Fast $\ell_1$-SPIRiT Compressed Sensing Parallel Imaging MRI: Scalable Parallel Implementation and Clinically Feasible Runtime

Abstract

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 MRI 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.

DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction

“…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. The main challenges are: (1) satisfying the incoherence criteria required by CS-MRI [1]; (2) the widely applied sparsifying transforms might be too simple to capture complex image details associated with subtle differences of biological tissues, e.g., TV based sparsifying transform penalises local variation in the reconstructed images but can introduce staircase artefacts and the wavelet transform enforces point singularities and isotropic features but orthogonal wavelets may lead to blocky artefacts [34], [35], [36]; (3) nonlinear optimisation solvers usually involve iterative computation that may result in relatively long reconstruction time [1]; (4) inappropriate hyperparameters predicted in current CS-MRI methods can cause over-regularisation that will yield overly smooth and unnatural looking reconstructions or images with residual undersampling artefacts [1].…”

Medical image processing on the GPU – Past, present and future

“…Smith et al (2012) instead used a Split Bregman solver and were able to reconstruct breast images of the size 4096 x 4096 in about 8 seconds. Murphy et al (2012) combined compressed sensing with parallel imaging, for 32 channels the multi-GPU implementation was about 50 times faster compared to a multi-threaded C++ implementation.…”

Improved l1-SPIRiT using 3D walsh transform-based sparsity basis

“…l1-SPIRiT [30,31] belongs to the first set of CS-pMRI methods. In this method, as an alternative to form a large linear system, the joint optimization with multiple objective functions was proposed and the solutions were iteratively constrained to satisfy Wavelet-sparsity constraints, data fidelity and calibration consistency.…”