Abstract #4051

Deep CNNs with Physical Constraints for simultaneous Multi-tissue Segmentation and Quantification (MSQ-Net) of Knee from UTE MRIs

Xing Lu¹, Yajun Ma¹, Saeed Jerban¹, Hyungseok Jang¹, Yanping Xue¹, Xiaodong Zhang¹, Mei Wu¹, Amilcare Gentili^1,2, Chun-nan Hsu³, Eric Y Chang^1,2, and Jiang Du¹

¹Department of Radiology, University of California, San Diego, San Diego, CA, United States, ²Radiology Service, Veterans Affairs San Diego Healthcare System, San Diego, CA, United States, ³Department of Neurosciences, University of California, San Diego, San Diego, CA, United States

In this study, we proposed end-to-end deep learning convolutional neural networks to perform simultaneous segmentation and quantification (MSQ-Net) on the knee without and with physical constraint networks (pcMSQ-Net). Both networks were trained and tested for the feasibility of simultaneous segmentation and quantitative evaluation of multiple knee joint tissues from 3D ultrashort echo time (UTE) magnetic resonance imaging. Results demonstrated the potential of MSQ-Net and pcMSQ-Net for fast and accurate UTE-MRI analysis of the knee, a “whole-organ” approach which is impossible with conventional clinical MRI.

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