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Chen T, You W, Zhang L, Ye W, Feng J, Lu J, Lv J, Tang Y, Wei D, Gui S, Jiang J, Wang Z, Wang Y, Zhao Q, Zhang Y, Qu J, Li C, Jiang Y, Zhang X, Li Y, Guan S. Automated anatomical labeling of the intracranial arteries via deep learning in computed tomography angiography. Front Physiol 2024; 14:1310357. [PMID: 38239880 PMCID: PMC10794642 DOI: 10.3389/fphys.2023.1310357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 11/28/2023] [Indexed: 01/22/2024] Open
Abstract
Background and purpose: Anatomical labeling of the cerebral vasculature is a crucial topic in determining the morphological nature and characterizing the vital variations of vessels, yet precise labeling of the intracranial arteries is time-consuming and challenging, given anatomical structural variability and surging imaging data. We present a U-Net-based deep learning (DL) model to automatically label detailed anatomical segments in computed tomography angiography (CTA) for the first time. The trained DL algorithm was further tested on a clinically relevant set for the localization of intracranial aneurysms (IAs). Methods: 457 examinations with varying degrees of arterial stenosis were used to train, validate, and test the model, aiming to automatically label 42 segments of the intracranial arteries [e.g., 7 segments of the internal carotid artery (ICA)]. Evaluation metrics included Dice similarity coefficient (DSC), mean surface distance (MSD), and Hausdorff distance (HD). Additionally, 96 examinations containing at least one IA were enrolled to assess the model's potential in enhancing clinicians' precision in IA localization. A total of 5 clinicians with different experience levels participated as readers in the clinical experiment and identified the precise location of IA without and with algorithm assistance, where there was a washout period of 14 days between two interpretations. The diagnostic accuracy, time, and mean interrater agreement (Fleiss' Kappa) were calculated to assess the differences in clinical performance of clinicians. Results: The proposed model exhibited notable labeling performance on 42 segments that included 7 anatomical segments of ICA, with the mean DSC of 0.88, MSD of 0.82 mm and HD of 6.59 mm. Furthermore, the model demonstrated superior labeling performance in healthy subjects compared to patients with stenosis (DSC: 0.91 vs. 0.89, p < 0.05; HD: 4.75 vs. 6.19, p < 0.05). Concurrently, clinicians with model predictions achieved significant improvements when interpreting the precise location of IA. The clinicians' mean accuracy increased by 0.04 (p = 0.003), mean time to diagnosis reduced by 9.76 s (p < 0.001), and mean interrater agreement (Fleiss' Kappa) increased by 0.07 (p = 0.029). Conclusion: Our model stands proficient for labeling intracranial arteries using the largest CTA dataset. Crucially, it demonstrates clinical utility, helping prioritize the patients with high risks and ease clinical workload.
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Affiliation(s)
- Ting Chen
- Department of Interventional Neuroradiology, Beijing Tiantan Hospital and Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- School of Biomedical Engineering, Capital Medical University, Beijing, China
| | - Wei You
- Department of Interventional Neuroradiology, Beijing Tiantan Hospital and Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Department of Neurointerventional Engineering and Technology, Beijing Engineering Research Center (NO: BG0287), Beijing, China
| | - Liyuan Zhang
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Wanxing Ye
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Junqiang Feng
- Department of Neurosurgery, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - Jing Lu
- Department of Radiology, Third Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Jian Lv
- Department of Interventional Neuroradiology, Beijing Tiantan Hospital and Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Yudi Tang
- Department of Interventional Neuroradiology, Beijing Tiantan Hospital and Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Dachao Wei
- Department of Interventional Neuroradiology, Beijing Tiantan Hospital and Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Siming Gui
- Department of Interventional Neuroradiology, Beijing Tiantan Hospital and Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Jia Jiang
- Department of Interventional Neuroradiology, Beijing Tiantan Hospital and Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Ziyao Wang
- Department of Interventional Neuroradiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Yanwen Wang
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Qi Zhao
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Yifan Zhang
