Accelerated cardiac cine magnetic resonance imaging using deep low-rank plus sparse network: validation in patients.

Abstract

Accurate and reproducible assessment of left ventricular (LV) volumes is important in managing various cardiac conditions. However, patients are required to hold their breath multiple times during data acquisition, which may result in discomfort and restrict cardiac motion, potentially compromising the accuracy of the detected results. Accelerated imaging techniques can help reduce the number of breath holds needed, potentially improving patient comfort and the reliability of the LV assessment. This study aimed to prospectively evaluate the feasibility and accuracy of LV assessment with a model-based low-rank plus sparse network (L+S-Net) for accelerated magnetic resonance (MR) cine imaging. Fourty-one patients with different cardiac conditions were recruited in this study. Both accelerated MR cine imaging with L+S-Net and traditional electrocardiogram (ECG)-gated segmented cine were performed for each patient. Subjective image quality (IQ) score and quantitative LV volume function parameters were measured and compared between L+S-Net and traditional standards. The IQ score and LV volume measurements of cardiovascular magnetic resonance (CMR) images reconstructed by L+S-Net and standard cine were compared by paired t-test. The acquisition time of the two methods was also calculated. In a quantitative analysis, L+S-Net and standard cine yielded similar measurements for all parameters of LV function (ejection fraction: 35±22 for standard vs. 33±23 for L+S-Net), although L+S-Net had slightly lower IQ scores than standard cine CMR (4.2±0.5 for L+S-Net vs. 4.8±0.4 for standard cine; P<0.001). The mean acquisition time of L+S-Net and standard cine was 0.83±0.08 vs. 6.35±0.78 s per slice (P<0.001). Assessment of LV function with L+S-Net at 3.0 T yields comparable results to the reference standard, albeit with a reduced acquisition time. This feature enhances the clinical applicability of the L+S-Net approach, helping alleviate patient discomfort and motion artifacts that may arise due to prolonged acquisition time.