Accelerated magnetic resonance imaging tissue phase mapping of the rat myocardium using compressed sensing with iterative soft-thresholding.

introductionTissue Phase Mapping (TPM) MRI can accurately measure regional myocardial velocities and strain. The lengthy data acquisition, however, renders TPM prone to errors due to variations in physiological parameters, and reduces data yield and experimental throughput. The purpose of the present study is to examine the quality of functional measures (velocity and strain) obtained by highly undersampled TPM data using compressed sensing reconstruction in infarcted and non-infarcted rat hearts.

methodsThree fully sampled left-ventricular short-axis TPM slices were acquired from 5 non-infarcted rat hearts and 12 infarcted rat hearts in vivo. The datasets were used to generate retrospectively (simulated) undersampled TPM datasets, with undersampling factors of 2, 4, 8 and 16. Myocardial velocities and circumferential strain were calculated from all datasets. The error introduced from undersampling was then measured and compared to the fully sampled data in order to validate the method. Finally, prospectively undersampled data were acquired and compared to the fully sampled datasets.

resultsBland Altman analysis of the retrospectively undersampled and fully sampled data revealed narrow limits of agreement and little bias (global radial velocity: median bias = -0.01 cm/s, 95% limits of agreement = [-0.16, 0.20] cm/s, global circumferential strain: median bias = -0.01%strain, 95% limits of agreement = [-0.43, 0.51] %strain, all for 4x undersampled data at the mid-ventricular level). The prospectively undersampled TPM datasets successfully demonstrated the feasibility of method implementation.

conclusionThrough compressed sensing reconstruction, highly undersampled TPM data can be used to accurately measure the velocity and strain of the infarcted and non-infarcted rat myocardium in vivo, thereby increasing experimental throughput and simultaneously reducing error introduced by physiological variations over time.