4 papers
A Proof-of-Concept Study of Multitask Learning for Cranial Synthetic CT Generation Across Heterogeneous MRI Field Strengths
Zhuoyao Xin, Yiren Zhang, Christopher Wu +6
Accurate synthesis of computed tomography (CT) images from magnetic resonance imaging (MRI) is clinically valuable for cranial applications such as attenuation correction, radiothe…
Synthetic Data in MR Spectroscopy: Current Practices, Applications, and Considerations
John T. LaMaster, Aaron T. Gudmundson, Alireza Abaei +75
The use of synthetic data has emerged as an essential tool in Magnetic Resonance Spectroscopy (MRS) research and applications, providing advantages for optimization of acquisition,…
Q-MRS: A Deep Learning Framework for Quantitative Magnetic Resonance Spectra Analysis
Christopher J. Wu, Lawrence S. Kegeles, Jia Guo
Magnetic resonance spectroscopy (MRS) is an established technique for studying tissue metabolism, particularly in central nervous system disorders. While powerful and versatile, MR…
Enhancing CT Image synthesis from multi-modal MRI data based on a multi-task neural network framework
Zhuoyao Xin, Christopher Wu, Dong Liu +3
Image segmentation, real-value prediction, and cross-modal translation are critical challenges in medical imaging. In this study, we propose a versatile multi-task neural network f…