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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.

Blood flow measurements are important for understanding the development of cerebrovascular diseases. With 4D flow magnetic resonance imaging (4D flow MRI), simultaneous velocity measurements are obtained in all cerebral arteries in a scan of about ten minutes. However, 4D flow MRI is a relatively new technique. For usefulness in both clinics and research, detailed knowledge is needed about its accuracy and precision for flow quantification. In patients with stroke or transient ischemic attack (TIA) from a symptomatic carotid stenosis, the stenosis may generate a difference in blood pressure and flow between the left and right cerebral hemispheres. Such a hemispheric pressure difference could be an early marker of to what extent a stenosis is affecting cerebral hemodynamics, which could be useful in the planning of carotid surgery. 

The overall aim of the thesis was to determine the accuracy of 4D flow MRI to measure cerebral arterial blood flow, and to develop and evaluate an approach combining 4D flow MRI and computational fluid dynamics (CFD) to characterize the cerebral arterial hemodynamics, with implementation in patients with symptomatic carotid stenosis. The thesis is based on four papers, investigating two cohorts.

The first cohort consisted of 35 elderly volunteers (mean age 79 years) and was studied in paper I-II. Blood flow rates were measured in nine cerebral arteries with 4D flow MRI and 2D phase-contrast MRI as reference. Three different flow quantification methods for 4D flow MRI were evaluated and optimized: one clustering approach and two threshold-based methods. The proposed new method, based on a locally adapted threshold, outperformed the previously suggested methods in flow rate quantification. For the clustering method, flow rates were systematically underestimated. 4D flow MRI was also evaluated to assess different arterial pulsatility measures, and a Windkessel model was used to estimate reference values for cerebrovascular resistance and cerebral arterial compliance in elderly.

The second cohort consisted of 28 stroke and TIA patients (mean age 73 years) with symptomatic carotid stenosis and was studied in paper III-IV. With 4D flow MRI and CFD, the preoperative hemispheric pressure laterality was quantified in the patients. The pressure laterality was compared to hemispheric flow lateralities. Estimating the hemispheric pressure laterality was a promising physiological biomarker for grading the cerebral arterial hemodynamic disturbances in patients with symptomatic carotid stenosis. A CFD model was also developed to predict carotid stump pressure, i.e., the important pressure measured in the clamped carotid artery during surgical removal of the stenosis. The predicted stump pressures were correlated with the pressures measured during surgery. Stump pressure prediction was promising and could be a potential tool in the preoperative planning in order to avoid hypoperfusion during surgery. 

In summary, post-processing methods were successfully developed and evaluated for accurate assessment of mean and pulsatile cerebral blood flow rates with 4D flow MRI. Thereby, this thesis provided knowledge about possibilities and limitations of how 4D flow MRI can be used with respect to cerebral arterial blood flow rate assessment. By contributing with models combining 4D flow MRI and CFD, specifically developed for analysis of pressure distributions in cerebral arteries, novel methods were proposed for assessing patients with symptomatic carotid stenosis in the planning of carotid surgery.