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Junda Qu
- School of Biomedical Engineering, Capital Medical University, Beijing, China
- Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China
| | - Chunlin Li
- School of Biomedical Engineering, Capital Medical University, Beijing, China
- Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China
| | - Yuhua Jiang
- Department of Interventional Neuroradiology, Beijing Tiantan Hospital and Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Xu Zhang
- School of Biomedical Engineering, Capital Medical University, Beijing, China
- Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China
| | - Youxiang Li
- Department of Interventional Neuroradiology, Beijing Tiantan Hospital and Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- School of Biomedical Engineering, Capital Medical University, Beijing, China
- Department of Neurointerventional Engineering and Technology, Beijing Engineering Research Center (NO: BG0287), Beijing, China
| | - Sheng Guan
- Department of Interventional Neuroradiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
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2
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Canals P, Balocco S, Díaz O, Li J, García-Tornel A, Tomasello A, Olivé-Gadea M, Ribó M. A fully automatic method for vascular tortuosity feature extraction in the supra-aortic region: unraveling possibilities in stroke treatment planning. Comput Med Imaging Graph 2023; 104:102170. [PMID: 36634467 DOI: 10.1016/j.compmedimag.2022.102170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 11/14/2022] [Accepted: 12/24/2022] [Indexed: 12/29/2022]
Abstract
Vascular tortuosity of supra-aortic vessels is widely considered one of the main reasons for failure and delays in endovascular treatment of large vessel occlusion in patients with acute ischemic stroke. Characterization of tortuosity is a challenging task due to the lack of objective, robust and effective analysis tools. We present a fully automatic method for arterial segmentation, vessel labelling and tortuosity feature extraction applied to the supra-aortic region. A sample of 566 computed tomography angiography scans from acute ischemic stroke patients (aged 74.8 ± 12.9, 51.0% females) were used for training, validation and testing of a segmentation module based on a U-Net architecture (162 cases) and a vessel labelling module powered by a graph U-Net (566 cases). Successively, 30 cases were processed for testing of a tortuosity feature extraction module. Measurements obtained through automatic processing were compared to manual annotations from two observers for a thorough validation of the method. The proposed feature extraction method presented similar performance to the inter-rater variability observed in the measurement of 33 geometrical and morphological features of the arterial anatomy in the supra-aortic region. This system will contribute to the development of more complex models to advance the treatment of stroke by adding immediate automation, objectivity, repeatability and robustness to the vascular tortuosity characterization of patients.
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Affiliation(s)
- P Canals
- Stroke Unit, Neurology, Hospital Vall d'Hebron, Barcelona, Spain; Departament de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain.
| | - S Balocco
- Department of Mathematics and Computer Science, University of Barcelona, Barcelona, Spain; Computer Vision Center, Bellaterra, Spain
| | - O Díaz
- Department of Mathematics and Computer Science, University of Barcelona, Barcelona, Spain
| | - J Li
- Stroke Unit, Neurology, Hospital Vall d'Hebron, Barcelona, Spain; Departament de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - A García-Tornel
- Stroke Unit, Neurology, Hospital Vall d'Hebron, Barcelona, Spain; Departament de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - A Tomasello
- Neuroradiology, Vall d'Hebron Hospital Universitari, Barcelona, Spain
| | - M Olivé-Gadea
- Stroke Unit, Neurology, Hospital Vall d'Hebron, Barcelona, Spain; Departament de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - M Ribó
- Stroke Unit, Neurology, Hospital Vall d'Hebron, Barcelona, Spain; Departament de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain
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3
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Hong SW, Song HN, Choi JU, Cho HH, Baek IY, Lee JE, Kim YC, Chung D, Chung JW, Bang OY, Kim GM, Park HJ, Liebeskind DS, Seo WK. Automated in-depth cerebral arterial labelling using cerebrovascular vasculature reframing and deep neural networks. Sci Rep 2023; 13:3255. [PMID: 36828857 PMCID: PMC9957982 DOI: 10.1038/s41598-023-30234-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Accepted: 02/20/2023] [Indexed: 02/26/2023] Open
