In-vivo iron mapping in patients with Parkinson's disease using deep learning-based susceptibility source separation MRI.
Authors
Affiliations (12)
Affiliations (12)
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA.
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Neuroregeneration and Stem Cell Programs, Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Physiology, Pharmacology and Therapeutics, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea.
- Division of MR Research, Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA. [email protected].
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA. [email protected].
- Section of Movement Disorders, Department of Neurology, University Medical Center Schleswig-Holstein, Campus Lübeck, Lübeck, Germany. [email protected].
- Institute of Neurogenetics, University of Lübeck, Lübeck, Germany. [email protected].
- Center for Brain, Behavior and Metabolism, University of Lübeck, Lübeck, Germany. [email protected].
Abstract
Parkinson's disease (PD) involves pathological iron accumulation, yet MRI metrics, such as R<sub>2</sub>* or magnetic susceptibility (χ), lack mechanistic specificity because they convolve paramagnetic and diamagnetic sources. We applied an AI-assisted χ-separation framework that combines deep learning (DL)-based preprocessing with biophysical modeling to assess paramagnetic iron with enhanced specificity. Twenty-five PD patients and twenty-six matched controls underwent 3 T multi-parametric MRI. DL-based χ-separation (χ-separation<sub>DL</sub>) separated the paramagnetic susceptibility component (χ<sub>para</sub>; indicative of iron) from χ, revealing alterations undetected by established susceptibility-based methods: χ<sub>para</sub> increased in dorsal premotor cortex ( +6.3%, P = 0.032) and substantia nigra pars compacta ( +10.2%, P = 0.024), χ<sub>para</sub> in premotor cortex correlated with disease duration (r = 0.41; P = 0.045). DL-based preprocessing was not inferior for the differentiation between PD patients vs. controls compared to established optimization-based χ-separation, indicating the potential for AI-enhanced χ-separation to be applied within the scope of susceptibility imaging in PD.