Feasibility of multiple-view myocardial perfusion MRI using radial simultaneous multi-slice acquisitions

Abstract

PurposeDynamic contrast enhanced MRI of the heart typically acquires 2–4 short-axis (SA) slices to detect and characterize coronary artery disease. This acquisition scheme is limited by incomplete coverage of the left ventricle. We studied the feasibility of using radial simultaneous multi-slice (SMS) technique to achieve SA, 2-chamber and/or 4-chamber long-axis (2CH LA and/or 4CH LA) coverage with and without electrocardiography (ECG) gating using a motion-robust reconstruction framework.Methods12 subjects were scanned at rest and/or stress, free breathing, with or without ECG gating. Multiple sets of radial SMS k-space were acquired within each cardiac cycle, and each SMS set sampled 3 parallel slices that were either SA, 2CH LA, or 4CH LA slices. The radial data was interpolated onto Cartesian space using an SMS GRAPPA operator gridding method. Self-gating and respiratory states binning of the data were done. The binning information as well as a pixel tracking spatiotemporal constrained reconstruction method were applied to obtain motion-robust image reconstructions. Reconstructions with and without the pixel tracking method were compared for signal-to-noise ratio and contrast-to-noise ratio.ResultsFull coverage of the heart (at least 3 SA and 3 LA slices) during the first pass of contrast at every heartbeat was achieved by using the radial SMS acquisition. The proposed pixel tracking reconstruction improves the average SNR and CNR by 21% and 30% respectively, and reduces temporal blurring for both gated and ungated acquisitions.ConclusionAcquiring simultaneous multi-slice SA, 2CH LA and/or 4CH LA myocardial perfusion images in every heartbeat is feasible in both gated and ungated acquisitions. This can add confidence when detecting and characterizing coronary artery disease by revealing ischemia in different views, and by providing apical coverage that is improved relative to SA slices alone. The proposed pixel tracking framework improves the reconstruction while adding little computational cost.

Whole‐heart, ungated, free‐breathing, cardiac‐phase‐resolved myocardial perfusion MRI by using Continuous Radial Interleaved simultaneous Multi‐slice acquisitions at sPoiled steady‐state (CRIMP)

“…Radial k-space data were gridded by SMS-GROG. 7 A temporally constrained reconstruction was performed on each slice group separately to obtain preliminary reconstructed images for all slices. The different colors (blue, green, and orange) represent k-space data from different slice groups…”

“…Ungated methods can overcome the potentially inadequate ECG signal and cover multiple cardiac phases. 7,[13][14][15][16][17][18][19] Interframe cardiac motion is captured rather than restrained during these acquisitions; however, image reconstruction and postprocessing are more involved. In previous studies, 13,15,17 retrospective self-gating was done by using a signal obtained from the left ventricle, and model-based image registration 13,15,20 or breath-holding 17 was used to suppress respiratory motion.…”

“…Another sequence used in this study was an ungated radial SMS-SR developed previously. 6,7 This sequence was used to acquire perfusion images for low-dose gadolinium injections for AIF estimation. SMS-SR was also used for normal-dose injections to compare the CRIMP with SMS-SR, as described in the in-vivo experiments and imaging protocol section .…”

Non-iterative image reconstruction from sparse magnetic resonance imaging radial data without priors

“…The compressed sensing approaches can be considered as Bayesian methods, in which the prior information is formulated as the constraints. It is a popular approach that the non-Cartesian kspace measurements are interpolated into the Cartesian grid before reconstruction [9][10][11].…”

Iterative versus non-iterative image reconstruction methods for sparse magnetic resonance imaging

“…If the image is piecewise constant, the total variation (TV) norm constraint can be used. In MRI, if the k-space is scanned using a non-Cartesian sampling scheme, it is popular to first interpolate the non-Cartesian k-space measurements into the Cartesian grid before reconstruction [9][10][11].…”