Keywords: Diffusion Analysis & Visualization, Analysis/Processing
Motivation: Diffusion-relaxation multidimensional-MRI (MD-MRI) provides valuable sub-voxel information.However, it suffers from estimation instability and high computational costs.
Goal(s): To reduce high computational cost for MD-MRI parameter estimation via a kernel-driven signal dictionary in conjunction with pattern matching, while preserving the quality of parameter maps.
Approach: Generate a synthesized signal dictionary using a kernel function, perform pattern matching, and compare the results with state-of-the-art Monte Carlo (MC) inversion. Analyze the results.
Results: We propose a novel framework for processing MD-MRI data and estimating diffusion-relaxation parameters. Applied to data from diverse age groups, it enhances parameter maps quality and reduces computational time.
Impact: We introduce a novel parameter estimation framework that reduces computational time by up to 12 times while preserving parameter map quality, allowing for efficient processing of large data in clinical settings.
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