Comparison of vessel enhancement algorithms applied to time‐of‐flight MRA images for cerebrovascular segmentation

Phellan, Renzo; Forkert, Nils D.

doi:10.1002/mp.12560

Cited by 19 publications

(18 citation statements)

References 28 publications

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“…These values are then used to determine the standard deviation values for the Frangi filter (Shikata et al, 2004). The minimum sigma value (0.8) and the increment in sigma (0.2) were chosen based on prior literature (Phellan and Forkert, 2017). The maximum sigma values were then selected based on the relation sigma max = √ radii max (Krissian et al, 2000).…”

Section: Mip Vessel Segmentationmentioning

confidence: 99%

“…While existing methods are limited to applying vessel enhancement filters to either the original non-projected 3D images or a single MIP through the entire imaged volume (Gao et al, 2011;Hsu et al, 2017;Phellan and Forkert, 2017), there has been little investigation of the influence of projection thickness on the effectiveness of vessel segmentation. Of the few studies reported, one showed similarity between vessel radii measurements extracted from parameter-dependent MIP MRA and digital subtraction angiography derived from high contrast x-ray images (Persson et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

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A Fully Automated Method for Segmenting Arteries and Quantifying Vessel Radii on Magnetic Resonance Angiography Images of Varying Projection Thickness

et al. 2020

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Precise quantification of cerebral arteries can help with differentiation and prognostication of cerebrovascular disease. Existing image processing and segmentation algorithms for magnetic resonance angiography (MRA) are limited to the analysis of either 2D maximum intensity projection images or the entire 3D volume. The goal of this study was to develop a fully automated, hybrid 2D-3D method for robust segmentation of arteries and accurate quantification of vessel radii using MRA at varying projection thicknesses.Methods: A novel algorithm that employs an adaptive Frangi filter for segmentation of vessels followed by estimation of vessel radii is presented. The method was evaluated on MRA datasets and corresponding manual segmentations from three healthy subjects for various projection thicknesses. In addition, the vessel metrics were computed in four additional subjects. Three synthetically generated angiographic datasets resembling brain vasculature were also evaluated under different noise levels. Dice similarity coefficient, Jaccard Index, F-score, and concordance correlation coefficient were used to measure the segmentation accuracy of manual versus automatic segmentation.Results: Our new adaptive filter rendered accurate representations of vessels, maintained accurate vessel radii, and corresponded better to manual segmentation at different projection thicknesses than prior methods. Validation with synthetic datasets under low contrast and noisy conditions revealed accurate quantification of vessels without distortions. Conclusion:We have demonstrated a method for automatic segmentation of vascular trees and the subsequent generation of a vessel radii map. This novel technique can be applied to analyze arterial structures in healthy and diseased populations and improve the characterization of vascular integrity.

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Section: Mip Vessel Segmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Fully Automated Method for Segmenting Arteries and Quantifying Vessel Radii on Magnetic Resonance Angiography Images of Varying Projection Thickness

et al. 2020

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“…Examples include prior segmentation of the TOF and/or ASL dataset to enhance the vessel-to-background contrast, centerline and bifurcation prediction of the vessels, automated detection of impaired flow behavior (e.g. in stenosis) and risk of rupture in aneurysms [20][21][22][23][24]. Additionally, databases of normal vascular images as well as normal flow behavior can be used to early detect pathological changes.…”

Section: Discussionmentioning

confidence: 99%

Time-Resolved High-Resolution Angiography Combining Arterial Spin Labeling and Time-of-Flight Imaging

2020

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A strategy to combine two non-contrast-enhanced magnetic resonance angiography techniques is presented. It is based on arterial spin-labeled magnetic resonance imaging to visualize the arterial system at different time points to obtain information about hemodynamic properties in conjunction with a high-resolution time-of-flight angiography acquisition. The temporal information obtained by arterial spin labeling (ASL) is combined with the highly spatial resolved time-of-flight image to obtain information about blood flow. Extracting the information of ASL and time-of-flight-imaging leads to images with high spatial resolution which also give information about the temporal course of blood through the intracerebral vasculature. Furthermore, owing to the properties of ASL, visible venous flow in the time-of-flight images can be suppressed. The behavior of vascular filling (i.e. signal changes in the ASL) is investigated and used for further interpretation of the data. Furthermore, the ASL data were down-sampled to find a minimally needed spatial resolution to combine both image types. Up to 1.6 mm isotropic resolution still showed satisfying results rated by two independent readers. In conclusion, a combination of these two different vascular imaging modalities allows to obtain highly spatial and time-resolved images.

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“…The labeled image is then subtracted from the control image to remove the signal of non-vascular tissues, but some residual noise can still be observed, even after subtraction, particularly in the last images of the 4D ASL MRA sequence. As a last preprocessing step, the subtracted images are filtered using the multiscale vesselness filter designed by Erdt et al [21], which enhances the intensity of vessels in the images and has been shown to help to increase the accuracy of the final segmentation [22].…”

Section: A Segmentationmentioning

confidence: 99%

Segmentation-based blood flow parameter refinement in cerebrovascular structures using 4D arterial spin labeling MRA

Phellan

Lindner²,

Helle

et al. 2019

IEEE Trans. Biomed. Eng.

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Objective: Cerebrovascular diseases are one of the main global causes of death and disability in the adult population. The preferred imaging modality for the diagnostic routine is digital subtraction angiography, an invasive modality. Timeresolved three-dimensional arterial spin labeling magnetic resonance angiography (4D ASL MRA) is an alternative non-invasive modality, which captures morphological and blood flow data of the cerebrovascular system, with high spatial and temporal resolution. This work proposes advanced medical image processing methods that extract the anatomical and hemodynamic information contained in 4D ASL MRA datasets. Methods: A previously published segmentation method, which uses blood flow data to improve its accuracy, is extended to estimate blood flow parameters by fitting a mathematical model to the measured vascular signal. The estimated values are then refined using regression techniques within the cerebrovascular segmentation. The proposed method was evaluated using fifteen 4D ASL MRA phantoms, with ground-truth morphological and hemodynamic data, fifteen 4D ASL MRA datasets acquired from healthy volunteers, and two 4D ASL MRA datasets from patients with a stenosis. Results: The proposed method reached an average Dice similarity coefficient of 0.957 and 0.938 in the phantom and real dataset segmentation evaluations, respectively. The estimated blood flow parameter values are more similar to the ground-truth values after the refinement step, when using phantoms. A qualitative analysis showed that the refined blood flow estimation is more realistic compared to the raw hemodynamic parameters. Conclusion: The proposed method can provide accurate segmentations and blood flow parameter estimations in the cerebrovascular system using 4D ASL MRA datasets. Significance: The information obtained with the proposed method can help clinicians and researchers to study the cerebrovascular system non-invasively.

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Comparison of vessel enhancement algorithms applied to time‐of‐flight MRA images for cerebrovascular segmentation

Cited by 19 publications

References 28 publications

A Fully Automated Method for Segmenting Arteries and Quantifying Vessel Radii on Magnetic Resonance Angiography Images of Varying Projection Thickness

A Fully Automated Method for Segmenting Arteries and Quantifying Vessel Radii on Magnetic Resonance Angiography Images of Varying Projection Thickness

Time-Resolved High-Resolution Angiography Combining Arterial Spin Labeling and Time-of-Flight Imaging

Segmentation-based blood flow parameter refinement in cerebrovascular structures using 4D arterial spin labeling MRA

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