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Abstract #5141

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

Shigeo Okuda1, Ryo Tsukada2, Manabu Arai1, Sari Motomatsu2, Atsushi Nozaki3, Xucheng Zhu4, and Masahiro Jinzaki1
1Radiology, Keio University School of Medicine, Tokyo, Japan, 2Keio University Hospital, Tokyo, Japan, 3GE Healthcare Japan, Tokyo, Japan, 4GE Healthcare, Menlo Park, CA, United States

Synopsis

Keywords: Heart, Cardiovascular, Accelerated cineAccelerated 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.

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Keywords