Keywords: Diffusion Analysis & Visualization, Quantitative Imaging, metabolic imaging
Motivation: MADI is a promising new diffusion-based MR method that produces quantitative maps of physiologic parameters: mean cell volume, cell (number) density, and oxidative metabolic activity. Quantitative maps display metabolic activity and uniquely characterize tumors. Accuracy of MADI maps is compromised by CSF-rich voxel contamination.
Goal(s): To improve the accuracy of MADI parameter maps by removing CSF-rich voxels and denoising diffusion-weighted images prior to quantification.
Approach: Develop and evaluate 1) a filter to remove CSF-rich voxels and 2) denoising of diffusion-weighted images using biexponential fitting.
Results: CSF-rich voxels were successfully removed, improving MADI quantitative maps. Denoising remarkably reduced noise in the diffusion-weighted images.
Impact: MADI produces quantitative maps of physiologic cellular parameters: cell volume, cell density, and oxidative metabolism. It uniquely characterizes tumor metabolism and treatment response. This work improves MADI accuracy by robust filtering of CSF-rich voxels.
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