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Abstract #0748

Deep Learning Super-Resolution reconstruction for fast cardiac MRI protocol:A Comparative Study with Conventional cardiac MR

Yiying Hua1, Hongfei Lu1, Xiuzheng Yue2, Fan Du1, Nan Zhang1, Hang Jin1, and Mengsu Zeng1
1Zhongshan hospital of Fudan University, Shanghai, China, 2Philips Healthcare, Beijing, China

Synopsis

Keywords: Diagnosis/Prediction, Heart

Motivation: Deep Learning (DL) Super-Resolution reconstruction methods combined with Compressed SENSE for cardiac MRI(CMR) have not been well studied. There is a need to reduce scan times without compromising image quality.

Goal(s): To evaluate a DL Compressed SENSE artificial intelligence (CSAI) in reconstructing common CMR sequences and compare its performance with standard sequences.

Approach: 100 patients were prospectively recruited for conventional SENSE CMR sequences and CSAI-accelerated CMR sequences between March and August 2024. Two readers assessed image quality qualitatively and quantitatively. Quantitative measurements of biventricular function, myocardial edema, and fibrosis were obtained.

Results: CSAI-CMR reduced acquisition time by 57.4% while significantly enhancing image quality.

Impact: This study demonstrates that CSAI-CMR improves image quality and significantly reduces scan time, enhancing patient comfort and clinical efficiency, it supports advancing cardiac MRI toward more precise, efficient, and patient-friendly practices, potentially increasing its clinical adoption.

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