基于图形处理器加速 Wave-CAIPI 重建的改进共轭梯度法
A Graphics Processing Unit-Based Modified Conjugate Gradient Method for Accelerating Wave-CAIPI Reconstruction
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摘要: Wave-CAIPI 是一种利用多通道线圈和 k 空间螺旋轨迹采样来加速磁共振成像的新 3D 成像方法。然而, Wave-CAIPI 采集的 3D 数据对于重建计算是巨大的。为了加速重建过程, 该文使用基于图形处理器改进的共轭梯度算法实现了 Wave-CAIPI 重建, 减少了重建时间。水模数据集和体内人脑数据集的实验表明, 基于图形处理器的 Wave-CAIPI 重建可以获得与传统基于中央处理器的 Wave-CAIPI 重建类似的图像结果, 且重建效率显著提升。Abstract: Wave-CAIPI is a novel 3D imaging method with multiple-channel coils and corkscrew trajectories in k-space to speed up magnetic resonance imaging acquisition. However, the 3D data acquisitions of Wave-CAIPI are usually time consuming. In order to accelerate the reconstruction procedure, we realized a Wave-CAIPI reconstruction method using a modified GPU-based conjugate gradient algorithm to reduce time cost of the image reconstructions. The experiments of phantom and in vivo human brain show that the proposed GPU-based Wave- CAIPI reconstruction can achieve similar imaging results with less time cost, comparing to the conventional CPU-based Wave-CAIPI reconstruction.