| 1. |
- Dunås, Tora, et al.
(författare)
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4D flow MRI : automatic assessment of blood flow in cerebral arteries
- 2019
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Ingår i: Biomedical Engineering & Physics Express. - : Institute of Physics Publishing (IOPP). - 2057-1976. ; 5:1
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 2. |
- Dunås, Tora, et al.
(författare)
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A Stereotactic Probabilistic Atlas for the Major Cerebral Arteries
- 2017
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Ingår i: Neuroinformatics. - : Springer Science and Business Media LLC. - 1539-2791 .- 1559-0089. ; 15:1, s. 101-110
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 3. |
- Dunås, Tora, et al.
(författare)
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Accuracy of blood flow assessment in cerebral arteries with 4D flow MRI : Evaluation with three segmentation methods
- 2019
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Ingår i: Journal of Magnetic Resonance Imaging. - : Wiley. - 1053-1807 .- 1522-2586. ; 50:2, s. 511-518
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Tidskriftsartikel (refereegranskat)abstract
- 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: 2Technical Efficacy: Stage 2
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| 4. |
- Dunås, Tora, et al.
(författare)
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Automatic labeling of cerebral arteries in magnetic resonance angiography
- 2016
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Ingår i: Magnetic Resonance Materials in Physics, Biology and Medicine. - : Springer Science and Business Media LLC. - 0968-5243 .- 1352-8661. ; 29:1, s. 39-47
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 5. |
- Dunås, Tora, 1988-
(författare)
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Blood flow assessment in cerebral arteries with 4D flow magnetic resonance imaging : an automatic atlas-based approach
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 6. |
- Dunås, Tora, et al.
(författare)
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Multimodal Image Analysis of Apparent Brain Age Identifies Physical Fitness as Predictor of Brain Maintenance
- 2021
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Ingår i: Cerebral Cortex. - : Oxford University Press. - 1047-3211 .- 1460-2199. ; 31:7, s. 3393-3407
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Tidskriftsartikel (refereegranskat)abstract
- Maintaining a youthful brain structure and function throughout life may be the single most important determinant ofsuccessful cognitive aging. In this study, we addressed heterogeneity in brain aging by making image-based brain agepredictions and relating the brain age prediction gap (BAPG) to cognitive change in aging. Structural, functional, anddiffusion MRI scans from 351 participants were used to train and evaluate 5 single-modal and 4 multimodal predictionmodels, based on 7 regression methods. The models were compared on mean absolute error and whether they were relatedto physical fitness and cognitive ability, measured both currently and longitudinally, as well as study attrition and years ofeducation. Multimodal prediction models performed at a similar level as single-modal models, and the choice of regressionmethod did not significantly affect the results. Correlation with the BAPG was found for current physical fitness, currentcognitive ability, and study attrition. Correlations were also found for retrospective physical fitness, measured 10 years priorto imaging, and slope for cognitive ability during a period of 15 years. The results suggest that maintaining a high physicalfitness throughout life contributes to brain maintenance and preserved cognitive ability.
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| 7. |
- Dunås, Tora, et al.
(författare)
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Towards Automatic Identification of Cerebral Arteries in 4D Flow MRI
- 2015
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Ingår i: 16th Nordic-Baltic Conference on Biomedical Engineering. - Cham : Springer International Publishing. - 9783319129662 - 9783319129679 ; , s. 40-43
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Konferensbidrag (refereegranskat)abstract
- 4D flow MRI is a powerful imaging technique which provides an angiographic image with information about blood flow in a large volume, time resolved over the cardiac cycle, in a short imaging time. This study aims to develop an automatic method for identification of cerebral arteries. The proposed method is based on an atlas of twelve arteries, developed from 4D flow MRI of 25 subjects. The atlas was constructed by normalizing all images to MNI-space, manually identifying the arteries and creating an average over the volume. The identification is done by extracting a vascular skeleton from the image, transforming it to MNI-space, labeling it with the atlas and transforming it back to subject space. The method was evaluated on a pilot cohort of 8 subjects. The rate of correctly identified arteries was 97%.
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| 8. |
- Gryska, Emilia, 1992, et al.
(författare)
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Deep learning for automatic brain tumour segmentation on MRI: evaluation of recommended reporting criteria via a reproduction and replication study.
