Accelerating Compressed-Sensing-Based DCE-MR Image Reconstruction with GPU

Jiangsheng Yu¹, Yiqun Xue², Hee Kwon Song²

Synopsis: Temporally constrained reconstruction based on compressed sensing (CS) has recently been developed for dynamic MR imaging to obtain high temporal resolution without losing image quality. The intensive computation overhead in CS reconstruction has limited the application for clinical data processing where large data sets are generated from multi-slice and multi-channel acquisition. The current work presents a parallelized GPU implementation to accelerate the CS-based image reconstruction in DCE-MRI. The forward and backward gridding operations, which are the most-time consuming part of the conjugate gradient searching, is addressed with a radial-point driven parallelization approach by assigning a thread for each radial point operation. A comparison with the C++ sequential implementation shows an acceleration factor of ~15 was achieved on a moderately GPU-powered laptop computer.