Dynamic deuterium MR spectroscopic imaging (2H-MRSI) is emerging as a powerful tool for measurement of metabolic changes using deuterated substrates. In this work, we propose a novel method to reconstruct the often extremely noisy dynamic 2H-MRSI data, incorporating both physics-based subspace spectral model and deep learning-based data priors via an information-theoretical framework. The proposed method has been validated using both simulated and experimental data, showing a significant improvement over the conventional reconstruction and processing method.
This abstract and the presentation materials are available to members only; a login is required.