Combining images and anatomical knowledge to improve automated vein segmentation in MRI

Ward, Phillip G. D.; Ferris, Nicholas J.; Raniga, Parnesh; Dowe, David L.; Ng, Amanda; Barnes, David G.; Egan, Gary F.

doi:10.1016/j.neuroimage.2017.10.049

Cited by 28 publications

(26 citation statements)

References 52 publications

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“…On average, intersubject variability decreased with increasing vessel diameter (Figure 3)where vessels with diameters >$2.5 mm tended to be most stable across subjects. This is in line with another study which examined cerebral veins in 10 subjects and reported high intersubject probabilities near the major veins, including in the superior sagittal sinus, dural sinuses, straight sinus, and internal cerebral veins(Ward et al, 2018). Notably, this relationship was also observed in arteries(Figure 3, R = [.46, .52], all p < .001).Moreover, to the best of our knowledge, no group-based probabilistic venous atlas with a relatively large number of healthy subjects (more than 10) exists in the literature(Nils Daniel Forkert et al, 2012), making ours the first publicly available.…”

supporting

confidence: 91%

“…Notably, this relationship was also observed in arteries(Figure 3, R = [.46, .52], all p < .001).Moreover, to the best of our knowledge, no group-based probabilistic venous atlas with a relatively large number of healthy subjects (more than 10) exists in the literature(Nils Daniel Forkert et al, 2012), making ours the first publicly available. Our segmentation results are similar to the probabilistic map of vasculature based on 10 subjects(Ward et al, 2018) and a deterministic map of large vein atlases,…”

supporting

confidence: 69%

“…Both ToF-MRA and SWI are routinely used in clinical applications, though rarely used when interpreting fMRI results. This could, at least in part, explain the low number of probabilistic atlases of brain arteries (Dufour et al, 2011;Nils Daniel Forkert et al, 2012) andveins (N. D. Forkert et al, 2013;Ward et al, 2018). The cortical vasculature is, therefore, a highly difficult system to model.…”

Section: Introductionmentioning

confidence: 99%

“…and (2) that registration often fails to properly align the vessel trees at smaller branches and thus necessitates many subjects (or samples) to minimize misregistration steps. This could, at least in part, explain the low number of probabilistic atlases of brain arteries (Dufour et al, 2011;Nils Daniel Forkert et al, 2012) and veins (N. D. Forkert et al, 2013;Ward et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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The morphology of the human cerebrovascular system

Bernier

Cunnane

Whittingstall

2018

Human Brain Mapping

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While several methodologies exist for quantifying gray and white matter properties in humans, relatively little is known regarding the spatial organization and the intersubject variability of cerebral vessels. To resolve this, we developed a fast, open‐source processing algorithm using advanced vessel segmentation schemes and iterative nonlinear registration to isolate, extract, and quantify cerebral vessels in susceptibility weighting imaging (SWI) and time‐of‐flight angiography (TOF‐MRA) datasets acquired in a large cohort (n = 42) of healthy individuals. From this, whole‐brain venous and arterial probabilistic maps were generated along with the computation of regional densities and diameters within regions based on popular anatomical and functional atlases. The results show that cerebral vasculature is highly heterogeneous, displaying disproportionally large vessel densities in brain areas such as the anterior and posterior cingulate, cuneus, precuneus, parahippocampus, insula, and temporal gyri. On average, venous densities were slightly higher and less variable across subjects than arterial. Moreover, regional variations in both venous and arterial density were significantly correlated to cortical thickness (R = 0.42). This publicly available new atlas of the human cerebrovascular system provides a first step toward quantifying morphological changes in the diseased brain and serving as a potential regression tool in fMRI analysis.

