Accelerated phase contrast flow imaging with direct complex difference reconstruction

Abstract

Purpose To propose and evaluate a new model-based reconstruction method for highly accelerated phase-contrast magnetic resonance imaging (PC-MRI) with sparse sampling. Theory and Methods This work presents a new constrained reconstruction method based on low-rank and sparsity constraints to accelerate PC-MRI. More specifically, we formulate the image reconstruction problem into separate reconstructions of flow-reference image sequence and complex differences. We then utilize the joint partial separability and sparsity constraints to enable high quality reconstruction from highly undersampled (k, t)-space data. We further integrate the proposed method with ESPIRiT based parallel imaging model to effectively handle multichannel acquisition. Results The proposed method was evaluated with in vivo data acquired from both 2D and 3D PC flow imaging experiments, and compared with several state-of-the-art methods. Experimental results demonstrate that the proposed method leads to more accurate velocity reconstruction from highly undersampled (k, t)-space data, and particularly superior capability of capturing the peak velocity of blood flow. In terms of flow visualization, blood flow patterns obtained from the proposed reconstruction also exhibit better agreement with those obtained from the fully sampled reference. Conclusion The proposed method achieves improved accuracy over several state-of-the-art methods for velocity reconstruction with highly accelerated (k, t)-space data

Improved magnetic resonance fingerprinting reconstruction with low‐rank and subspace modeling

“…Over the past few years, the low-rank and subspace model-based imaging paradigm (9) has been successfully applied to a variety of imaging applications, including cardiac imaging (11,35,36), phase-contrast flow imaging (37,38), speech imaging (39), functional MRI (40), spectroscopic imaging (41), and parameter mapping (12,13,15,17,42). Our contribution here is to utilize the low-dimensional subspace structure of magnetization dynamics to enable FIG.…”

Accelerating 4D flow MRI by exploiting low‐rank matrix structure and hadamard sparsity

“…The idea of exploiting complex difference sparsity and partial separability of the temporal basis functions has already been proposed . In , a complex difference operation was applied in k‐space and sparsity was enforced in a transformed domain . The temporal basis was estimated from a calibration area by building a Casorati matrix and exploiting its low rank nature .…”

“…In , a complex difference operation was applied in k‐space and sparsity was enforced in a transformed domain . The temporal basis was estimated from a calibration area by building a Casorati matrix and exploiting its low rank nature . These temporal basis functions were subsequently used as sparsity domain in a CS‐type reconstruction applied to the complex difference of the k‐space data.…”

Real-time phase-contrast flow cardiovascular magnetic resonance with low-rank modeling and parallel imaging

“…It has been demonstrated in [ 40 , 43 , 55 ] that joint low-rank and sparsity constrained reconstruction leads to improved performance for dynamic MRI. Along this line, we can extend the proposed real-time flow imaging method by exploiting our early work [ 56 ] in cine flow imaging, although such an extension will come with additional computational cost.…”