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Abstract #4627

AI-assisted compressed sensing (ACS) in accelerating knee MR imaging: A pilot study in adults

Xu Xu1, Chunchao Xia2, Wen Zeng2, Wanlin Peng2, Yongming Dai3, Ke Xue3, Zhenlin Li2, Zhenlin Li2, and Zhenlin Li2
1Department of Radiology, west China hospital of Sichuan University, Chengdu, China, 2west China hospital of Sichuan University, Chengdu, China, 3MR Collaboration, Central Research Institute, United Imaging Healthcare, Shanghai, China


In knee MRI, patients with pain or limited motion can have difficult to endure a long-time imaging position. At the same time, the relatively high spatial resolution and good image quality were required in such musculoskeletal MRI. Therefore, shortening scan time and improving the image quality is the aim of current knee MRI.Our study compared conventional parallel imaging (PI) and artificial intelligence assisted compressed sensing (ACS) in terms of imaging quality and diagnostic performance in knee MRI, and achieved a 5-minute comprehensive examination of the knee, without compromising either image quality or visualization of anatomical and pathological structures.

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