GPU-Accelerated Radial Image Reconstruction with an Improved Parallel Gridding Method

Jiangsheng Yu¹, Yiqun Xue¹, Xia Zhao¹, Ping Wang¹, Walter Witschey¹, Hee Kwon Song¹

Graphic processing units (GPU) have recently been utilized to accelerate radial MR image reconstruction. One of the challenges of gridding using GPU is the issue of synchronization, and several approaches have been reported to address this issue. This work presents a simple and effective solution for avoiding this synchronization problem by utilizing a divide-and-conquer strategy that ensures that the gridded points are non-overlapping. Performance tests on the reconstruction of a DCE-MRI data set demonstrates that an acceleration factor of 67 can be achieved on an Nvida Tesla C2050 GPU system.