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<p>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.</p>

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In-vivo iron mapping in patients with Parkinson’s disease using deep learning-based susceptibility source separation MRI

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月27日

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  • Hyeong-Geol Shin,
  • Kelly A. Mills,
  • Ted M. Dawson,
  • Taechang Kim,
  • Jongho Lee,
  • Xu Li,
  • Peter van Zijl,
  • Jannik Prasuhn

摘要

Parkinson’s disease (PD) involves pathological iron accumulation, yet MRI metrics, such as R2* 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-separationDL) separated the paramagnetic susceptibility component (χpara; indicative of iron) from χ, revealing alterations undetected by established susceptibility-based methods: χpara increased in dorsal premotor cortex ( +6.3%, P = 0.032) and substantia nigra pars compacta ( +10.2%, P = 0.024), χpara 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.