Abstract #3462

Deep CNNs with Physical Constraints for simultaneous Multi-tissue Segmentation and Multi-parameter Quantification (MSMQ-Net) of Knee

Xing Lu¹, Yajun Ma¹, Kody Xu¹, Saeed Jerban¹, Hyungseok Jang¹, Chun-Nan Hsu², Amilcare Gentili^1,3, Eric Y Chang^1,3, and Jiang Du¹

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

Synopsis

In this study, we proposed end-to-end deep learning convolutional neural networks to perform simultaneous multi-tissue segmentation and multi-parameter quantification (MSMQ-Net) on the knee without and with physical constraints. The performance robustness of MSMQ-Net was also evaluated using reduced input magnetic resonance images. Results demonstrated the potential of MSMQ-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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