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Informed Dictionary-Guided Monte Carlo Inversion for Robust and Reproducible Multidimensional MRI.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Informed Dictionary-Guided Monte Carlo Inversion for Robust and Reproducible Multidimensional MRI.
Συγγραφείς: Park JS; Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA., Manninen E; Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA., Yang Y; Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland, USA., Benjamini D; Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA.
Πηγή: Magnetic resonance in medicine [Magn Reson Med] 2026 May; Vol. 95 (5), pp. 2947-2962. Date of Electronic Publication: 2025 Dec 28.
Τύπος έκδοσης: Journal Article
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Wiley Country of Publication: United States NLM ID: 8505245 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1522-2594 (Electronic) Linking ISSN: 07403194 NLM ISO Abbreviation: Magn Reson Med Subsets: MEDLINE
Imprint Name(s): Publication: 1999- : New York, NY : Wiley
Original Publication: San Diego : Academic Press
Ιατρικοί όροι (MeSH): Brain*/diagnostic imaging , Image Processing, Computer-Assisted*/methods , Magnetic Resonance Imaging*/methods, Humans ; Monte Carlo Method ; Reproducibility of Results ; Computer Simulation ; Signal-To-Noise Ratio ; Algorithms ; Male ; Female ; Adult
Περίληψη: Purpose: To develop a robust and efficient multidimensional MRI (MD-MRI) data processing framework for accurately estimating joint frequency-dependent diffusion-relaxation distributions, while overcoming computational limitations and noise instability inherent to Monte Carlo (MC) inversion.
Methods: We introduced an Informed Dictionary-guided Monte Carlo (ID-MC) strategy that incorporates data-driven dictionary matching into the inversion process, followed by targeted local mutation refinement to enhance flexibility and reduce overfitting. This hybrid approach aims to improve the stability, accuracy, and reproducibility of MD-MRI parameter estimation. We evaluated ID-MC through in silico simulations across a range of signal-to-noise ratios and in vivo test-retest experiments in the human brain. Reproducibility was assessed using intraclass correlation coefficients (ICC) and within-subject variability, allowing rigorous comparison with MC.
Results: In simulations, the ID-MC approach consistently achieved lower fitting errors and higher estimation accuracy across a wide range of noise levels, demonstrating its ability to balance local flexibility and global biological plausibility. Compared to MC inversion, ID-MC also reduced computation time by approximately 69%, highlighting its potential for time-efficient large-scale applications. In in vivo test-retest analyses, ID-MC substantially improved reproducibility, doubling the number of MD-MRI parameters with ICC greater than 0.75 relative to MC. Notably, diffusion frequency-dependent parameters, previously poorly reproducible with MC, showed up to 146% higher ICC with ID-MC.
Conclusion: By integrating data-driven dictionary matching with targeted mutation refinement, ID-MC improves the robustness, reproducibility, and computational efficiency of MD-MRI inversion, supporting studies that require highly sensitive detection of subtle brain microstructural changes.
(Published 2025. This article is a U.S. Government work and is in the public domain in the USA. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)
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Grant Information: United States AG NIA NIH HHS
Contributed Indexing: Keywords: MC inversion; brain microstructure; dictionary matching; diffusion and relaxation; multidimensional MRI
Entry Date(s): Date Created: 20251229 Date Completed: 20260307 Latest Revision: 20260307
Update Code: 20260308
PubMed Central ID: PMC12962224
DOI: 10.1002/mrm.70228
PMID: 41457529
Βάση Δεδομένων: MEDLINE
Περιγραφή
ISSN:1522-2594
DOI:10.1002/mrm.70228