Quantitative multi-metabolite imaging of Parkinson's disease using AI boosted molecular MRI
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 5 of its 109 references that are in the map. First posted on arXiv 15 Jul 2025.
Builds on
5 of 109 references.
References · 5
- 2017MR fingerprinting Deep RecOnstruction NEtwork (DRONE)cited by 28
- 2021CEST MR fingerprinting (CEST-MRF) for Brain Tumor Quantification Using EPI Readout and Deep Learning Reconstructioncited by 4
- 2022Accelerated and Quantitative 3D Semisolid MT/CEST Imaging using a Generative Adversarial Network (GAN-CEST)cited by 4
- 2023Dynamic and Rapid Deep Synthesis of Molecular MRI Signalscited by 4
- 2024Multi-Parameter Molecular MRI Quantification using Physics-Informed Self-Supervised Learningcited by 3
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Citing papers per year: 2026: 1.
1 paper.