Academic Journal
MICAFlow: Fast and robust MRI preprocessing bridging research neuroimaging and clinical practice.
| Τίτλος: | MICAFlow: Fast and robust MRI preprocessing bridging research neuroimaging and clinical practice. |
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| Συγγραφείς: | Goodall-Halliwell I; McConnell Brain Imaging Centre (BIC) and Centre of Excellence in Epilepsy at The Neuro (CEEN), Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada., DeKraker J; McConnell Brain Imaging Centre (BIC) and Centre of Excellence in Epilepsy at The Neuro (CEEN), Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada., Bautin P; McConnell Brain Imaging Centre (BIC) and Centre of Excellence in Epilepsy at The Neuro (CEEN), Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada., Mendelson D; McConnell Brain Imaging Centre (BIC) and Centre of Excellence in Epilepsy at The Neuro (CEEN), Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada., Cabalo DG; McConnell Brain Imaging Centre (BIC) and Centre of Excellence in Epilepsy at The Neuro (CEEN), Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada., Sahlas E; McConnell Brain Imaging Centre (BIC) and Centre of Excellence in Epilepsy at The Neuro (CEEN), Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada., Ngo A; McConnell Brain Imaging Centre (BIC) and Centre of Excellence in Epilepsy at The Neuro (CEEN), Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada., Xie K; McConnell Brain Imaging Centre (BIC) and Centre of Excellence in Epilepsy at The Neuro (CEEN), Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada., Lam J; McConnell Brain Imaging Centre (BIC) and Centre of Excellence in Epilepsy at The Neuro (CEEN), Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada., Smith M; McConnell Brain Imaging Centre (BIC) and Centre of Excellence in Epilepsy at The Neuro (CEEN), Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada., Hwang Y; McConnell Brain Imaging Centre (BIC) and Centre of Excellence in Epilepsy at The Neuro (CEEN), Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada., Vavassori L; McConnell Brain Imaging Centre (BIC) and Centre of Excellence in Epilepsy at The Neuro (CEEN), Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada., Milano P; Neurophysiology Unit and Epilepsy Centre, AOU Modena, University of Modena and Reggio Emilia, Modena, Italy., Chen J; McConnell Brain Imaging Centre (BIC) and Centre of Excellence in Epilepsy at The Neuro (CEEN), Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada., Dascal A; McConnell Brain Imaging Centre (BIC) and Centre of Excellence in Epilepsy at The Neuro (CEEN), Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada., Ding R; McConnell Brain Imaging Centre (BIC) and Centre of Excellence in Epilepsy at The Neuro (CEEN), Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada., Zhou G; McConnell Brain Imaging Centre (BIC) and Centre of Excellence in Epilepsy at The Neuro (CEEN), Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada., Naish M; McConnell Brain Imaging Centre (BIC) and Centre of Excellence in Epilepsy at The Neuro (CEEN), Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada., Mo J; Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China., Fadaie F; McConnell Brain Imaging Centre (BIC) and Centre of Excellence in Epilepsy at The Neuro (CEEN), Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada., Cruces RR; McConnell Brain Imaging Centre (BIC) and Centre of Excellence in Epilepsy at The Neuro (CEEN), Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada., Bernhardt BC; McConnell Brain Imaging Centre (BIC) and Centre of Excellence in Epilepsy at The Neuro (CEEN), Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada. |
| Πηγή: | Imaging neuroscience (Cambridge, Mass.) [Imaging Neurosci (Camb)] 2026 Sep 11; Vol. 4. Date of Electronic Publication: 2026 Sep 11 (Print Publication: 2026). |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: The MIT Press Country of Publication: United States NLM ID: 9918663686606676 Publication Model: eCollection Cited Medium: Internet ISSN: 2837-6056 (Electronic) Linking ISSN: 28376056 NLM ISO Abbreviation: Imaging Neurosci (Camb) Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Cambridge, Massachusetts : The MIT Press, [2023]- |
| Ιατρικοί όροι (MeSH): | Magnetic Resonance Imaging*/methods , Neuroimaging*/methods , Image Processing, Computer-Assisted*/methods , Brain*/diagnostic imaging, Humans ; Reproducibility of Results ; Deep Learning |
| Περίληψη: | MICAFlow is a fully automated MRI preprocessing pipeline designed to support translation of advanced neuroimaging workflows toward clinically feasible research and prospective clinical-evaluation settings. The pipeline emphasizes speed, robustness, and ease of use, focusing on structural and diffusion MRI. Key innovations include a Label-Augmented Modality-Agnostic Registration (LAMAReg) technique driven by deep learning segmentations for reliable cross-modal alignment, integration of state-of-the-art distortion corrections, and adherence to reproducible standards (Snakemake workflow, BIDSApp specifications). We describe the design of MICAFlow and evaluate its performance across heterogeneous datasets. First, accessibility: MICAFlow processes a multimodal MRI exam in minutes with clinically accessible hardware and without requiring GPU access, enabling same-day processing on clinically realistic hardware. Second, registration accuracy: LAMAReg achieves cutting-edge multimodal registration accuracy, yielding accurate alignment of diffusion MRI, FLAIR, and intra-subject T1-weighted images while remaining generally robust to common artifacts. Third, data reproducibility: Using identifiability, we show MICAFlow maintains consistent performance across diverse datasets, including subjects with pathology, and is closely comparable to contemporary pipelines. In sum, MICAFlow's combination of machine learning and efficient workflows produces research-grade preprocessing outputs with runtimes compatible with clinically oriented research workflows. This work demonstrates that advanced MRI preprocessing can be done fast and robustly, helping close the gap between research neuroimaging and future clinical evaluation of quantitative MRI techniques. The source code for MICAFlow is available here: https://github.com/MICA-MNI/MICAFlow, and for LAMAReg here: https://github.com/MICA-MNI/LAMAReg. (© 2026 The Authors. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.) |
| Competing Interests: | B.C.B., J.D.K., I.G.H., R.R.C., and J.C. hold stock in BrainScores Inc. |
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| Contributed Indexing: | Keywords: MRI in epilepsy; MRI preprocessing; diffusion MRI; image processing; magnetic resonance imaging; multimodal MRI |
| Entry Date(s): | Date Created: 20260916 Date Completed: 20260916 Latest Revision: 20260917 |
| Update Code: | 20260917 |
| PubMed Central ID: | PMC13576946 |
| DOI: | 10.1162/IMAG.a.1366 |
| PMID: | 42746345 |
| Βάση Δεδομένων: | MEDLINE |
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