Abstract
Identifying the cerebral arterial branches is essential for undertaking a computational approach to cerebrovascular imaging. However, the complexity and inter-individual differences involved in this process have not been thoroughly studied. We used machine learning to examine the anatomical profile of the cerebral arterial tree. The method is less sensitive to inter-subject and cohort-wise anatomical variations and exhibits robust performance with an unprecedented in-depth vessel range. We applied machine learning algorithms to disease-free healthy control subjects (n = 42), patients with stroke with intracranial atherosclerosis (ICAS) (n = 46), and patients with stroke mixed with the existing controls (n = 69). We trained and tested 70% and 30% of each study cohort, respectively, incorporating spatial coordinates and geometric vessel feature vectors. Cerebral arterial images were analyzed based on the 'segmentation-stacking' method using magnetic resonance angiography. We precisely classified the cerebral arteries across the exhaustive scope of vessel components using advanced geometric characterization, redefinition of vessel unit conception, and post-processing algorithms. We verified that the neural network ensemble, with multiple joint models as the combined predictor, classified all vessel component types independent of inter-subject variations in cerebral arterial anatomy. The validity of the categorization performance of the model was tested, considering the control, ICAS, and control-blended stroke cohorts, using the area under the receiver operating characteristic (ROC) curve and precision-recall curve. The classification accuracy rarely fell outside each image's 90-99% scope, independent of cohort-dependent cerebrovascular structural variations. The classification ensemble was calibrated with high overall area rates under the ROC curve of 0.99-1.00 [0.97-1.00] in the test set across various study cohorts. Identifying an all-inclusive range of vessel components across controls, ICAS, and stroke patients, the accuracy rates of the prediction were: internal carotid arteries, 91-100%; middle cerebral arteries, 82-98%; anterior cerebral arteries, 88-100%; posterior cerebral arteries, 87-100%; and collections of superior, anterior inferior, and posterior inferior cerebellar arteries, 90-99% in the chunk-level classification. Using a voting algorithm on the queued classified vessel factors and anatomically post-processing the automatically classified results intensified quantitative prediction performance. We employed stochastic clustering and deep neural network ensembles. Ma-chine intelligence-assisted prediction of vessel structure allowed us to personalize quantitative predictions of various types of cerebral arterial structures, contributing to precise and efficient decisions regarding the cerebrovascular disease.
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Affiliation(s)
- Suk-Woo Hong
- Department of Neurology and Stroke Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Korea
- Program in Brain Science, College of Natural Sciences, Seoul National University, Seoul, 08826, Korea
| | - Ha-Na Song
- Department of Neurology and Stroke Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Korea
| | - Jong-Un Choi
- Department of Neurology and Stroke Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Korea
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Irwon-dong, Gangnam-gu, Seoul, 06351, Korea
| | - Hwan-Ho Cho
- Department of Medical Artificial Intelligence, Konyang University, Daejeon, Korea
| | - In-Young Baek
- Department of Neurology and Stroke Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Korea
| | - Ji-Eun Lee
- Department of Neurology and Stroke Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Korea
| | - Yoon-Chul Kim
- Division of Digital Healthcare, Yonsei University Mirae Campus, Wonju, 26493, Korea
| | - Darda Chung
- Department of Neurology and Stroke Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Korea
| | - Jong-Won Chung
- Department of Neurology and Stroke Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Korea
| | - Oh-Young Bang
- Department of Neurology and Stroke Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Korea
| | - Gyeong-Moon Kim
- Department of Neurology and Stroke Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Korea
| | - Hyun-Jin Park
- Department of Electronic Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, 16419, Korea
| | - David S Liebeskind
- Department of Neurology and Comprehensive Stroke Center, UCLA, Los Angeles, CA, USA
| | - Woo-Keun Seo
- Department of Neurology and Stroke Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Korea.
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Irwon-dong, Gangnam-gu, Seoul, 06351, Korea.
