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Abstract #0299

Rapid High-Value Diagnostic and Quantitative Knee MRI: A Prospective Artificial Intelligence Study

Akshay S Chaudhari1, Murray Grissom2, Zhongnan Fang3, Bragi Sveinsson4, Jin Hyung Lee1, Garry E Gold1, Brian A Hargreaves1, and Kathryn J Stevens1
1Stanford University, Stanford, CA, United States, 2Santa Clara Valley Medical Center, San Jose, CA, United States, 3LVIS Corporation, Palo Alto, CA, United States, 4Harvard Medical School, Boston, MA, United States

Knee MRI protocols usually require 20+ minutes of scan time, leading to great interest in expedited and high-value imaging examinations. Moreover, despite the popularity of quantitative imaging for osteoarthritis, it is not routinely implemented clinically. In this study, we use a 5-minute quantitative double-echo steady-state (qDESS) sequence that produces simultaneous morphological images and T2 relaxation time measurements. We prospectively enhance the slice-resolution of qDESS using deep learning. We show that qDESS provided high diagnostic accuracy compared to both diagnostic knee MRI and surgical findings. Additionally, automatic T2 maps increased reader diagnostic confidence and sensitivity to cartilage lesions.

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