Deep Convolutional Neural Network for Dedicated Regions-of-Interest Based Multi-Parameter Quantitative Ultrashort Echo Time (UTE) Magnetic Resonance Imaging of the Knee Joint.

We proposed an end-to-end deep learning convolutional neural network (DCNN) for region-of-interest based multi-parameter quantification (RMQ-Net) to accelerate quantitative ultrashort echo time (UTE) MRI of the knee joint with automatic multi-tissue segmentation and relaxometry mapping. The study in...

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Bibliographic Details

Published in:	Journal of Digital Imaging Vol. 37; no. 5; pp. 2126 - 2135
Main Authors:	Lu, Xing, Ma, Yajun, Chang, Eric Y., Athertya, Jiyo, Jang, Hyungseok, Jerban, Saeed, Covey, Dana C., Bukata, Susan, Chung, Christine B., Du, Jiang
Format:	diagnostic images pictorial research tables/charts Journal Article
Published:	Springer Nature Oct2024
Online Access:	View this record in EBSCOhost