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4D flow MRI: automatic assessment of blood flow in cerebral arteries
Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper. Umeå Universitet.ORCID-id: 0000-0002-5911-9511
Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper. Umeå universitet, Medicinska fakulteten, Umeå centrum för funktionell hjärnavbildning (UFBI).
Umeå universitet, Medicinska fakulteten, Institutionen för farmakologi och klinisk neurovetenskap, Klinisk neurovetenskap.
Umeå universitet, Medicinska fakulteten, Institutionen för farmakologi och klinisk neurovetenskap, Klinisk neurovetenskap.ORCID-id: 0000-0001-6451-1940
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2019 (Engelska)Ingår i: Biomedical Physics & Engineering Express, ISSN 2057-1976, Vol. 5, nr 1, artikel-id 015003Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Objective: 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. To efficiently analyze and model the large dataset in clinical practice, automatization is needed. We hypothesized that identification of selected arterial regions using an atlas with a priori probability information on their spatial distribution can provide standardized measurements of blood flow in the main cerebral arteries.

Approach: A new method for automatic placement of measurement locations in 4D flow MRI was developed based on an existing atlas-based method for arterial labeling, by defining specific regions of interest within the corresponding arterial atlas. The suggested method was evaluated on 38 subjects with carotid artery stenosis, by comparing measurements of blood flow rate at automatically selected locations to reference measurements at manually selected locations.

Main results: Automatic and reference measurement ranged from 10 to 580 ml min−1 and were highly correlated (r = 0.99) with a mean flow difference of 0.61 ± 10.7 ml min−1 (p = 0.21). Out of the 559 arterial segments in the manual reference, 489 were correctly labeled, yielding a sensitivity of 88%, a specificity of 85%, and a labeling accuracy of 87%.

Significance: 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.

Ort, förlag, år, upplaga, sidor
Institute of Physics Publishing (IOPP), 2019. Vol. 5, nr 1, artikel-id 015003
Nyckelord [en]
cerebral arteries, hemodynamics, carotid stenosis, magnetic resonance imaging, circle of willis, cerebrovascular circulation
Nationell ämneskategori
Medicinsk bildbehandling
Identifikatorer
URN: urn:nbn:se:umu:diva-147254DOI: 10.1088/2057-1976/aae8d1ISI: 000457627700003OAI: oai:DiVA.org:umu-147254DiVA, id: diva2:1202719
Forskningsfinansiär
Vetenskapsrådet, 2015-05616Hjärt-Lungfonden, 20110383, 20140592
Anmärkning

Originally included in thesis in manuscript form

Tillgänglig från: 2018-04-30 Skapad: 2018-04-30 Senast uppdaterad: 2019-11-19Bibliografiskt granskad
Ingår i avhandling
1. Blood flow assessment in cerebral arteries with 4D flow magnetic resonance imaging: an automatic atlas-based approach
Öppna denna publikation i ny flik eller fönster >>Blood flow assessment in cerebral arteries with 4D flow magnetic resonance imaging: an automatic atlas-based approach
2018 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Alternativ titel[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.

Ort, förlag, år, upplaga, sidor
Umeå: Umeå universitet, 2018. s. 60
Serie
Umeå University medical dissertations, ISSN 0346-6612 ; 1965
Nyckelord
Circle of Willis, 4D flow MRI, Cerebral arteries, Vascular disease, Stroke, Automatic labeling, Probabilistic atlas, Cerebral blood flow, Neuroimaging, Magnetic Resonance Imaging
Nationell ämneskategori
Medicinteknik
Identifikatorer
urn:nbn:se:umu:diva-147256 (URN)978-91-7601-889-7 (ISBN)
Disputation
2018-05-25, Betula, NUS, Umeå, 13:00 (Svenska)
Opponent
Handledare
Forskningsfinansiär
Vetenskapsrådet, 2015–05616Hjärt-Lungfonden, 20110383Hjärt-Lungfonden, 20140592Hjärnfonden
Tillgänglig från: 2018-05-04 Skapad: 2018-04-30 Senast uppdaterad: 2018-06-09Bibliografiskt granskad

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