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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.

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.

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.

Background: Accelerated 4D flow MRI allows for high‐resolution velocity measurements with whole‐brain coverage. Such scans are increasingly used to calculate flow rates of individual arteries in the vascular tree, but detailed information about the accuracy and precision in relation to different postprocessing options is lacking.

Purpose: To evaluate and optimize three proposed segmentation methods and determine the accuracy of in vivo 4D flow MRI blood flow rate assessments in major cerebral arteries, with high‐resolution 2D PCMRI as a reference.

Study Type: Prospective.

Subjects: Thirty‐five subjects (20 women, 79 ± 5 years, range 70–91 years).

Field Strength/Sequence: 4D flow MRI with PC‐VIPR and 2D PCMRI acquired with a 3 T scanner.

Assessment: We compared blood flow rates measured with 4D flow MRI, to the reference, in nine main cerebral arteries. Lumen segmentation in the 4D flow MRI was performed with k‐means clustering using four different input datasets, 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 global thresholding approaches were used, with evaluated thresholds from 6–26%.

Statistical Tests: Paired t‐test, F‐test, linear correlation (P < 0.05 was considered significant) along with intraclass correlation (ICC).

Results: With the thresholding methods, the lowest average flow difference was obtained for 20% local (0.02 ± 15.0 ml/min, ICC = 0.97, n = 310) or 10% global (0.08 ± 17.3 ml/min, ICC = 0.97, n = 310) thresholding with a significant lower standard deviation for local (F‐test, P = 0.01). For all clustering methods, we found a large systematic underestimation of flow compared with 2D PCMRI (16.1–22.3 ml/min).

Data Conclusion: A locally adapted threshold value gives a more stable result compared with a globally fixed threshold. 4D flow with the proposed segmentation method has the potential to become a useful reliable clinical tool for assessment of blood flow in the major cerebral arteries.

Level of Evidence: 2

Technical Efficacy: Stage 2