Optimization principles and application performance evaluation of a multithreaded GPU using CUDA
Proceedings of the 13th ACM SIGPLAN Symposium on Principles and practice of parallel programming
Accelerating advanced MRI reconstructions on GPUs
Journal of Parallel and Distributed Computing
Accelerating MR image reconstruction on GPUs
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
IEEE Transactions on Signal Processing
Automatic restructuring of GPU kernels for exploiting inter-thread data locality
CC'12 Proceedings of the 21st international conference on Compiler Construction
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We propose a fast implementation for iterative MR image reconstruction using Graphics Processing Units (GPU). In MRI, iterative reconstruction with conjugate gradient algorithms allows for accurate modeling the physics of the imaging system. Specifically, methods have been reported to compensate for the magnetic field inhomogeneity induced by the susceptibility differences near the air/tissue interface in human brain (such as orbitofrontal cortex). Our group has previously presented an algorithm for field inhomogeneity compensation using magnetic field map and its gradients. However, classical iterative reconstruction algorithms are computationally costly, and thus significantly increase the computation time. To remedy this problem, one can utilize the fact that these iterative MR image reconstruction algorithms are highly parallelizable. Therefore, parallel computational hardware, such as GPU, can dramatically improve their performance. In this work, we present an implementation of our field inhomogeneity compensation technique using NVIDA CUDA (Compute Unified Device Architecture)-enabled GPo. We show that the proposed implementation significantly reduces the computation times around two orders of magnitude (compared with non-GPU implementation) while accurately compensating for field inhomogeneity.