3D Cartesian MRI with compressed sensing and variable view sharing using complementary poisson‐disc sampling

Abstract

Purpose To enable robust high spatio-temporal-resolution 3D Cartesian MRI using a scheme incorporating a novel variable density random k-space sampling trajectory allowing flexible and retrospective selection of the temporal footprint with compressed sensing (CS). Methods A “complementary Poisson-disc” k-space sampling trajectory was designed to allow view sharing and varying combinations of reduced view sharing with CS from the same prospective acquisition. These schemes were used for two-point-Dixon-based dynamic contrast-enhanced MRI (DCE-MRI) of the breast and abdomen. Results were validated in vivo with a novel approach using variable-flip-angle data, which was retrospectively accelerated using the same methods but offered a ground truth. Results In breast DCE-MRI, the temporal footprint could be reduced 2.3-fold retrospectively without introducing noticeable artifacts, improving depiction of rapidly enhancing lesions. Further, experiments with variable-flip-angle data showed that reducing view sharing improved accuracy in reconstruction and T1 mapping. In abdominal MRI, 2.3-fold and 3.6-fold reductions in temporal footprint allowed reduced motion artifacts. Conclusion The complementary-Poisson-disc k-space sampling trajectory allowed a retrospective spatiotemporal resolution tradeoff using CS and view sharing, imparting robustness to motion and contrast enhancement. The technique was also validated using a novel approach of fully acquired variable-flip-angle acquisition.

Volumetric abdominal perfusion measurement using a pseudo‐randomly sampled 3D fast‐spin‐echo (FSE) arterial spin labeling (ASL) sequence and compressed sensing reconstruction

“…While reducing the effective acceleration to an overall modest 4‐fold acceleration (neglecting the temporal dimension), this pattern combined increased k‐space sampling, especially in the higher frequencies with sufficient incoherent sampling for a successful k‐t CS reconstruction, hence minimizing the spatial resolution loss as shown by the reduced blurring level. The success of this strategy is similar to the favorable results with view‐sharing to gain spatiotemporal resolution in 3D Cartesian MRI (e.g., for dynamic contrast‐enhanced applications) . Blinded radiologist assessment showed that perfusion‐weighted volumes acquired with this sampling strategy led to diagnostically acceptable images, especially in terms of cortico‐medullary contrast and low artifacts level compared to single‐averaged CS accelerated data.…”

“…The success of this strategy is similar to the favorable results with view-sharing to gain spatiotemporal resolution in 3D Cartesian MRI (e.g., for dynamic contrast-enhanced applications). 41 Blinded radiologist assessment showed that perfusion-weighted volumes acquired with this sampling strategy led to diagnostically acceptable images, especially in terms of cortico-medullary contrast and low artifacts level compared to single-averaged CS accelerated data. A similar evaluation should be performed in clinical cases to fully assess the clinical applicability of this sequence.…”

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

“…First, investigations have sought the best undersampling scheme, which should be as random as possible to create incoherent undersampling artefacts so that a proper nonlinear reconstruction can be applied to suppress noise-like artefacts without degrading image quality of the reconstruction [12], [13], [14]. The sparsity of the random undersampling determines the acceleration rate.…”

Feasibility of high spatiotemporal resolution for an abbreviated 3D radial breast MRI protocol

“…Our results show preliminary experience with an ultrafast volumetric bilateral breast MRI exam with submillimeter isotropic resolution, surpassing the clinical standard while providing temporal resolutions 6‐to‐15‐fold faster than typical clinical protocols. Although a judicious combination of CS and view sharing can produce high spatiotemporal resolution in various DCE MRI applications, the imaging performance of these methods is object dependent . The local spatially variant temporal constraint is able to further exploit data redundancy by learning and modeling temporal behavior rather than the assumption of consistency over a period of time .…”