Accelerated T2 Mapping with GS-Assisted Deep Translation of T1W Image Prior

Deep learning (DL)-based methods have shown great potential for accelerating T2W imaging by using image prior generated from a companion T1W image. However, quantitative T2 mapping requires multiple T2W images acquired with multi-TE, creating practical problem for the use of DL for accelerated T2 mapping due to insufficient multi-TE training data. This work addresses this problem by using a generalized series model to map a T2W DL prior for one TE to multiple TEs, enabling effective use of T1W image prior for high-quality T2 mapping from sparsely sampled data. The method has been validated using experimental data, producing impressive results.