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MATI: A GPU-accelerated toolbox for microstructural diffusion MRI simulation and data fitting with a graphical user interface.

作者信息

Xu Junzhong, Devan Sean P, Shi Diwei, Pamulaparthi Adithya, Yan Nicholas, Zu Zhongliang, Smith David S, Harkins Kevin D, Gore John C, Jiang Xiaoyu

机构信息

Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States of America; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States of America; Department of Physics and Astronomy, Vanderbilt University, Nashville, TN, United States of America.

Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States of America.

出版信息

Magn Reson Imaging. 2025 Oct;122:110428. doi: 10.1016/j.mri.2025.110428. Epub 2025 May 24.

Abstract

PURPOSE

To introduce MATI (Microstructural Analysis Toolbox for Imaging), a versatile MATLAB-based toolbox that combines both simulation and data fitting capabilities for microstructural dMRI research.

METHODS

MATI 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.

RESULTS

Optimized 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.

CONCLUSION

MATI offers a significant step toward translating advanced microstructural MRI techniques into clinical applications.

摘要

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