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Abstract #4339

A semi-data-driven cellular microstructural model considering cell size distribution in diffusion MRI

Diwei Shi1, Fan Liu2, Sisi Li2, Li Chen1, Xiaoyu Jiang3,4, Quanshui Zheng1, Junzhong Xu3,4,5,6, and Hua Guo2
1Center for Nano and Micro Mechanics, Department of Engineering Mechanics, Tsinghua University, Beijing, China, 2Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China, 3Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States, 4Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States, 5Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States, 6Department of Physics and Astronomy, Vanderbilt University, Nashville, TN, United States

Synopsis

Keywords: Signal Modeling, Microstructure, quantitative microstructure imaging, signal modeling

Motivation: Incorporating the impact of cell size distribution is challenging in current dMRI-based microstructural imaging. All relevant models fail to provide an analytical signal expression, but instead replace the intracellular signal with the sum of signal terms corresponding to different cell diameters. Although this is intuitive, subsequent equations are usually ill-conditioned and hard to resolve.

Goal(s): To derive the analytical expression for dMRI signals and rebuild a cellular microstructural model.

Approach: We performed theoretical modelling based on simulated signals and validations on numerical simulations and in-vitro cell experiments.

Results: A semi-data-driven cellular microstructural model is proposed and it outperforms the published method.

Impact: This work provides the first analytical expression for dMRI signals while incorporating cell size distribution. The proposed microstructural model can extract not only accurate mean cell size, but also distribution information, which provides an additional biomarker for tumor monitoring.

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Keywords