Quantitative multi-metabolite imaging of Parkinson's disease using AI boosted molecular MRI
This work addresses the need for improved, non-radioactive imaging biomarkers for Parkinson's disease, representing an incremental advance in molecular MRI techniques.
The researchers tackled the problem of non-invasive, quantitative imaging of Parkinson's disease by combining rapid molecular MRI with deep learning reconstruction, achieving multi-metabolite quantification in a mouse model with results generally agreeing with histology and MR spectroscopy.
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.