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4
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Hilbert A, Rieger J, Madai VI, Akay EM, Aydin OU, Behland J, Khalil AA, Galinovic I, Sobesky J, Fiebach J, Livne M, Frey D. Anatomical labeling of intracranial arteries with deep learning in patients with cerebrovascular disease. Front Neurol 2022; 13:1000914. [PMID: 36341105 PMCID: PMC9634733 DOI: 10.3389/fneur.2022.1000914] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 09/22/2022] [Indexed: 11/21/2022] Open
Abstract
Brain arteries are routinely imaged in the clinical setting by various modalities, e.g., time-of-flight magnetic resonance angiography (TOF-MRA). These imaging techniques have great potential for the diagnosis of cerebrovascular disease, disease progression, and response to treatment. Currently, however, only qualitative assessment is implemented in clinical applications, relying on visual inspection. While manual or semi-automated approaches for quantification exist, such solutions are impractical in the clinical setting as they are time-consuming, involve too many processing steps, and/or neglect image intensity information. In this study, we present a deep learning-based solution for the anatomical labeling of intracranial arteries that utilizes complete information from 3D TOF-MRA images. We adapted and trained a state-of-the-art multi-scale Unet architecture using imaging data of 242 patients with cerebrovascular disease to distinguish 24 arterial segments. The proposed model utilizes vessel-specific information as well as raw image intensity information, and can thus take tissue characteristics into account. Our method yielded a performance of 0.89 macro F1 and 0.90 balanced class accuracy (bAcc) in labeling aggregated segments and 0.80 macro F1 and 0.83 bAcc in labeling detailed arterial segments on average. In particular, a higher F1 score than 0.75 for most arteries of clinical interest for cerebrovascular disease was achieved, with higher than 0.90 F1 scores in the larger, main arteries. Due to minimal pre-processing, simple usability, and fast predictions, our method could be highly applicable in the clinical setting.
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Affiliation(s)
- Adam Hilbert
- Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
- *Correspondence: Adam Hilbert
| | - Jana Rieger
- Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Vince I. Madai
- Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
- Quality | Ethics | Open Science | Translation Center for Transforming Biomedical Research, Berlin Institute of Health (BIH), Charité Universitätsmedizin Berlin, Berlin, Germany
- Faculty of Computing, Engineering and the Built Environment, School of Computing and Digital Technology, Birmingham City University, Birmingham, United Kingdom
| | - Ela M. Akay
- Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Orhun U. Aydin
- Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Jonas Behland
- Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Ahmed A. Khalil
- Centre for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Berlin, Germany
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Mind, Brain, Body Institute, Berlin School of Mind and Brain, Humboldt-Universität Berlin, Berlin, Germany
- Biomedical Innovation Academy, Berlin Institute of Health, Berlin, Germany
| | - Ivana Galinovic
- Centre for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Jan Sobesky
- Centre for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Berlin, Germany
- Department of Neurology, Johanna-Etienne-Hospital, Neuss, Germany
| | - Jochen Fiebach
- Centre for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Michelle Livne
- Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Dietmar Frey
- Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
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5
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Zhu Y, Qian P, Zhao Z, Zeng Z. Deep Feature Fusion via Graph Convolutional Network for Intracranial Artery Labeling. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:467-470. [PMID: 36086340 DOI: 10.1109/embc48229.2022.9871848] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Intracranial arteries are critical blood vessels that supply the brain with oxygenated blood. Intracranial artery labels provide valuable guidance and navigation to numerous clinical applications and disease diagnoses. Various machine learning algorithms have been carried out for automation in the anatomical labeling of cerebral arteries. However, the task remains challenging because of the high complexity and variations of intracranial arteries. This study investigates a novel graph convolutional neural network with deep feature fusion for cerebral artery labeling. We introduce stacked graph convolutions in an encoder-core-decoder architecture, extracting high-level representations from graph nodes and their neighbors. Furthermore, we efficiently aggregate intermediate features from different hierarchies to enhance the proposed model's representation capability and labeling performance. We perform extensive experiments on public datasets, in which the results prove the superiority of our approach over baselines by a clear margin. Clinical relevance- The graph convolutions and feature fusion in our approach effectively extract graph information, which provides more accurate intracranial artery label predictions than existing methods and better facilitates medical research and disease diagnosis.
