Fast ℓ1-SPIRiT Compressed Sensing Parallel Imaging MRI: Scalable Parallel Implementation and Clinically Feasible Runtime

By Mark Murphy, Marcus T. Alley, J. Demmel, Kurt Keutzer, Shreyas S. Vasanawala, Michael Lustig. IEEE Transactions on Medical Imaging, 2012

314 citations recorded by the source corpus.

We present l1 -SPIRiT, a simple algorithm for auto calibrating parallel imaging (acPI) and compressed sensing (CS) that permits an efficient implementation with clinically-feasible runtimes. We propose a CS objective function that minimizes cross-channel joint sparsity in the wavelet domain. Our reconstruction minimizes this objective via iterative soft-thresholding, and integrates naturally with iterative self-consistent parallel imaging (SPIRiT). Like many iterative magnetic resonance imaging reconstructions, l1-SPIRiT's image quality comes at a high computational cost. Excessively long runtimes are a barrier to the clinical use of any reconstruction approach, and thus we discuss our approach to efficiently parallelizing l1 -SPIRiT and to achieving clinically-feasible runtimes. We present parallelizations of l1 -SPIRiT for both multi-GPU systems and multi-core CPUs, and discuss the software optimization and parallelization decisions made in our implementation. The performance of these alternatives depends on the processor architecture, the size of the image matrix, and the number of parallel imaging channels. Fundamentally, achieving fast runtime requires the correct trade-off between cache usage and parallelization overheads. We demonstrate image quality via a case from our clinical experimentation, using a custom 3DFT spoiled gradient echo (SPGR) sequence with up to 8× acceleration via Poisson-disc undersampling in the two phase-encoded directions.

Subfields: Computational Mechanics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/bd5d49c0fa675fc698666382d05b7f9b34c533aa

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In Computational Mechanics

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

ESPIRiT — An Eigenvalue Approach to Autocalibrating Parallel MRI: Where SENSE meets GRAPPA (2014, methodology): “Volumes corresponding to the different maps have then been combined as described before to obtain a single volume for comparison with a similar `1-SPIRiT [4, 21] reconstruction.”
Medical image processing on the GPU - Past, present and future (2013, methodology): “Murphy et al. (2012) combined compressed sensing with parallel imaging, for 32 channels the multi-GPU implementation was about 50 times faster compared”
DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction (2018, background): “Although there are promising studies applying fast CS-MRI in clinical environments [31], [32], [33], most routine clinical MRI scanning is still based on standard fully-sampled Cartesian sequences or is accelerated only using parallel imaging.”