- 2022
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Ingår i: BMJ open. - : BMJ. - 2044-6055. ; 12:7
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Tidskriftsartikel (refereegranskat)abstract
- To determine the reproducibility and replicability of studies that develop and validate segmentation methods for brain tumours on MRI and that follow established reproducibility criteria; and to evaluate whether the reporting guidelines are sufficient.Two eligible validation studies of distinct deep learning (DL) methods were identified. We implemented the methods using published information and retraced the reported validation steps. We evaluated to what extent the description of the methods enabled reproduction of the results. We further attempted to replicate reported findings on a clinical set of images acquired at our institute consisting of high-grade and low-grade glioma (HGG, LGG), and meningioma (MNG) cases.We successfully reproduced one of the two tumour segmentation methods. Insufficient description of the preprocessing pipeline and our inability to replicate the pipeline resulted in failure to reproduce the second method. The replication of the first method showed promising results in terms of Dice similarity coefficient (DSC) and sensitivity (Sen) on HGG cases (DSC=0.77, Sen=0.88) and LGG cases (DSC=0.73, Sen=0.83), however, poorer performance was observed for MNG cases (DSC=0.61, Sen=0.71). Preprocessing errors were identified that contributed to low quantitative scores in some cases.Established reproducibility criteria do not sufficiently emphasise description of the preprocessing pipeline. Discrepancies in preprocessing as a result of insufficient reporting are likely to influence segmentation outcomes and hinder clinical utilisation. A detailed description of the whole processing chain, including preprocessing, is thus necessary to obtain stronger evidence of the generalisability of DL-based brain tumour segmentation methods and to facilitate translation of the methods into clinical practice.
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| 9. |
- Helland, Ragnhild Holden, et al.
(författare)
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Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks.
- 2023
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Ingår i: Scientific reports. - 2045-2322. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- Extent of resection after surgery is one of the main prognostic factors for patients diagnosed with glioblastoma. To achieve this, accurate segmentation and classification of residual tumor from post-operative MR images is essential. The current standard method for estimating it is subject to high inter- and intra-rater variability, and an automated method for segmentation of residual tumor in early post-operative MRI could lead to a more accurate estimation of extent of resection. In this study, two state-of-the-art neural network architectures for pre-operative segmentation were trained for the task. The models were extensively validated on a multicenter dataset with nearly 1000 patients, from 12 hospitals in Europe and the United States. The best performance achieved was a 61% Dice score, and the best classification performance was about 80% balanced accuracy, with a demonstrated ability to generalize across hospitals. In addition, the segmentation performance of the best models was on par with human expert raters. The predicted segmentations can be used to accurately classify the patients into those with residual tumor, and those with gross total resection.
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| 10. |
- Holmgren, Madelene, et al.
(författare)
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Assessment of Cerebral Blood Flow Pulsatility and Cerebral Arterial Compliance With 4D Flow MRI
- 2020
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Ingår i: Journal of Magnetic Resonance Imaging. - : Wiley-Blackwell. - 1053-1807 .- 1522-2586. ; 51:5, s. 1516-1525
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Four-dimensional flow magnetic resonance imaging (4D flow MRI) enables efficient investigation of cerebral blood flow pulsatility in the cerebral arteries. This is important for exploring hemodynamic mechanisms behind vascular diseases associated with arterial pulsations.PURPOSE: To investigate the feasibility of pulsatility assessments with 4D flow MRI, its agreement with reference two-dimensional phase-contrast MRI (2D PC-MRI) measurements, and to demonstrate how 4D flow MRI can be used to assess cerebral arterial compliance and cerebrovascular resistance in major cerebral arteries.STUDY TYPE: Prospective.SUBJECTS: Thirty-five subjects (20 women, 79 ± 5 years, range 70-91 years).FIELD STRENGTH/SEQUENCE: 4D flow MRI (PC-VIPR) and 2D PC-MRI acquired with a 3T scanner.ASSESSMENT: Time-resolved flow was assessed in nine cerebral arteries. From the pulsatile flow waveform in each artery, amplitude (ΔQ), volume load (ΔV), and pulsatility index (PI) were calculated. To reduce high-frequency noise in the 4D flow MRI data, the flow waveforms were low-pass filtered. From the total cerebral blood flow, total PI (PItot ), total volume load (ΔVtot ), cerebral arterial compliance (C), and cerebrovascular resistance (R) were calculated.STATISTICAL TESTS: Two-tailed paired t-test, intraclass correlation (ICC).RESULTS: There was no difference in ΔQ between 4D flow MRI and the reference (0.00 ± 0.022 ml/s, mean ± SEM, P = 0.97, ICC = 0.95, n = 310) with a cutoff frequency of 1.9 Hz and 15 cut plane long arterial segments. For ΔV, the difference was -0.006 ± 0.003 ml (mean ± SEM, P = 0.07, ICC = 0.93, n = 310) without filtering. Total R was 11.4 ± 2.41 mmHg/(ml/s) (mean ± SD) and C was 0.021 ± 0.009 ml/mmHg (mean ± SD). ΔVtot was 1.21 ± 0.29 ml (mean ± SD) with an ICC of 0.82 compared with the reference. PItot was 1.08 ± 0.21 (mean ± SD).DATA CONCLUSION: We successfully assessed 4D flow MRI cerebral arterial pulsatility, cerebral arterial compliance, and cerebrovascular resistance. Averaging of multiple cut planes and low-pass filtering was necessary to assess accurate peak-to-peak features in the flow rate waveforms.LEVEL OF EVIDENCE: 2Technical Efficacy Stage: 2
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