High‐dimensional fast convolutional framework (HICU) for calibrationless MRI

Abstract

Purpose To present a computational procedure for accelerated, calibrationless magnetic resonance image (Cl‐MRI) reconstruction that is fast, memory efficient, and scales to high‐dimensional imaging. Theory and Methods Cl‐MRI methods can enable high acceleration rates and flexible sampling patterns, but their clinical application is limited by computational complexity and large memory footprint. The proposed computational procedure, HIgh‐dimensional fast convolutional framework (HICU), provides fast, memory‐efficient recovery of unsampled k‐space points. For demonstration, HICU is applied to 6 2D T2‐weighted brain, 7 2D cardiac cine, 5 3D knee, and 1 multi‐shot diffusion weighted imaging (MSDWI) datasets. Results The 2D imaging results show that HICU can offer 1‐2 orders of magnitude computation speedup compared to other Cl‐MRI methods without sacrificing imaging quality. The 2D cine and 3D imaging results show that the computational acceleration techniques included in HICU yield computing time on par with SENSE‐based compressed sensing methods with up to 3 dB improvement in signal‐to‐error ratio and better perceptual quality. The MSDWI results demonstrate the feasibility of HICU for a challenging multi‐shot echo‐planar imaging application. Conclusions The presented method, HICU, offers efficient computation and scalability as well as extendibility to a wide variety of MRI applications.

A 2D-GRAPPA Algorithm with a Boomerang Kernel for 3D MRI Data Accelerated along Two Phase-Encoding Directions

“…To this end, researchers have developed advanced reconstruction methods, which collect a portion of k-space data and estimate unacquired or missing data points, including sensitivity encoding (SENSE), simultaneous acquisition of spatial harmonics (SMASH), AUTO-SMASH and generalized autocalibrating partially parallel acquisitions (GRAPPA) [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 ]. Other advanced methods include SAKE [ 10 ], AC-LORAKs [ 11 ], PRUNO [ 12 ] and HICU [ 13 ], which rely on the linear predictability in the MRI data [ 14 ]. In these algorithms, the unfolded image or missing data points can be reconstructed from sensitivity maps [ 1 ], from a linear combination of neighboring data points [ 7 , 8 , 14 , 15 ], by utilizing a structured low-rank matrix [ 10 , 11 , 16 , 17 ] or a convolutional framework [ 13 ].…”

“…Other advanced methods include SAKE [ 10 ], AC-LORAKs [ 11 ], PRUNO [ 12 ] and HICU [ 13 ], which rely on the linear predictability in the MRI data [ 14 ]. In these algorithms, the unfolded image or missing data points can be reconstructed from sensitivity maps [ 1 ], from a linear combination of neighboring data points [ 7 , 8 , 14 , 15 ], by utilizing a structured low-rank matrix [ 10 , 11 , 16 , 17 ] or a convolutional framework [ 13 ].…”