Meeting Banner
Abstract #2429

Direct Synthesis of Multi-Contrast Images from MR Multitasking Spatial Factors Using Deep Learning

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

MR Multitasking is an efficient approach for quantification of multiple parametric maps in a single scan. The Bloch equations can be used to derive conventional contrast-weighted images, which are still preferred by clinicians for diagnosis, from quantitative maps. However, due to imperfect modeling and acquisition, these synthetic images often exhibit artifacts. In this study, we developed a deep learning-based method to synthesize contrast-weighted images from Multitasking spatial factors without explicit Bloch modeling. We demonstrated that our method provided synthetic images with higher quality and fidelity than the model-based approach or a similar deep learning method using quantitative maps as input.

This abstract and the presentation materials are available to members only; a login is required.

Join Here