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Abstract #4815

Improving data fitting accuracy and precision for MRI cytometry

Dara Chanthavisay1,2, Xiaoyu Jiang1, John C. Gore1, and Junzhong Xu1,2
1Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN, United States, 2Biomedical Engineering, Vanderbilt University, Nashville, TN, United States

Synopsis

Keywords: Diffusion Analysis & Visualization, Diffusion Analysis and Visualization, Data fitting, optimization, microstructure, MRI cytometry

Motivation: A generalized protocol for efficient and accurate data fitting is important.

Goal(s): To optimize an accurate and precise fitting approach for MRI cytometry.

Approach: A Monte Carlo simulation was performed to optimize parameters for a dictionary-based fitting method. Additionally, a selection of data fitting methods were compared for accuracy and speed.

Results: A dictionary step size of 𝚫d< 0.5 μm and prior Gaussian distribution with dmean = 14 μm and σd = 100 μm provide accurate and precise data fitting for a broad range of ground truth cell size values. Dictionary-matching with a prior distribution proved to be the most robust.

Impact: To improve and achieve robust data fitting accuracy and precision for clinical applications.

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Keywords