Multi-Parametric Molecular Imaging of the Brain Using Optimized Multi-TE Subspace MRSI

Objective: To develop a novel multi-TE MR spectroscopic imaging (MRSI) approach to enable label-free, simultaneous, high-resolution mapping of several molecules and their biophysical parameters in the brain. Methods: The proposed method uniquely integrated an augmented molecular-component-specific subspace model for multi-TE $^{1}$ H-MRSI signals, an estimation-theoretic experiment optimization (nonuniform TE selection) for molecule separation and parameter estimation, a physics-driven subspace learning strategy for spatiospectral reconstruction and molecular quantification, and a new accelerated multi-TE MRSI acquisition for generating high-resolution data in clinically relevant times. Numerical studies, phantom and in vivo experiments were conducted to validate the optimized experiment design and demonstrate the imaging capability offered by the proposed method. Results: The proposed TE optimization improved estimation of metabolites, neurotransmitters and their $T_{2}$ 's over conventional TE choices, e.g., reducing variances of neurotransmitter concentration by $\sim 40\%$ and metabolite $T_{2}$ by $\sim 60\%$ . Simultaneous metabolite and neurotransmitter mapping of the brain can be achieved at a nominal resolution of 3.4 $\times 3.4\times$ 6.4 mm $^{3}$ . High-resolution, 3D metabolite $T_{2}$ mapping was made possible for the first time. The translational potential of the proposed method was demonstrated by mapping biochemical abnormality in a post-traumatic epilepsy (PTE) patient. Conclusion: The feasibility for high-resolution mapping of metabolites/neurotransmitters and metabolite $T_{2}$ 's within clinically relevant time was demonstrated. We expect our method to offer richer information for revealing and understanding metabolic alterations in neurological diseases. Significance: A novel multi-TE MRSI approach was presented that enhanced the technological capability of multi-parametric molecular imaging of the brain. The proposed method presents new technology development and application opportunities for providing richer molecular level information to uncover and comprehend metabolic changes relevant in various neurological applications.