Toward an early diagnostic marker of Parkinson’s: measuring iron in dopaminergic neurons with MR relaxometry

Brammerloh, Malte; Morawski, Markus; Weigelt, Isabel; Reinert, Tilo; Lange, Charlotte de; Pelicon, Primož; Vavpetič, Primož; Jánkuhn, Steffen; Jäger, Carsten; Alkemade, Anneke; Balesar, Rawien; Pine, Kerrin; Gavriilidis, Filippos; Trampel, Robert; Reimer, Enrico; Arendt, Thomas; Weiskopf, Nikolaus; Kirilina, Evgeniya

doi:10.1101/2020.07.01.170563

Cited by 3 publications

(2 citation statements)

References 80 publications

(117 reference statements)

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“…However, additional contrast sources such as chemical exchange may contribute to frequency shift and/or R 2’ , although the effect size is smaller than that of susceptibility 52,53 . Additionally, the static dephasing condition may or may not hold for biological tissues and is affected by water diffusivity, and the size, geometry, and characteristic frequency of susceptibility sources 12,54 , influencing the relaxometric constant 55 . If the static dephasing condition breaks, R2 and R2* measurements can be changed by sequence parameters such as the echo space in spin echo 56,57 , affecting χ -separation results.…”

Section: Discussionmentioning

confidence: 99%

“…Additionally, the validity of the static dephasing condition is influenced by water diffusivity, and the size, geometry, and characteristic frequency of susceptibility sources (Brammerloh et al, 2020;Duyn and Schenck, 2017) and may not strictly hold for brain regions with high iron concentrations (e.g., GP, RN, and SN in Fig. 6; (Yablonskiy et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

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χ-separation: Magnetic susceptibility source separation toward iron and myelin mapping in the brain

Shin

Lee²,

Yun

et al. 2020

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Obtaining a histological fingerprint from the in-vivo brain has been a long-standing target of magnetic resonance imaging (MRI). In particular, non-invasive imaging of iron and myelin, which are involved in normal brain functions and are histopathological hallmarks in a few neurodegenerative diseases, has practical utilities in neuroscience and medicine. Here, we propose a biophysical model that describes the individual contribution of iron and myelin to MRI signals via their difference in magnetic susceptibility (i.e., paramagnetic iron vs. diamagnetic myelin). Using this model, we develop a method, χ-separation, that generates the voxel-wise distributions of iron and myelin. The method is validated using computer simulation and phantom experiments, and applied to ex-vivo and in-vivo brains. The results delineate the well-known histological features of iron and myelin in the specimen (e.g., co-localization of iron and myelin in Gennari line), healthy volunteers (e.g., myelin-lacking and iron-rich pulvinar), and multiple sclerosis patients (e.g., demyelinated iron-rim lesion). This new in-vivo histology technology, taking less than 20 min, may serve as a practical tool for exploring the microstructural information of the brain.

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