Haifeng Wang^{1}, Shi Su^{1}, Xin Liu^{1}, Yuchou Chang^{2}, and Dong Liang^{1}

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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Fig 1. Algorithm flow of the proposed GPU-based Wave-CAIPI reconstruction with the modified CG algorithm on the NVIDIA CUDA platform.

Fig 2. Time-cost comparison of the BPE and Wave-CAIPI reconstructions based CPU and GPU on the MATLAB and NVIDIA CUDA platforms. Here, the acceleration factors are all Rz× Ry =
3×3; Rz is the acceleration factor along Z direction; Ry is
the acceleration factor along Y direction.

Fig 3. Reconstruction comparison of 2D CAIPI, BPE
and Wave-CAIPI reconstructions based on CPU and GPU at
the acceleration factors of Rz× Ry = 3×3.

Fig 4. Difference maps between 2D CAIPI and the
two reconstruction methods, BPE and Wave-CAIPI, respectively based on CPU and GPU at the acceleration factors of Rz× Ry = 3×3.