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A Stereotactic Probabilistic Atlas for the Major Cerebral Arteries
Umeå University, Faculty of Medicine, Department of Radiation Sciences.
Umeå University, Faculty of Medicine, Department of Radiation Sciences. Umeå University, Faculty of Medicine, Umeå Centre for Functional Brain Imaging (UFBI).
Umeå University, Faculty of Medicine, Department of Radiation Sciences. Umeå University, Faculty of Science and Technology, Centre for Biomedical Engineering and Physics (CMTF).
Umeå University, Faculty of Medicine, Department of Pharmacology and Clinical Neuroscience, Clinical Neuroscience.
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2017 (English)In: Neuroinformatics, ISSN 1539-2791, E-ISSN 1559-0089, Vol. 15, no 1, p. 101-110Article in journal (Refereed) Published
Abstract [en]

Improved whole brain angiographic and velocity-sensitive MRI is pushing the boundaries of noninvasively obtained cerebral vascular flow information. The complexity of the information contained in such datasets calls for automated algorithms and pipelines, thus reducing the need of manual analyses by trained radiologists. The objective of this work was to lay the foundation for such automated pipelining by constructing and evaluating a probabilistic atlas describing the shape and location of the major cerebral arteries. Specifically, we investigated how the implementation of a non-linear normalization into Montreal Neurological Institute (MNI) space improved the alignment of individual arterial branches. In a population-based cohort of 167 subjects, age 64-68 years, we performed 4D flow MRI with whole brain volumetric coverage, yielding both angiographic and anatomical data. For each subject, sixteen cerebral arteries were manually labeled to construct the atlas. Angiographic data were normalized to MNI space using both rigid-body and non-linear transformations obtained from anatomical images. The alignment of arterial branches was significantly improved by the non-linear normalization (p < 0.001). Validation of the atlas was based on its applicability in automatic arterial labeling. A leave-one-out validation scheme revealed a labeling accuracy of 96 %. Arterial labeling was also performed in a separate clinical sample (n = 10) with an accuracy of 92.5 %. In conclusion, using non-linear spatial normalization we constructed an artery-specific probabilistic atlas, useful for cerebral arterial labeling.

Place, publisher, year, edition, pages
2017. Vol. 15, no 1, p. 101-110
Keywords [en]
Cerebral arteries, Probabilistic atlas, 4D flow MRI, Automatic labeling, Spatial normalization
National Category
Clinical Medicine
Identifiers
URN: urn:nbn:se:umu:diva-131144DOI: 10.1007/s12021-016-9320-yISI: 000394260000009PubMedID: 27873151Scopus ID: 2-s2.0-84996542654OAI: oai:DiVA.org:umu-131144DiVA, id: diva2:1071866
Available from: 2017-02-06 Created: 2017-02-06 Last updated: 2018-06-09Bibliographically approved
In thesis
1. Blood flow assessment in cerebral arteries with 4D flow magnetic resonance imaging: an automatic atlas-based approach
Open this publication in new window or tab >>Blood flow assessment in cerebral arteries with 4D flow magnetic resonance imaging: an automatic atlas-based approach
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Blodflödesmätning i cerebrala artärer med 4D flödes magnetresonanstomografi : en automatisk atlasbaserad metod
Abstract [en]

Background: Disturbed blood flow to the brain has been associated with several neurological diseases, from stroke and vascular diseases to Alzheimer’s and cognitive decline. To determine the cerebral arterial blood flow distribution, measurements are needed in both distal and proximal arteries.

4D flow MRI makes it possible to obtain blood flow velocities from a volume covering the entire brain in one single scan. This facilitates more extensive flow investigations, since flow rate assessment in specific arteries can be done during post-processing. The flow rate assessment is still rather laborious and time consuming, especially if the number of arteries of interest is high. In addition, the quality of the measurements relies heavily on the expertise of the investigator.

The aim of this thesis was to develop and evaluate an automatic post-processing tool for 4D flow MRI that identifies the main cerebral arteries and calculates their blood flow rate with minimal manual input. Atlas-based labeling of brain tissue is common in toolboxes for analysis of neuroimaging-data, and we hypothesized that a similar approach would be suitable for arterial labeling. We also wanted to investigate how to best separate the arterial lumen from background for calculation of blood flow.

Methods: An automatic atlas-based arterial identification method (AAIM) for flow assessment was developed. With atlas-based labeling, voxels are labeled based on their spatial location in MNI-space, a stereotactic coordinate system commonly used for neuroimaging analysis. To evaluate the feasibility of this approach, a probabilistic atlas was created from a set of angiographic images derived from 4D flow MRI. Included arteries were the anterior (ACA), middle (MCA) and posterior (PCA) cerebral arteries, as well as the internal carotid (ICA), vertebral (VA), basilar (BA) and posterior communicating (PCoA) arteries. To identify the arteries in an angiographic image, a vascular skeleton where each branch represented an arterial segment was extracted and labeled according to the atlas. Labeling accuracy of the AAIM was evaluated by visual inspection.

Next, the labeling method was adapted for flow measurements by pre-defining desired regions within the atlas. Automatic flow measurements were then compared to measurements at manually identified locations. During the development process, arterial identification was evaluated on four patient cohorts, with and without vascular disease. Finally, three methods for flow quantification using 4D flow MRI: k-means clustering; global thresholding; and local thresholding, were evaluated against a standard reference method.

Results: The labeling accuracy on group level was between 96% and 87% for all studies, and close to 100% for ICA and BA. Short arteries (PCoA) and arteries with large individual anatomical variation (VA) were the most challenging. Blood flow measurements at automatically identified locations were highly correlated (r=0.99) with manually positioned measurements, and difference in mean flow was negligible.

Both global and local thresholding out-performed k-means clustering, since the threshold value could be optimized to produce a mean difference of zero compared to reference. The local thresholding had the best concordance with the reference method (p=0.009, F-test) and was the only method that did not have a significant correlation between flow difference and flow rate. In summary, with a local threshold of 20%, ICC was 0.97 and the flow rate difference was -0.04 ± 15.1 ml/min, n=308.

Conclusion: This thesis work demonstrated that atlas-based labeling was suitable for identification of cerebral arteries, enabling automated processing and flow assessment in 4D flow MRI. Furthermore, the proposed flow rate quantification algorithm reduced some of the most important shortcomings associated with previous methods. This new platform for automatic 4D flow MRI data analysis fills a gap needed for efficient in vivo investigations of arterial blood flow distribution to the entire vascular tree of the brain, and should have important applications to practical use in neurological diseases.

Place, publisher, year, edition, pages
Umeå: Umeå universitet, 2018. p. 60
Series
Umeå University medical dissertations, ISSN 0346-6612 ; 1965
Keywords
Circle of Willis, 4D flow MRI, Cerebral arteries, Vascular disease, Stroke, Automatic labeling, Probabilistic atlas, Cerebral blood flow, Neuroimaging, Magnetic Resonance Imaging
National Category
Medical Engineering
Identifiers
urn:nbn:se:umu:diva-147256 (URN)978-91-7601-889-7 (ISBN)
Public defence
2018-05-25, Betula, NUS, Umeå, 13:00 (Swedish)
Opponent
Supervisors
Funder
Swedish Research Council, 2015–05616Swedish Heart Lung Foundation, 20110383Swedish Heart Lung Foundation, 20140592The Swedish Brain Foundation
Available from: 2018-05-04 Created: 2018-04-30 Last updated: 2018-06-09Bibliographically approved

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