A Processing Pipeline for Quantifying Lenticulostriate Artery Vascular Volume in Subcortical Nuclei

Wei, Ning; Zhang, Xianchang; An, Jing; Zhuo, Yan; Zhang, Zihao

doi:10.3389/fneur.2021.700476

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…The high metabolic rate of subcortical nuclei explains the preponderance of subcortical damage in hypoxic-ischemic brain injury and correlates with acute and long-term neurologic syndromes ( 16 , 17 ). There are spatial relationships among the small arteries and basal nuclei ( 18 ). Cerebral small vessel disease is commonly seen in aging and has been associated with subcortical nuclei abnormalities ( 19 ).…”

Section: Discussionmentioning

confidence: 99%

Long-term follow-up of dynamic brain changes in patients recovered from COVID-19 without neurological manifestations

Tian¹,

Wu²,

Chen³

et al. 2022

JCI Insight

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BACKGROUND After the initial surge in COVID-19 cases, large numbers of patients were discharged from a hospital without assessment of recovery. Now, an increasing number of patients report postacute neurological sequelae, known as “long COVID” — even those without specific neurological manifestations in the acute phase. METHODS Dynamic brain changes are crucial for a better understanding and early prevention of “long COVID.” Here, we explored the cross-sectional and longitudinal consequences of COVID-19 on the brain in 34 discharged patients without neurological manifestations. Gray matter morphology, cerebral blood flow (CBF), and volumes of white matter tracts were investigated using advanced magnetic resonance imaging techniques to explore dynamic brain changes from 3 to 10 months after discharge. RESULTS Overall, the differences of cortical thickness were dynamic and finally returned to the baseline. For cortical CBF, hypoperfusion in severe cases observed at 3 months tended to recover at 10 months. Subcortical nuclei and white matter differences between groups and within subjects showed various trends, including recoverable and long-term unrecovered differences. After a 10-month recovery period, a reduced volume of nuclei in severe cases was still more extensive and profound than that in mild cases. CONCLUSION Our study provides objective neuroimaging evidence for the coexistence of recoverable and long-term unrecovered changes in 10-month effects of COVID-19 on the brain. The remaining potential abnormalities still deserve public attention, which is critically important for a better understanding of “long COVID” and early clinical guidance toward complete recovery. FUNDING National Natural Science Foundation of China.

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Section: Discussionmentioning

confidence: 99%

Long-term follow-up of dynamic brain changes in patients recovered from COVID-19 without neurological manifestations

Tian¹,

Wu²,

Chen³

et al. 2022

JCI Insight

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A Deep Learning Pipeline for Analysis of the 3D Morphology of the Cerebral Small Perforating Arteries from Time-of-Flight 7 Tesla MRI

Li,

Chatterjee,

Jiaerken

et al. 2024

Preprint

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The lenticulostriate arteries (LSA) supply important subcortical structures in the brain and are affected in cerebral small vessel disease (CSVD), leading to changes in their morphology. 7 Tesla Time-of-Flight magnetic resonance angiography (7T-TOF-MRA) now allows their visualisation in humans, but current analysis of LSA morphology largely relies on manual tracing on 2D coronal maximum-intensity-projection (MIP) images, which discards significant information from the third spatial dimension. We aimed to develop a semi-automatic pipeline for quantifying the 3D morphology of LSAs from 7T-TOF-MRA in patients with CSVD. We used contrast-enhanced 7T-TOF-MRA data from 15 subjects enrolled in a local CSVD study. Our pipeline consists of two main stages: vessel segmentation and LSA quantification. For segmentation, we fine-tuned a state-of-the-art deep learning model, DS6, for vessel segmentation and compared its performance against a classical Frangi filter-based pipeline, Multi-Scale Frangi Diffusive Filter (MSFDF). Both methods were evaluated against manually labelled ground-truth masks in LSA regions. In the LSA quantification stage, the user defines a region-of-interest around LSAs and checks the segmentation. Based on this, the LSA centrelines are extracted, and branch counts, length, tortuosity, and curvature are computed. Additionally, we conducted the traditional LSA analysis using 2D coronal MIPs, and we evaluated the correlation between the results from the 2D and 3D analyses. For vessel segmentation, the fine-tuned DS6 model achieved a mean Dice similarity coefficient (DSC) of 0.814±0.029 during testing, outperforming MSFDF on DSC, sensitivity, and balanced average Hausdorff distance in terms of both mean value and stability. Visual inspection confirmed that DS6 was more sensitive in detecting LSA branches with weak signals. On average, the 15 subjects had 5.9±1.6 LSA stems and 28.7±9.9 branches. The mean length of an LSA branch was 42.5±5.7mm, and mean tortuosity was 1.9±0.2. Finally, the branch counts from 2D and 3D analyses correlated well (ρ=0.741, p=2.816e-06), whereas the stem count, branch length and tortuosity measurements were significantly different, showing the insufficiency of MIP analysis (stem: ρ=0.230, p=2.207e-01; length: r=0.565, p=1.153e-03; tortuosity: r=0.400, p=2.847e-02). We have developed an open-source semi-automatic pipeline using deep learning for evaluating the 3D morphology of LSAs in CSVD patients from 7T-TOF-MRA. We show that analysing LSA morphology in 3D reveals previously inaccessible aspects of morphology. Our pipeline offers a valuable tool for clinical research studies to characterise the 3D morphology of LSAs in CSVD.

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A Processing Pipeline for Quantifying Lenticulostriate Artery Vascular Volume in Subcortical Nuclei

Cited by 2 publications

References 22 publications

Long-term follow-up of dynamic brain changes in patients recovered from COVID-19 without neurological manifestations

Long-term follow-up of dynamic brain changes in patients recovered from COVID-19 without neurological manifestations

A Deep Learning Pipeline for Analysis of the 3D Morphology of the Cerebral Small Perforating Arteries from Time-of-Flight 7 Tesla MRI

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