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Abstract #3396

Undersampled High-frequency Diffusion Signal Recovery Using Model-free Multi-scale Dictionary Learning

Enhao Gong 1 , Qiyuan Tian 1 , John M Pauly 1 , and Jennifer A McNab 2

1 Electrical Engineering, STANFORD UNIVERSITY, Stanford, California, United States, 2 Radiology, STANFORD UNIVERSITY, Stanford, California, United States

Low Signal-to-Noise Ratio (SNR), especially at high b-values, is a critical problem for Diffusion MRI (dMRI). Methods with different signal models may fail to reconstruct under-sampled data from noisy measurement. Diffusion MRI signal contains redundancy as a multi-dimensional signal in both k-space and q-space. Here we proposed a novel approach to recover signal without explicitly enforcing any physical signal model. The method is model-free but learns the multi-dimensional redundancy, including the redundancy between neighborhood voxels, different directions and low\high b-values, from training samples. A Dictionary Learning approach is used to recover under-sampled signals in q-space. Quantitative results demonstrate the method can more accurately predict high b-value signal (>3000s/mm2) from low b-value signal. Also it produces more accurate physiological metrics such as Generalized Fractional Anisotropy (GFA) and Orientation Distribution Function (ODF) that potentially help to resolve intra-voxel crossing fibers.

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