Xueqian Xie
Papers
2
Total Citations
26
H-Index
2
About
Xueqian Xie is a pioneering researcher in cardiovascular imaging, with a focus on motion correction and artificial intelligence (AI) applications in coronary artery disease assessment. His work centers on using convolutional neural networks (CNNs) to address motion artifacts in coronary calcium scoring—a critical challenge in cardiac CT imaging. In his most-cited study (22 citations), Xie developed a CNN-based motion-correction algorithm for coronary calcium scores, validated through robotic simulations that mimic realistic cardiac and respiratory motion. This work demonstrated that AI can significantly reduce motion-induced errors, improving the accuracy of cardiovascular risk stratification. His subsequent research (4 citations) further classified moving calcified plaques based on motion artifact patterns, identifying key factors influencing CNN performance, such as plaque density and motion amplitude. Xie’s contributions bridge the gap between deep learning and clinical radiology, offering practical solutions for motion-contaminated cardiac images. His robotic simulation methodology is particularly notable, providing a reproducible framework for testing AI models under controlled conditions. With his innovative approach, Xie is advancing the reliability of non-invasive coronary calcium scoring, directly impacting early detection and management of heart disease.
Research Focus
Key Achievements
Top Papers
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