Highly Accelerated T1 and T2 Mapping in Water-Unsuppressed MRSI Using Generalized Series Modeling and Deep Learning

Motivation: MRSI and quantitative parametric mapping provide complementary tissue information, but separate acquisitions result in long scan times. Goal(s): To achieve 1-mm whole-brain T1/T2 mapping in water-unsuppressed MRSI for rapid simultaneous metabolic and parametric mapping. Approach: In data acquisition, we further accelerated the SPICE sequence for T1/T2 mapping using shorter TR, extended readout, and sparser sampling. In image reconstruction, we proposed a novel model-based method to incorporate spatiotemporal priors from metabolic imaging and training data for T1/T2 reconstruction from highly sparse data. Results: Phantom and in vivo experiments showed the proposed method was accurate and reproducible. Impact: The proposed method provides a powerful multimodal imaging capability that provides tissue structural and biochemical biomarkers at the same time. This new imaging technology may enable better tissue characterization desired for various research and clinical applications.