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Abstract #4354

Accelerated cardiac cine imaging using deep learning: impact on left ventricular function and AI segmentation model

Wenyun Liu1, Chinting Wong2, Cheng Li3, Di Yu1, Yi Zhu4, Ke Jiang4, and Huimao Zhang1
1Radiology, The First Hospital of Jilin University, Changchun, China, 2Nuclear Medicine, The First Hospital of Jilin University, Changchun, China, 3Cardiovascular Center, The First Hospital of Jilin University, Changchun, China, 4Philips Healthcare, Beijing, China

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Myocardium, Cardiomyopathy,Data AnalysisCardiac MRI is considered reference standard for the noninvasive assessment of ventricular volumes and function. However, long multibreath-hold acquisition time can prove difficult in patients and lead to poor image quality. In this study, we investigated the use of a deep learning-based reconstruction algorithm, named Compressed SENSE Artificial Intelligence(CS-AI), to accelerate two-dimensional cine bSSFP for cardiac MRI. The purpose of this study was to compare the image quality and performance of a CS-AI-based cine sequence between reference and accelerated methods: SENSE, Compressed-SENSE, and CS-AI, and then to investigate the impact of images reconstructed by deep learning on AI segmentation model.

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