Quantitative multi-metabolite imaging of Parkinson's disease using AI boosted molecular MRI.

Parkinson's disease (PD) diagnosis remains a substantial clinical challenge due to its heterogeneous symptomatology and the absence of reliable early-stage biomarkers. While molecular imaging offers promise, current methods are lengthy or have limited specificity. Here, we combined a rapid molecular MRI acquisition paradigm with deep learning based reconstruction for multi-metabolite quantification of glutamate, mobile proteins, semisolid, and mobile macromolecules in an acute MPTP (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine) mouse model. The resulting quantitative parameter maps align well with histology and magnetic resonance spectroscopy (MRS) findings. Notably, the semisolid magnetization transfer (MT), amide, and aliphatic relayed nuclear Overhauser effect (rNOE) proton volume fractions emerged as promising PD biomarkers.