ArticleMagnetic resonance imaging2025
MATI: A GPU-accelerated toolbox for microstructural diffusion MRI simulation and data fitting with a graphical user interface.
Article in Magnetic resonance imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- MR Virtual Cytometry for Zone-specific Prostate Cancer Characterization with Histologic and Molecular Validation.Radiology. Imaging cancer · 2026Article
- Time-dependent diffusion MRI for assessing tumor microstructure and prognostic risk factors in cervical cancer.Abdominal radiology (New York) · 2026Article
- The role of diffusion-weighted MRI in biological image-guided radiation therapy: a roadmap.Physics in medicine and biology · 2026Review
- The predictive value of MR cytometry in histological differentiation of rectal cancer: an exploratory study.European radiology · 2026Observational
- MRI cytometry imaging for cervical cancer differential diagnosis: a preliminary study.BMC medical imaging · 2025Article
- Quantitative Time-Dependent Diffusion MRI for Diagnosis and Aggressiveness Assessment of Endometrial Cancer: A Prospective Study.Korean journal of radiology · 2025Article
- Comparison of MR cytometry methods in predicting immunohistochemical factor status and molecular subtypes of breast cancer.Radiology and oncology · 2025Article
- Gpu-accelerated JEMRIS for extensive MRI simulations.Magma (New York, N.Y.) · 2025Article
- Massively parallelized brain tractography using compute clusters, supercomputers, and graphics processing units.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
purposeTo introduce MATI (Microstructural Analysis Toolbox for Imaging), a versatile MATLAB-based toolbox that combines both simulation and data fitting capabilities for microstructural dMRI research.
methodsMATI provides a user-friendly, graphical user interface that enables researchers, including those without much programming experience, to perform advanced simulations and data analyses for microstructural MRI research. For simulation, MATI supports arbitrary microstructural tissues and pulse sequences. For data fitting, MATI supports a range of fitting methods, including traditional non-linear least squares, Bayesian approaches, machine learning, and dictionary matching methods, allowing users to tailor analyses based on specific research needs.
resultsOptimized with vectorized matrix operations and high-performance numerical libraries, MATI achieves high computational efficiency, enabling rapid simulations and data fitting on CPU and GPU hardware. While designed for microstructural dMRI, MATI's generalized framework can be extended to other imaging methods, making it a flexible and scalable tool for quantitative MRI research.
conclusionMATI offers a significant step toward translating advanced microstructural MRI techniques into clinical applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.