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
Electrical Engineering, STANFORD UNIVERSITY,
Stanford, California, United States,
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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