Dhiraj D. Kalamkar1, Joshua D. Trzasko2, Srinivas Sridharan1, Mikhail Smelyanskiy3, Daehyun Kim3, Yunhong Shu4, Matt A. Bernstein4, Bharat Kaul1, Pradeep Dubey3, Armando Manduca2
1Parallel Computing Lab, Intel Labs, Bangalore, KA, India; 2Mayo Clinic, Rochester, MN, United States; 3Parallel Computing Lab, Intel Labs, Santa Clara, CA, United States; 4Department of Radiology, Mayo Clinic, Rochester, MN, United States
With increasing usage of higher-resolution acquisitions, more receiver channels, and iterative reconstruction strategies, the ability to quickly and accurately transform an image to and from k-space, known as reverse gridding and gridding, is crucial for non-Cartesian MRI applications. In practice, both of these operations are typically realized via the non-uniform fast Fourier transform (NUFFT). In this work, we propose a novel preprocessing and parallelization strategy for both the forward and adjoint NUFFT targeted for x86 architectures. We demonstrate that this implementation strategy, which is based on a variable-size geometric partitioning along with a barrier-free task queue, and selective privatization, is substantially faster than contemporary x86 implementations, and computationally competitive with state-of-the-art GPU implementations.