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

TL;DR

This study introduces a rapid, AI-enhanced molecular MRI method for quantitatively imaging multiple metabolites in Parkinson's disease, providing specific biomarkers that align with histological and spectroscopic data.

Contribution

It combines fast MRI acquisition with deep learning reconstruction to enable multi-metabolite quantification in vivo, advancing PD molecular imaging.

Findings

Quantitative maps agree with histology and MR spectroscopy.

Semisolid MT, amide, and aliphatic rNOE proton fractions are potential PD biomarkers.

Method enables rapid, specific, multi-metabolite imaging in a mouse model.

Abstract

Traditional approaches for molecular imaging of Parkinson's disease (PD) in vivo require radioactive isotopes, lengthy scan times, or deliver only low spatial resolution. Recent advances in saturation transfer-based PD magnetic resonance imaging (MRI) have provided biochemical insights, although the image contrast is semi-quantitative and nonspecific. 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 quantitative parameter maps are in general agreement with the histology and MR spectroscopy, and demonstrate that semisolid magnetization transfer (MT), amide, and aliphatic relayed nuclear Overhauser effect (rNOE) proton volume fractions may serve as PD biomarkers.