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6
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Abstract
Alterations in cerebral blood flow are common in several neurological diseases among the elderly including stroke, cerebral small vessel disease, vascular dementia, and Alzheimer's disease. 4D flow magnetic resonance imaging (MRI) is a relatively new technique to investigate cerebrovascular disease, and makes it possible to obtain time-resolved blood flow measurements of the entire cerebral arterial venous vasculature and can be used to derive a repertoire of hemodynamic biomarkers indicative of cerebrovascular health. The information that can be obtained from one single 4D flow MRI scan allows both the investigation of aberrant flow patterns at a focal location in the vasculature as well as estimations of brain-wide disturbances in blood flow. Such focal and global hemodynamic biomarkers show the potential of being sensitive to impending cerebrovascular disease and disease progression and can also become useful during planning and follow-up of interventions aiming to restore a normal cerebral circulation. Here, we describe 4D flow MRI approaches for analyzing the cerebral vasculature. We then survey key hemodynamic biomarkers that can be reliably assessed using the technique. Finally, we highlight cerebrovascular diseases where one or multiple hemodynamic biomarkers are of central interest.
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Affiliation(s)
- Anders Wåhlin
- Department of Radiation Sciences, Umeå University, Umeå, Sweden.,Department of Applied Physics and Electronics, Umeå University, Umeå, Sweden.,Umeå Center for Functional Brain Imaging (UFBI), Umeå University, Umeå, Sweden
| | - Anders Eklund
- Department of Radiation Sciences, Umeå University, Umeå, Sweden.,Umeå Center for Functional Brain Imaging (UFBI), Umeå University, Umeå, Sweden
| | - Jan Malm
- Department of Clinical Science and Neurosciences, Umeå University, Umeå, Sweden
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7
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Morgan AG, Thrippleton MJ, Wardlaw JM, Marshall I. 4D flow MRI for non-invasive measurement of blood flow in the brain: A systematic review. J Cereb Blood Flow Metab 2021; 41:206-218. [PMID: 32936731 PMCID: PMC8369999 DOI: 10.1177/0271678x20952014] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Revised: 06/22/2020] [Accepted: 07/05/2020] [Indexed: 01/25/2023]
Abstract
The brain's vasculature is essential for brain health and its dysfunction contributes to the onset and development of many dementias and neurological disorders. While numerous in vivo imaging techniques exist to investigate cerebral haemodynamics in humans, phase-contrast magnetic resonance imaging (MRI) has emerged as a reliable, non-invasive method of quantifying blood flow within intracranial vessels. In recent years, an advanced form of this method, known as 4D flow, has been developed and utilised in patient studies, where its ability to capture complex blood flow dynamics within any major vessel across the acquired volume has proved effective in collecting large amounts of information in a single scan. While extremely promising as a method of examining the vascular system's role in brain-related diseases, the collection of 4D data can be time-consuming, meaning data quality has to be traded off against the acquisition time. Here, we review the available literature to examine 4D flow's capabilities in assessing physiological and pathological features of the cerebrovascular system. Emerging techniques such as dynamic velocity-encoding and advanced undersampling methods, combined with increasingly high-field MRI scanners, are likely to bring 4D flow to the forefront of cerebrovascular imaging studies in the years to come.
