Shihan Qiu1,2, Yuhua Chen1,2, Sen Ma1, Zhaoyang Fan1,2, Anthony G. Christodoulou1,2, Yibin Xie1, and Debiao Li1,2
1Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States, 2Department of Bioengineering, UCLA, Los Angeles, CA, United States
1Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States, 2Department of Bioengineering, UCLA, Los Angeles, CA, United States
We developed a deep learning-based method to synthesize conventional weighted images from MR Multitasking spatial factors. It generates higher-fidelity weighted images than a Bloch equation-based approach and a deep learning method using quantitative maps as input.
Figure 1. Preprocessing of spatial factors and the proposed network architecture
Figure 2. An example case of synthetic images using different methods. (a) T1 MPRAGE, (b) T1 GRE, (c) T2 FLAIR.
