Abstract #2554
A Parallel Imaging and Compressed Sensing Combined Framework for Accelerating High-resolution Diffusion Tensor Imaging Utilizing Inter-image Correlation
Xinwei Shi 1,2 , Xiaodong Ma 2 , Wenchuan Wu 2 , Feng Huang 3 , Chun Yuan 2,4 , and Hua Guo 2
1
Department of Electrical Engineering,
Stanford University, Stanford, CA, United States,
2
Center
for Biomedical Imaging Research, Tsinghua University,
Beijing, Beijing, China,
3
Philips Healthcare,
FL, United States,
4
Department
of Radiology, University of Washington, Seattle, WA,
United States
Increasing acquisition efficiency is always a challenge
in high-resolution diffusion tensor imaging (DTI), which
has low SNR and is sensitive to image artifacts. In this
work, a parallel imaging and compressed sensing combined
reconstruction framework is proposed, which features
multi-shot motion error correction, parallel imaging
kernel calibration and anisotropic sparsity model
utilizing inter-image correlation tailored for
high-resolution DTI. The proposed method, titled as
AS-SPIRiT, is implemented based on multi-shot variable
density spiral, and evaluated in in-vivo brain DTI
experiment. Compared with traditional parallel imaging
methods and other sparsity models, AS-SPIRiT provides
better preserved image quality and more accurate DTI
parameters.
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