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Abstract #2426

A GPU-based Modified Conjugate Gradient Method for Accelerating Wave-CAIPI Reconstruction

Haifeng Wang1, Shi Su1, Xin Liu1, Yuchou Chang2, and Dong Liang1

1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, 2Department of Computer Science and Technology Engineering, University of Houston-Downtown, Houston, TX, United States

Wave-CAIPI is a novel 3D imaging method with corkscrew trajectory in k-space to speed up MRI acquisition. However, the 3D data acquisitions of Wave-CAIPI are also tremendous for reconstruction calculations. In order to accelerate the reconstruction procedure, we realized a Wave-CAIPI reconstruction using a modified GPU-based conjugate gradient (CG) algorithm to reduce time cost of reconstructions. The experiments of in vivo human brain dataset show that using our GPU-based Wave-CAIPI reconstruction can achieve similar image results as the conventional CPU-based Wave-CAIPI reconstruction with less time cost than the conventional CPU-based Wave-CAIPI reconstruction.

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