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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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