP-HP, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Centre Inria de Sorbonne Université, Centre Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), ANR-23-IACL-0008,PR AI RIE-PSAI,PR AI RIE-PSAI - Paris School of Artificial Intelligence(2023), ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), ANR-10-IAHU-0006,IHU-A-ICM,Institut de Neurosciences Translationnelles de Paris(2010), ANR-23-CE45-0005,ANO-NEURO,Détection d'anomalies dans les images cérébrales multimodales pour l'aide au diagnostic des démences(2023), European Project: 101136607,HORIZON-WIDERA-2023-ACCESS-01,HORIZON-WIDERA-2023-ACCESS-01,CLARA(2024)
*International audience ; Neurodegenerative diseases require longitudinal assessment to track disease progression, with brain volume change from T1-weighted MRI serving as a key biomarker that demands robust and precise processing methods. Although several longitudinal preprocessing pipelines exist, there is no consensus on which offers the highest reliability. In this study, we evaluate six widely used open-source tools for cross-sectional and longitudinal preprocessing of T1-weighted MRI: FreeSurfer, SAMSEG, ANTs, ANTsPyNet, SPM12, and CAT12. We assess their robustness using test-retest data from the MIRIAD cohort, in which no meaningful anatomical change is expected between repeated scans. Our results show that, overall, longitudinal preprocessing methods demonstrate greater robustness than their cross-sectional counterparts. However, this pattern is not consistent across all tools: some longitudinal implementations do not outperform their cross-sectional versions, and the magnitude of improvement varies by method and brain region. We conclude that while the existing longitudinal preprocessing approaches can improve consistency in brain volume estimation, these benefits are method-dependent.*
