Keywords: Image Reconstruction, Molecular Imaging
Motivation: Heteronuclear magnetic resonance spectroscopic imaging (MRSI) can assess tumor aggressiveness and response to treatments. Regarding its slow acquisition and starving signal-to-noise ratio, efficient deep-learning reconstruction adaptive for different applications is required.
Goal(s): To propose an adaptive deep learning method for reconstructing high-quality MRSI.
Approach: A deep learning prior was trained using singular maps extracted from hyperpolarized 13C MRSI and deuterium metabolic imaging (DMI), generated through multi-pool exchange and free induction decay. The prior was incorporated with SPICE and date fidelity terms for MRSI reconstruction.
Results: The model was evaluated on various datasets, demonstrating its generalizability in reconstructing high-quality MRSI using high acceleration rates.
Impact: The generalizability of the proposed pipeline for high-quality MRSI reconstruction has been demonstrated in various applications, including HP 13C MRSI and DMI, suggesting its feasibility as a molecular imaging tool for both scientific and clinical applications.
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