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Affiliation(s)
- Alasdair G Morgan
- Brain Research Imaging Centre, Centre for Clinical Brain
Sciences, University of Edinburgh, Edinburgh, UK
- UK Dementia Research Institute at The University of Edinburgh,
Edinburgh Medical School, Edinburgh, UK
| | - Michael J Thrippleton
- Brain Research Imaging Centre, Centre for Clinical Brain
Sciences, University of Edinburgh, Edinburgh, UK
- UK Dementia Research Institute at The University of Edinburgh,
Edinburgh Medical School, Edinburgh, UK
| | - Joanna M Wardlaw
- Brain Research Imaging Centre, Centre for Clinical Brain
Sciences, University of Edinburgh, Edinburgh, UK
- UK Dementia Research Institute at The University of Edinburgh,
Edinburgh Medical School, Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology,
University of Edinburgh, Edinburgh, UK
| | - Ian Marshall
- Brain Research Imaging Centre, Centre for Clinical Brain
Sciences, University of Edinburgh, Edinburgh, UK
- UK Dementia Research Institute at The University of Edinburgh,
Edinburgh Medical School, Edinburgh, UK
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8
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Abstract
Human brain atlases have been evolving tremendously, propelled recently by brain big projects, and driven by sophisticated imaging techniques, advanced brain mapping methods, vast data, analytical strategies, and powerful computing. We overview here this evolution in four categories: content, applications, functionality, and availability, in contrast to other works limited mostly to content. Four atlas generations are distinguished: early cortical maps, print stereotactic atlases, early digital atlases, and advanced brain atlas platforms, and 5 avenues in electronic atlases spanning the last two generations. Content-wise, new electronic atlases are categorized into eight groups considering their scope, parcellation, modality, plurality, scale, ethnicity, abnormality, and a mixture of them. Atlas content developments in these groups are heading in 23 various directions. Application-wise, we overview atlases in neuroeducation, research, and clinics, including stereotactic and functional neurosurgery, neuroradiology, neurology, and stroke. Functionality-wise, tools and functionalities are addressed for atlas creation, navigation, individualization, enabling operations, and application-specific. Availability is discussed in media and platforms, ranging from mobile solutions to leading-edge supercomputers, with three accessibility levels. The major application-wise shift has been from research to clinical practice, particularly in stereotactic and functional neurosurgery, although clinical applications are still lagging behind the atlas content progress. Atlas functionality also has been relatively neglected until recently, as the management of brain data explosion requires powerful tools. We suggest that the future human brain atlas-related research and development activities shall be founded on and benefit from a standard framework containing the core virtual brain model cum the brain atlas platform general architecture.
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Affiliation(s)
- Wieslaw L Nowinski
- John Paul II Center for Virtual Anatomy and Surgical Simulation, University of Cardinal Stefan Wyszynski, Woycickiego 1/3, Block 12, room 1220, 01-938, Warsaw, Poland.
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9
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Birnefeld J, Wåhlin A, Eklund A, Malm J. Cerebral arterial pulsatility is associated with features of small vessel disease in patients with acute stroke and TIA: a 4D flow MRI study. J Neurol 2019; 267:721-730. [PMID: 31728712 PMCID: PMC7035303 DOI: 10.1007/s00415-019-09620-6] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Revised: 10/30/2019] [Accepted: 11/02/2019] [Indexed: 11/16/2022]
Abstract
Cerebral small vessel disease (SVD) is a major cause of stroke and cognitive impairment. However, the underlying mechanisms behind SVD are still poorly understood. High cerebral arterial pulsatility has been suggested as a possible cause of SVD. In population studies, arterial pulsatility has been linked to white matter hyperintensities (WMH), cerebral atrophy, and cognitive impairment, all features of SVD. In stroke, pulsatility data are scarce and contradictory. The aim of this study was to investigate the relationship between arterial pulsatility and SVD in stroke patients. With a cross-sectional design, 89 patients with acute ischemic stroke or TIA were examined with MRI. A neuropsychological assessment was performed 1 year later. Using 4D flow MRI, pulsatile indices (PI) were calculated for the internal carotid artery (ICA) and middle cerebral artery (M1, M3). Flow volume pulsatility (FVP), a measure corresponding to the cyclic expansion of the arterial tree, was calculated for the same locations. These parameters were assessed for associations with WMH volume, brain volume and cognitive function. ICA-FVP was associated with WMH volume (β = 1.67, 95% CI: [0.1, 3.24], p = 0.037). M1-PI and M1-FVP were associated with decreasing cognitive function (β = − 4.4, 95% CI: [− 7.7, − 1.1], p = 0.009 and β = − 13.15, 95% CI: [− 24.26, − 2.04], p = 0.02 respectively). In summary, this supports an association between arterial pulsatility and SVD in stroke patients, and provides a potential target for further research and preventative treatment. FVP may become a useful biomarker for assessing pulsatile stress with PCMRI and 4D flow MRI.
