A human brain atlas of χ-separation (chi-separation) for normative iron and myelin distributions
Poster No:
2207
Submission Type:
Abstract Submission
Authors:
Kyeongseon Min1, Beomseok Sohn2, Woo Jung Kim3, Chae Jung Park3, Soohwa Song4, Dong Hoon Shin4, Kyung Won Chang5, Na-Young Shin5, Minjun Kim1, Hyeong-Geol Shin6, Phil Hyu Lee5, Jongho Lee1
Institutions:
1Seoul National University, Seoul, Korea, Republic of, 2Samsung Medical Center, Seoul, Korea, Republic of, 3Yongin Severance Hospital, Yongin, Korea, Republic of, 4Heuron Co., Ltd, Seoul, Korea, Republic of, 5Severance Hospital, Seoul, Korea, Republic of, 6Johns Hopkins University School of Medicine, Baltimore, MD
First Author:
Co-Author(s):
Introduction:
Alterations in iron and myelin distribution in the human brain are associated with neurogenerative diseases such as Alzheimer's disease, Parkinson's disease, and multiple sclerosis (Möller, 2019). The imaging of paramagnetic iron and diamagnetic myelin can be achieved with magnetic resonance imaging (MRI) by quantitative susceptibility mapping (QSM) (de Rochefort, 2008). However, when iron and myelin are co-localized in a voxel, their contributions to QSM contrast are hardly separable. This limitation can be resolved with a susceptibility source separation method, χ-separation (chi-separation) (Shin, 2021), which utilizes phase and R2' (or R2*) to create para- and diamagnetic susceptibility (χpara and χdia) maps. In this study, we created a brain atlas of χ-separation from 106 healthy human brains as a reference for utilizing χ-separation in the neuroimaging field (https://github.com/SNU-LIST/chi-separation-atlas).
Methods:
106 healthy human volunteers (27-85 years old) were recruited from two hospitals and scanned using 3 T MRI (Ingenia CX or Ingenia Elition X) with multi-echo gradient-echo and magnetization-prepared rapid gradient-echo (MPRAGE) sequences.
Fig. 1 shows the workflow for atlas construction. The phases of multi-echo gradient-echo images were combined (Wu, 2012) and unwrapped (Schofield, 2003). The background field was removed to generate a tissue field map. A R2* map was generated from the magnitudes of multi-echo gradient-echo images. χpara and χdia maps were acquired with χ-sepnet (Kim, 2022) utilizing the tissue field and R2* maps as inputs. A QSM map was calculated by summing the χpara and χdia maps. The QSM map and T1-weighted image were linearly combined to generate a hybrid image, which was nonlinearly registered (Avants, 2008) to the hybrid image atlas from MuSus-100 (He, 2023) in the MNI space. Using the resulting deformation field, the χpara and χdia maps were registered to the MNI space. These maps were averaged across subjects to create a χ-separation atlas. The inter-subject variability of the atlas was evaluated by a relative standard deviation (rSD) map, which is standard deviation divided by mean.
To conduct regions of interest (ROI)-based analysis on the χ-separation atlas, eight subcortical nuclei and three thalamic nuclei labels from MuSus-100 (He, 2023), twenty-eight white matter labels from ICBM-DTI-81 (Oishi, 2008), and a whole white matter ROI generated via intensity-based segmentation (Avants, 2011) were employed. The median χpara, χdia, and QSM in ROIs were averaged across subjects and the population means and standard deviations were reported.
Fig. 1 shows the workflow for atlas construction. The phases of multi-echo gradient-echo images were combined (Wu, 2012) and unwrapped (Schofield, 2003). The background field was removed to generate a tissue field map. A R2* map was generated from the magnitudes of multi-echo gradient-echo images. χpara and χdia maps were acquired with χ-sepnet (Kim, 2022) utilizing the tissue field and R2* maps as inputs. A QSM map was calculated by summing the χpara and χdia maps. The QSM map and T1-weighted image were linearly combined to generate a hybrid image, which was nonlinearly registered (Avants, 2008) to the hybrid image atlas from MuSus-100 (He, 2023) in the MNI space. Using the resulting deformation field, the χpara and χdia maps were registered to the MNI space. These maps were averaged across subjects to create a χ-separation atlas. The inter-subject variability of the atlas was evaluated by a relative standard deviation (rSD) map, which is standard deviation divided by mean.
