Literature DB >> 20632608

Automatic segmentation of intracranial arteries and veins in four-dimensional cerebral CT perfusion scans.

Adriënne Mendrik1, Evert-Jan Vonken, Bram van Ginneken, Ewoud Smit, Annet Waaije, Giovanna Bertolini, Max A Viergever, Mathias Prokop.   

Abstract

PURPOSE: CT angiography (CTA) scans are the current standard for vascular analysis of patients with cerebrovascular diseases, such as acute stroke and subarachnoid hemorrhage. An additional CT perfusion (CTP) scan is acquired of these patients to assess the perfusion of the cerebral tissue. The aim of this study is to extend the diagnostic yield of the CTP scans to also include vascular information.
METHODS: CTP scans are acquired by injecting contrast material and repeatedly scanning the head over time. Therefore, time-intensity profiles are available for each voxel in the scanned volume, resulting in a 4D dataset. These profiles can be utilized to differentiate not only between vessels and background but also between arteries and veins. In this article, a fully automatic method is proposed for the segmentation of the intracranial arteries and veins from 4D cerebral CTP scans. Furthermore, a vessel enhanced volume is presented, in which the vasculature is highlighted and background structures are suppressed. Combining this volume with the artery/vein segmentation results in an arteriogram and a venogram, which could serve as additional means for vascular analysis in patients with cerebrovascular diseases. The artery/vein segmentation is quantitatively evaluated by comparing the results to manual segmentations by two expert observers.
RESULTS: Results (paired two-tailed t-test) show that the accuracies of the proposed artery/vein labeling are not significantly different from the accuracies of the expert observer manual labeling (ground truth). Moreover, sensitivity and specificity of the proposed artery/vein labeling, relative to both expert observer ground truths, were similar to the sensitivity and specificity of the expert observer labeling compared to each other.
CONCLUSIONS: The proposed method for artery/vein segmentation is shown to be very accurate for arteries and veins with normal perfusion. Combining the artery/vein segmentation with the vessel enhanced volume produces an arteriogram and a venogram, which have the potential to extend the diagnostic yield of CTP scans and replace the additional CTA scan, but could also be helpful to radiologists in addition to the CTA scan.

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Year:  2010        PMID: 20632608     DOI: 10.1118/1.3397813

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  9 in total

1.  ART 3.5D: an algorithm to label arteries and veins from three-dimensional angiography.

Authors:  Beatrice Barra; Elena De Momi; Giancarlo Ferrigno; Guglielmo Pero; Francesco Cardinale; Giuseppe Baselli
Journal:  J Med Imaging (Bellingham)       Date:  2016-11-29

2.  Improved arterial visualization in cerebral CT perfusion-derived arteriograms compared with standard CT angiography: a visual assessment study.

Authors:  A M Mendrik; E P A Vonken; G A P de Kort; B van Ginneken; E J Smit; M A Viergever; M Prokop
Journal:  AJNR Am J Neuroradiol       Date:  2012-05-24       Impact factor: 3.825

3.  Validation of a method to differentiate arterial and venous vessels in CT perfusion data using linear combinations of quantitative time-density curve characteristics.

Authors:  Lukas Havla; Moritz Schneider; Kolja M Thierfelder; Sebastian E Beyer; Birgit Ertl-Wagner; Wieland H Sommer; Olaf Dietrich
Journal:  Eur Radiol       Date:  2015-03-28       Impact factor: 5.315

4.  Masked smoothing using separable kernels for CT perfusion images.

Authors:  David S Wack; Kenneth V Snyder; Kevin F Seals; Adnan H Siddiqui
Journal:  BMC Med Imaging       Date:  2014-08-21       Impact factor: 1.930

5.  Cerebral Artery and Vein Segmentation in Four-dimensional CT Angiography Using Convolutional Neural Networks.

Authors:  Midas Meijs; Sjoert A H Pegge; Maria H E Vos; Ajay Patel; Sil C van de Leemput; Kevin Koschmieder; Mathias Prokop; Frederick J A Meijer; Rashindra Manniesing
Journal:  Radiol Artif Intell       Date:  2020-07-29

6.  Total bolus extraction method improves arterial image quality in dynamic CTAs derived from whole-brain CTP data.

Authors:  Elyas Ghariq; Adriënne M Mendrik; Peter W A Willems; Raoul M S Joemai; Eidrees Ghariq; Evert-jan Vonken; Matthias J P van Osch; Marianne A A van Walderveen
Journal:  Biomed Res Int       Date:  2014-07-16       Impact factor: 3.411

7.  Robust Segmentation of the Full Cerebral Vasculature in 4D CT of Suspected Stroke Patients.

Authors:  Midas Meijs; Ajay Patel; Sil C van de Leemput; Mathias Prokop; Ewoud J van Dijk; Frank-Erik de Leeuw; Frederick J A Meijer; Bram van Ginneken; Rashindra Manniesing
Journal:  Sci Rep       Date:  2017-11-15       Impact factor: 4.379

8.  White Matter and Gray Matter Segmentation in 4D Computed Tomography.

Authors:  Rashindra Manniesing; Marcel T H Oei; Luuk J Oostveen; Jaime Melendez; Ewoud J Smit; Bram Platel; Clara I Sánchez; Frederick J A Meijer; Mathias Prokop; Bram van Ginneken
Journal:  Sci Rep       Date:  2017-03-09       Impact factor: 4.379

9.  An easy method to differentiate retinal arteries from veins by spectral domain optical coherence tomography: retrospective, observational case series.

Authors:  Yanling Ouyang; Qing Shao; Dirk Scharf; Antonia M Joussen; Florian M Heussen
Journal:  BMC Ophthalmol       Date:  2014-05-15       Impact factor: 2.209

  9 in total

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