Synthesize High-Quality Multi-Contrast Magnetic Resonance Imaging From Multi-Echo Acquisition Using Multi-Task Deep Generative Model

• Published in: IEEE transactions on medical imaging Vol. 39; no. 10; pp. 3089 - 3099
• Main Authors: Wang, Guanhua, Gong, Enhao, Banerjee, Suchandrima, Martin, Dann, Tong, Elizabeth, Choi, Jay, Chen, Huijun, Wintermark, Max, Pauly, John M., Zaharchuk, Greg
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Deep learning; deep learning (DL); generative adversarial network (GAN); Generative adversarial networks; Generators; Image contrast; image fusion; Image quality; Image reconstruction; image synthesis; Machine learning; Magnetic resonance imaging; Magnetic resonance imaging (MRI); Medical imaging; Neuroimaging; Pathology; Resonance; Spatial data; Synthesis; Training
• ISSN: 0278-0062; 1558-254X; 1558-254X
• DOI: 10.1109/TMI.2020.2987026

Multi-echo saturation recovery sequence can provide redundant information to synthesize multi-contrast magnetic resonance imaging. Traditional synthesis methods, such as GE's MAGiC platform, employ a model-fitting approach to generate parameter-weighted contrasts. However, models' over-simplification, as well as imperfections in the acquisition, can lead to undesirable reconstruction artifacts, especially in T2-FLAIR contrast. To improve the image quality, in this study, a multi-task deep learning model is developed to synthesize multi-contrast neuroimaging jointly using both signal relaxation relationships and spatial information. Compared with previous deep learning-based synthesis, the correlation between different destination contrast is utilized to enhance reconstruction quality. To improve model generalizability and evaluate clinical significance, the proposed model was trained and tested on a large multi-center dataset, including healthy subjects and patients with pathology. Results from both quantitative comparison and clinical reader study demonstrate that the multi-task formulation leads to more efficient and accurate contrast synthesis than previous methods.