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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