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

Author:Shigeo Okuda Ryo Tsukada Manabu Arai Sari Motomatsu Atsushi Nozaki Xucheng Zhu Masahiro Jinzaki

Session Type:Digital Poster

Session Date:Thursday, 08 June 2023

Session Name:Technical Solutions in Cardiovascular Imaging & Image Processing II

Program Number:5141

Room Session:Exhibition Halls D/E

Institution :GE Healthcare GE Healthcare Japan Keio University Hospital Keio University School of Medicine

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