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Abstract #4686

Knee Osteoarthritis DTI using Deep Learning Reconstruction: A Prospective Study on Quantitative Accuracy and Diagnostic Efficacy

Xiaxia Wu1, Weiyin Vivian Liu2, and Yunfei Zha1
1Renmin Hospital of Wuhan University, Wuhan, China, 2GE Healthcare, Beijing, China

Synopsis

Keywords: Cartilage, MSK

Motivation: Diffusion-tensorimaging(DTI) has the potential to serve as a marker of joint degeneration and can elucidate the integrity of articular cartilage with good reproducibility.

Goal(s): The use of DTI as a marker in the clinical setting is challenging due to the difficulty in balancing signal-to-noise ratio, resolution, and acceptable scan time.

Approach: A deep learning reconstruction (DLR) technique breaks the tradeoffs between signal-to-noise ratio, spatial resolution, and scan time.

Results: High-resolution and fast DTI was potentially achieved to identify cartilage injury or degeneration has good test-retest reproducibility, and may be accurate in discriminating healthy subjects from subjects with OA.

Impact: The application of deep learning reconstruction technology to select the appropriate acceleration factor can significantly shorten the acquisition time of DTI sequence images of the knee joint with good test-retest repeatability.

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