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Blood flow assessment in cerebral arteries with 4D flow magnetic resonance imaging: an automatic atlas-based approach
Umeå University, Faculty of Medicine, Department of Radiation Sciences.ORCID iD: 0000-0002-5911-9511
2018 (English)Doctoral thesis, comprehensive summary (Other academic)Alternative title
Blodflödesmätning i cerebrala artärer med 4D flödes magnetresonanstomografi : en automatisk atlasbaserad metod (Swedish)
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 [en]
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: urn:nbn:se:umu:diva-147256ISBN: 978-91-7601-889-7 (print)OAI: oai:DiVA.org:umu-147256DiVA, id: diva2:1202749
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 FoundationAvailable from: 2018-05-04 Created: 2018-04-30 Last updated: 2018-06-09Bibliographically approved
List of papers
1. Automatic labeling of cerebral arteries in magnetic resonance angiography
Open this publication in new window or tab >>Automatic labeling of cerebral arteries in magnetic resonance angiography
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2016 (English)In: Magnetic Resonance Materials in Physics, Biology and Medicine, ISSN 0968-5243, E-ISSN 1352-8661, Vol. 29, no 1, p. 39-47Article in journal (Refereed) Published
Abstract [en]

In order to introduce 4D flow magnetic resonance imaging (MRI) as a standard clinical instrument for studying the cerebrovascular system, new and faster postprocessing tools are necessary. The objective of this study was to construct and evaluate a method for automatic identification of individual cerebral arteries in a 4D flow MRI angiogram. Forty-six elderly individuals were investigated with 4D flow MRI. Fourteen main cerebral arteries were manually labeled and used to create a probabilistic atlas. An automatic atlas-based artery identification method (AAIM) was developed based on vascular-branch extraction and the atlas was used for identification. The method was evaluated by comparing automatic with manual identification in 4D flow MRI angiograms from 67 additional elderly individuals. Overall accuracy was 93 %, and internal carotid artery and middle cerebral artery labeling was 100 % accurate. Smaller and more distal arteries had lower accuracy; for posterior communicating arteries and vertebral arteries, accuracy was 70 and 89 %, respectively. The AAIM enabled fast and fully automatic labeling of the main cerebral arteries. AAIM functionality provides the basis for creating an automatic and powerful method to analyze arterial cerebral blood flow in clinical routine.

Keywords
Magnetic resonance angiography, Cerebral angiography, Circle of Willis, Atlases as topic, Automatic data processing
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:umu:diva-117830 (URN)10.1007/s10334-015-0512-5 (DOI)000370159800005 ()
Available from: 2016-04-05 Created: 2016-03-04 Last updated: 2018-06-07Bibliographically approved
2. A Stereotactic Probabilistic Atlas for the Major Cerebral Arteries
Open this publication in new window or tab >>A Stereotactic Probabilistic Atlas for the Major Cerebral Arteries
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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.

Keywords
Cerebral arteries, Probabilistic atlas, 4D flow MRI, Automatic labeling, Spatial normalization
National Category
Clinical Medicine
Identifiers
urn:nbn:se:umu:diva-131144 (URN)10.1007/s12021-016-9320-y (DOI)000394260000009 ()27873151 (PubMedID)2-s2.0-84996542654 (Scopus ID)
Available from: 2017-02-06 Created: 2017-02-06 Last updated: 2018-06-09Bibliographically approved
3. 4D flow MRI - Automatic assessment of blood flow in cerebral arteries
Open this publication in new window or tab >>4D flow MRI - Automatic assessment of blood flow in cerebral arteries
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

Purpose: With a 10-minute 4D flow MRI scan, the distribution of blood flow to individual arteries throughout the brain can be analyzed. This technique has potential to become a biomarker for treatment decisions and to predict prognosis after stroke. For efficient analyzing and modeling of the large dataset in clinical practice, automatization is needed. We hypothesized that a recently presented atlas-based identification method could be expanded to include standardized automatic assessment of blood flow in the main cerebral arteries.

Method: A previously developed atlas-based method for arterial labeling was adapted to facilitate measurements of blood flow rate by defining specific regions of interest within the arterial atlas and implementing a method for blood flow quantification. The suggested method was evaluated on 38 subjects with carotid artery stenosis, by comparing automatically measured blood flow with manual reference measurements. The method was evaluated based on both labeling accuracy and agreement with the manual reference in terms of blood flow rate.

Results: Out of the 559 arteries in the manual reference, 489 were correctly labeled, yielding a sensitivity of 88%, a specificity of 85%, and an accuracy of 87%. Automatic and reference measurement ranged from 10 to 580 ml/min and were highly correlated (r=0.99) with a mean flow difference of 0.61 ml/min (p=0.21).

Conclusion: This study confirms that atlas-based labeling of 4D flow MRI data is suitable for efficient flow quantification in the major cerebral arteries. The suggested method improves the feasibility of analyzing cerebral 4D flow data, and fills a gap necessary for implementation in clinical use.

National Category
Medical Engineering
Identifiers
urn:nbn:se:umu:diva-147254 (URN)
Available from: 2018-04-30 Created: 2018-04-30 Last updated: 2018-06-09
4. Blood flow assessment in cerebral arteries with 4D flow MRI, concordance with 2D PCMRI
Open this publication in new window or tab >>Blood flow assessment in cerebral arteries with 4D flow MRI, concordance with 2D PCMRI
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

Accurate and efficient assessment of the arterial cerebral blood flow is important when studying vascular diseases of the brain. Today arterial blood flow is mainly measured with ultrasound or 2D PCMRI. 4D flow MRI provides the possibility to measure cerebral blood flow in the whole brain volume in one scan of less than ten minutes, but evaluated post-processing tools are still lacking. The aim of this study was to determine and optimize the accuracy of in vivo 4D flow MRI blood flow rate assessments in major cerebral arteries, evaluated with 2D PCMRI as reference.

We compared blood flow rates measured with 4D flow MRI, to 2D PCMRI in nine large cerebral arteries, in 35 elderly subjects (20 women, 79  5 years, range 70-91 years). Lumen segmentation in the 4D flow MRI was performed with k-means clustering methods using four different sets of input data, and with two types of thresholding methods. The threshold was defined as a percentage of the maximum intensity value in the complex difference image. Local and a global thresholding approaches was used, and threshold values from 6% to 26% were evaluated.

For all clustering methods, a large systematic underestimation of flow compared to 2D PCMRI was found. With the thresholding methods, the lowest average flow difference was found for 20% local (-0.04 ± 15.1 ml/min, ICC = 0.971) or 10% global (-0.07 ± 17.3 ml/min, ICC = 0.967) thresholding with a significant lower standard deviation for local (F-test, p=0.009). These results indicate that a locally adapted threshold value gives a more stable result compared to a global fixed threshold. Averaging flow rates in several adjacent cut-planes, did not improve flow difference, standard deviation of the difference, or the intraclass correlation.

In conclusion, we describe an algorithm based on local thresholding, making it possible to obtain accurate 4D flow quantification in cerebral arteries. Importantly, the remaining measurement variability was similar to the within-subject variation reported for the reference method. Taken together 4D flow with the proposed post-processing has the potential to contribute to a useful reliable clinical tool for assessment of blood flow in the major cerebral arteries.

National Category
Medical Engineering
Identifiers
urn:nbn:se:umu:diva-147255 (URN)
Available from: 2018-04-30 Created: 2018-04-30 Last updated: 2018-06-09

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