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Affiliation(s)
- Johan Birnefeld
- Department of Pharmacology and Clinical Neuroscience, Umeå University, 90187, Umeå, Sweden.
| | - Anders Wåhlin
- Department of Radiation Sciences, Umeå University, Umeå, Sweden.,Umeå Centre for Functional Brain Imaging, Umeå University, Umeå, Sweden
| | - Anders Eklund
- Department of Radiation Sciences, Umeå University, Umeå, Sweden.,Centre for Biomedical Engineering and Physics, Umeå University, Umeå, Sweden
| | - Jan Malm
- Department of Pharmacology and Clinical Neuroscience, Umeå University, 90187, Umeå, Sweden
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10
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Dunås T, Holmgren M, Wåhlin A, Malm J, Eklund A. Accuracy of blood flow assessment in cerebral arteries with 4D flow MRI: Evaluation with three segmentation methods. J Magn Reson Imaging 2019; 50:511-518. [PMID: 30637846 PMCID: PMC6767555 DOI: 10.1002/jmri.26641] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2018] [Revised: 12/20/2018] [Accepted: 12/20/2018] [Indexed: 12/03/2022] Open
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: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:511–518.
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Affiliation(s)
- Tora Dunås
- Department of Radiation Sciences, Umeå University, Umeå, Sweden
| | | | - Anders Wåhlin
- Department of Radiation Sciences, Umeå University, Umeå, Sweden.,Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden
| | - Jan Malm
- Department of Pharmacology and Clinical Neuroscience, Umeå University, Umeå, Sweden
| | - Anders Eklund
- Department of Radiation Sciences, Umeå University, Umeå, Sweden.,Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden
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Dunås T, Wåhlin A, Zarrinkoob L, Malm J, Eklund A. 4D flow MRI—Automatic assessment of blood flow in cerebral arteries. Biomed Phys Eng Express 2018. [DOI: 10.1088/2057-1976/aae8d1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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12
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Bernier M, Cunnane SC, Whittingstall K. The morphology of the human cerebrovascular system. Hum Brain Mapp 2018; 39:4962-4975. [PMID: 30265762 DOI: 10.1002/hbm.24337] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Revised: 07/02/2018] [Accepted: 07/19/2018] [Indexed: 12/13/2022] Open
Abstract
While several methodologies exist for quantifying gray and white matter properties in humans, relatively little is known regarding the spatial organization and the intersubject variability of cerebral vessels. To resolve this, we developed a fast, open-source processing algorithm using advanced vessel segmentation schemes and iterative nonlinear registration to isolate, extract, and quantify cerebral vessels in susceptibility weighting imaging (SWI) and time-of-flight angiography (TOF-MRA) datasets acquired in a large cohort (n = 42) of healthy individuals. From this, whole-brain venous and arterial probabilistic maps were generated along with the computation of regional densities and diameters within regions based on popular anatomical and functional atlases. The results show that cerebral vasculature is highly heterogeneous, displaying disproportionally large vessel densities in brain areas such as the anterior and posterior cingulate, cuneus, precuneus, parahippocampus, insula, and temporal gyri. On average, venous densities were slightly higher and less variable across subjects than arterial. Moreover, regional variations in both venous and arterial density were significantly correlated to cortical thickness (R = 0.42). This publicly available new atlas of the human cerebrovascular system provides a first step toward quantifying morphological changes in the diseased brain and serving as a potential regression tool in fMRI analysis.
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Affiliation(s)
- Michaël Bernier
- Department of Nuclear Medicine and Radiobiology, Université de Sherbrooke, Sherbrooke, Québec, Canada
| | - Stephen C Cunnane
- Department of Medicine, Université de Sherbrooke, Sherbrooke, Québec, Canada.,Department of Pharmacology and Physiology, Université de Sherbrooke, Sherbrooke, Québec, Canada.,Research Center on Aging, Université de Sherbrooke, Sherbrooke, Québec, Canada
| | - Kevin Whittingstall
- Department of Radiology, Université de Sherbrooke, Sherbrooke, Québec, Canada.,CR-CHUS, Université de Sherbrooke, Sherbrooke, Québec, Canada
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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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