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supporting

confidence: 91%

supporting

confidence: 69%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The morphology of the human cerebrovascular system

Bernier

Cunnane

Whittingstall

2018

Human Brain Mapping

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“…MRI is a noninvasive technique that provides excellent soft tissue contrast, and the relatively recent development of susceptibility‐weighted imaging (SWI) has dramatically improved the visibility of small veins . A number of algorithms making use of Hessian filters have been proposed to facilitate the segmentation of blood vessels made visible by SWI . In this work, we build on the Steger algorithm to create a Hessian‐based segmentation technique, which takes advantage of the increased signal and contrast available at MRI strengths of 7 T to detect venous structures in vivo.…”

Section: Introductionmentioning

confidence: 99%

Seven‐tesla susceptibility‐weighted analysis of hippocampal venous structures: Application to magnetic‐resonance–normal focal epilepsy

et al. 2020

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Objective Vascular structures may play a significant role in epileptic pathology. Although previous attempts to characterize vasculature relative to epileptogenic zones and hippocampal sclerosis have been inconsistent, an in vivo method of analysis would assist in resolving these inconsistencies and facilitate a comparison against healthy controls in a human model. Magnetic resonance imaging is a noninvasive technique that provides excellent soft tissue contrast, and the relatively recent development of susceptibility‐weighted imaging has dramatically improved the visibility of small veins. Methods We built and tested a Hessian‐based segmentation technique, which takes advantage of the increased signal and contrast available at 7 T to detect venous structures in vivo. We investigate the ability of this technique to quantify vessels in the brain and apply it to an asymmetry analysis of vessel density in the hippocampus in patients with mesial temporal lobe epilepsy (MTLE) and neocortical epilepsy. Results Vessel density was highly symmetric in the hippocampus in controls (mean asymmetry = 0.080 ± 0.076, median = 0.05027), whereas average vessel density asymmetry was greater in neocortical (mean asymmetry = 0.23 ± 0.17, median = 0.14) and MTLE (mean asymmetry = 0.37 ± 0.46, median = 0.26) patients, with the decrease in vessel density ipsilateral to the suspected seizure onset zone. Post hoc testing with one‐way analysis of variance and Tukey post hoc test indicated significant differences in the group means (P < .02) between MTLE and the control group only. Significance Asymmetry in vessel density in the hippocampus is visible in patients with MTLE, even when qualitative and quantitative measures of hippocampal asymmetry show little volumetric difference between epilepsy patients and healthy controls.

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The use of the intensity‐curvature measurement approaches: Applications in magnetic resonance imaging of the human brain

Ciulla

2019

Engineering Reports

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This research describes signal processing techniques useful to highlight and contrast enhance human brain vessels detected with magnetic resonance imaging (MRI). The signal is here represented as multiplication between image intensity and the sum of second‐order partial derivatives of the model function fitted to MRI data. The implication of this novel and different concept is the introduction of the new variable called intensity‐curvature. The following six intensity‐curvature measurement approaches (ICMAs) can hence be calculated: classic‐curvature, intensity‐curvature functional (ICF), signal resilient to interpolation, resilient curvature, and intensity‐curvature terms (ICTs) before and after interpolation. This paper explores the use of ICMAs with applications in human brain MRI. Studying vasculature of the human brain is possible through two techniques apt to measure human brain MRI intensity‐curvature. The first technique is the inverse Fourier transformation procedure, which uses as k‐space filters the ICTs before and after interpolation. The other technique is called intensity‐curvature masking procedure, and it raises each ICMA to a power determining a nonlinear combination of ICMAs to sum up to MRI image intensity on a pixel‐by‐pixel basis. This paper reports that ICTs are k‐space filters, ICF is a high‐pass filter, and ICMAs are mask images. The major implication of these methodologies is effective k‐space filtering. The techniques here presented support visualization of human brain vessels. The advantage of these methods relies in speed of computation.

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Combining images and anatomical knowledge to improve automated vein segmentation in MRI

Abstract: The accuracy of automated vein segmentations derived from the composite vein image was overwhelmingly superior to segmentations derived from SWI or QSM alone.

Cited by 28 publications

References 52 publications

The morphology of the human cerebrovascular system

The morphology of the human cerebrovascular system

Seven‐tesla susceptibility‐weighted analysis of hippocampal venous structures: Application to magnetic‐resonance–normal focal epilepsy

The use of the intensity‐curvature measurement approaches: Applications in magnetic resonance imaging of the human brain

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