Abstract #2154

Free-breathing Multi-Phase MRI using Deep Learning-based Respiratory Motion Compensation

Vahid K Ghodrati^1,2, Jiaxin Shao¹, Mark Bydder¹, Kim-Lien Nguyen^3,4, Xiaodong Zhong⁵, Yingli Yang⁶, and Peng Hu^1,2

¹Radiology, University of California Los Angeles, Los Angeles, CA, United States, ²Biomedical Physics Inter-Departmental Graduate Program, University of California Los Angeles, Los Angeles, CA, United States, ³Department of Medicine, Division of Cardiolog, University of California Los Angeles, Los Angeles, CA, United States, ⁴Division of Cardiology, Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA, United States, ⁵siemens, Los Angeles, CA, United States, ⁶Department of Oncology, University of California Los Angeles, Los Angeles, CA, United States

To minimize respiratory motion-induced image blurring and artifacts, conventional cardiothoracic and abdominal MRI techniques rely mostly on breath-holding. These approaches result in limited time window for data acquisition, especially in many ill patients who are unable to breath-hold for an extended period of time. In this study, we employed deep learning as a promising tool for detection and correction of complex respiratory motion during free-breathing MRI scanning. On average, our proposed network increased the sharpness of the images 20 percent.

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