Key result
Compressed sensing reconstruction of 4x undersampled tissue phase mapping data accurately measured myocardial velocity in rats, with a median bias of -0.01 cm/s compared to fully sampled data.
Why the study?
Lengthy data acquisition makes TPM MRI prone to errors from physiological variations and reduces throughput, prompting evaluation of functional measures from highly undersampled TPM with compressed sensing reconstruction.
Does compressed sensing reconstruction of highly undersampled TPM data accurately measure myocardial velocity and strain compared to fully sampled data in infarcted and non-infarcted rat hearts?
Does compressed sensing reconstruction of highly undersampled TPM data accurately measure myocardial velocity and strain compared to fully sampled data in infarcted and non-infarcted rat hearts?
Mean Difference: -0.01 (95% CI -0.16–0.2)
Compressed sensing reconstruction allows for highly undersampled TPM data to accurately measure myocardial velocity and strain in vivo in rats, significantly reducing acquisition time.
May reduce TPM scan times in rat models; leaves open clinical translation for velocity assessment.
INTRODUCTION: Tissue 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. METHODS: Three 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. RESULTS: Bland 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. CONCLUSION: Through 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.
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McGinley et al. (2019) studied Myocardial infarction (animal model) (n=17). Compressed sensing reconstruction of undersampled TPM data vs. Fully sampled TPM data was evaluated on Agreement of global radial velocity at mid-ventricular level (4x undersampled vs fully sampled) (Median bias -0.01 cm/s, 95% CI -0.16 to 0.20). Compressed sensing reconstruction of 4x undersampled tissue phase mapping data accurately measured myocardial velocity in rats, with a median bias of -0.01 cm/s compared to fully sampled data.
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