A feasibility study of deep learning cardiac cine comparing image quality and volumetry with the conventional ASSET cine

Accelerated cardiac cine was obtained with deep learning reconstructed technique on ten patients (DL Cine). Three series of DL Cine with different parameters were acquired, including reduction factor (RF) of 12 under free breathing (FB), RF of 12 during one breathhold (R12) and RF = 9 dividing the left ventricular short axis into two slabs during each breathhold (R9). The two readers evaluate image quality (IQ) score and measure the cardiac functional parameters and compared them with the conventional ASSET cine. Although the IQ score was smaller than ones of the conventional cine, they are clinically acceptable. We found good correlations between volumetry on the conventional and DL cine.