To conduct regions of interest (ROI)-based analysis on the χ-separation atlas, eight subcortical nuclei and three thalamic nuclei labels from MuSus-100 (He, 2023), twenty-eight white matter labels from ICBM-DTI-81 (Oishi, 2008), and a whole white matter ROI generated via intensity-based segmentation (Avants, 2011) were employed. The median χpara, χdia, and QSM in ROIs were averaged across subjects and the population means and standard deviations were reported.
·Schematic flowchart illustrating the processing pipeline for generating the χpara and χdia atlases. The hybrid images were created by a linear combination of the QSM and T1-weighted images.
Results:
In Fig. 2, representative slices of χpara atlas exhibits high values in iron-rich nuclei in basal ganglia, thalamus, and midbrain, while white matter fibers such as corpus callosum are clearly depicted as high |χdia| value. The slices provide a comprehensive view in our χ-separation atlas, visualizing anatomical structures associated with iron and myelin distributions.
When the normative profile of χpara and χdia across subcortical nuclei, thalamus, and white matter were examined, high χpara values (45–145 ppb) are observed in subcortical nuclei and pulvinar, while white matter shows χpara of 10–30 ppb. Contrarily, white matter shows high |χdia| of 25–50 ppb, while it is 10–25 ppb in subcortical and thalamic nuclei. In thalamus, nearly same levels of χpara and |χdia| are observed in lateral thalamic nuclei.
When the normative profile of χpara and χdia across subcortical nuclei, thalamus, and white matter were examined, high χpara values (45–145 ppb) are observed in subcortical nuclei and pulvinar, while white matter shows χpara of 10–30 ppb. Contrarily, white matter shows high |χdia| of 25–50 ppb, while it is 10–25 ppb in subcortical and thalamic nuclei. In thalamus, nearly same levels of χpara and |χdia| are observed in lateral thalamic nuclei.
·Ten axial slices of the χpara and χdia atlases. Anatomical structures are marked with red dotted lines.
Conclusions:
The χ-separation atlas demonstrates exquisite details of anatomical structures associated with iron and myelin distribution. Moreover, it provides normative ranges of χpara and χdia in the brain across subcortical nuclei, thalamic nuclei, and white matter fibers. Beyond its application in research, our atlas may be utilized in treatments targeting deep brain structures such as deep brain stimulation or high-power focused ultrasound.
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Anatomy and Functional Systems
Subcortical Structures
White Matter Anatomy, Fiber Pathways and Connectivity 2
Neuroinformatics and Data Sharing:
Brain Atlases 1
Keywords:
Atlasing
Basal Ganglia
MRI
Myelin
NORMAL HUMAN
Open Data
Spatial Normalization
Thalamus
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
1|2Indicates the priority used for review
Provide references using author date format
Avants, B.B. (2011). "An open source multivariate framework for n-tissue segmentation with evaluation on public data." Neuroinformatics 9: 381-400.
de Rochefort, L.R. (2008). "Quantitative MR susceptibility mapping using piece-wise constant regularized inversion of the magnetic field." Magnetic Resonance in Medicine 60: 1003-1009.
He, C. (2023). "Quantitative susceptibility atlas construction in Montreal Neurological Institute space: towards histological-consistent iron-rich deep brain nucleus subregion identification." Brain Structure and Function 228: 1045-1067.
Kim, M. (2022). "Chi-sepnet: Susceptibility source separation using deep neural network." Proceedings of International Society of Magnetic Resonance in Medicine 30: 2464.
Möller, H.E. (2019). "Iron, myelin, and the brain: Neuroimaging meets neurobiology." Trends in Neurosciences 42: 384-401.
Naidich, T.P. (2013). Imaging of the Brain, Saunders.
Oishi, K. (2008). "Human brain white matter atlas: Identification and assignment of common anatomical structures in superficial white matter." NeuroImage 43: 447-457.
Schaltenbrand, G. (1977). Atlas for Stereotaxy of the Human Brain, Thieme.
Schofield, M.A. (2003). "Fast phase unwrapping algorithm for interferometric applications." Optics Letters 28(14): 1194-1196.
Shin, H.-G. (2021). "χ-separation: Magnetic susceptibility source separation toward iron and myelin mapping in the brain." NeuroImage 240: 118371.
Wu, B. (2012). "Fast and tissue-optimized mapping of magnetic susceptibility and T2* with multi-echo and multi-shot spirals." NeuroImage 59: 297-305.