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Ahmed R, Al Shehhi A, Werghi N, Seghier ML. Segmentation of stroke lesions using transformers-augmented MRI analysis. Hum Brain Mapp 2024; 45:e26803. [PMID: 39119860 PMCID: PMC11310771 DOI: 10.1002/hbm.26803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 05/20/2024] [Accepted: 07/17/2024] [Indexed: 08/10/2024] Open
Abstract
Accurate segmentation of chronic stroke lesions from mono-spectral magnetic resonance imaging scans (e.g., T1-weighted images) is a difficult task due to the arbitrary shape, complex texture, variable size and intensities, and varied locations of the lesions. Due to this inherent spatial heterogeneity, existing machine learning methods have shown moderate performance for chronic lesion delineation. In this study, we introduced: (1) a method that integrates transformers' deformable feature attention mechanism with convolutional deep learning architecture to improve the accuracy and generalizability of stroke lesion segmentation, and (2) an ecological data augmentation technique based on inserting real lesions into intact brain regions. Our combination of these two approaches resulted in a significant increase in segmentation performance, with a Dice index of 0.82 (±0.39), outperforming the existing methods trained and tested on the same Anatomical Tracings of Lesions After Stroke (ATLAS) 2022 dataset. Our method performed relatively well even for cases with small stroke lesions. We validated the robustness of our method through an ablation study and by testing it on new unseen brain scans from the Ischemic Stroke Lesion Segmentation (ISLES) 2015 dataset. Overall, our proposed approach of transformers with ecological data augmentation offers a robust way to delineate chronic stroke lesions with clinically relevant accuracy. Our method can be extended to other challenging tasks that require automated detection and segmentation of diverse brain abnormalities from clinical scans.
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Affiliation(s)
- Ramsha Ahmed
- Department of Biomedical Engineering and BiotechnologyKhalifa University of Science and TechnologyAbu DhabiUAE
| | - Aamna Al Shehhi
- Department of Biomedical Engineering and BiotechnologyKhalifa University of Science and TechnologyAbu DhabiUAE
- Healthcare Engineering Innovation Center (HEIC)Khalifa University of Science and TechnologyAbu DhabiUAE
| | - Naoufel Werghi
- Department of Computer ScienceKhalifa University of Science and TechnologyAbu DhabiUAE
| | - Mohamed L. Seghier
- Department of Biomedical Engineering and BiotechnologyKhalifa University of Science and TechnologyAbu DhabiUAE
- Healthcare Engineering Innovation Center (HEIC)Khalifa University of Science and TechnologyAbu DhabiUAE
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Wright AM, Xu T, Ingram J, Koo J, Zhao Y, Tong Y, Wen Q. Robust data-driven segmentation of pulsatile cerebral vessels using functional magnetic resonance imaging. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.17.603932. [PMID: 39091755 PMCID: PMC11290998 DOI: 10.1101/2024.07.17.603932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
Functional magnetic resonance imaging (fMRI) captures rich physiological and neuronal information that can offer insights into neurofluid dynamics, vascular health, and waste clearance function. The availability of cerebral vessel segmentation could facilitate fluid dynamics research in fMRI. However, without magnetic resonance angiography scans, cerebral vessel segmentation is challenging and time-consuming. This study leverages cardiac-induced pulsatile fMRI signal to develop a data-driven, automatic segmentation of large cerebral arteries and the superior sagittal sinus (SSS). The method was validated in a local dataset by comparing it to ground truth cerebral artery and SSS segmentations. Using the Human Connectome Project (HCP) aging dataset, the method's reproducibility was tested on 422 participants aged 36 to 100 years, each with four repeated fMRI scans. The method demonstrated high reproducibility, with an intraclass correlation coefficient > 0.7 in both cerebral artery and SSS segmentation volumes. This study demonstrates that the large cerebral arteries and SSS can be reproducibly and automatically segmented in fMRI datasets, facilitating the investigation of fluid dynamics in these regions.
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Chou YC, Payne S. Static and dynamic analysis of cerebral blood flow in fifty-six large arterial vessel networks. Physiol Meas 2024; 45:075004. [PMID: 38917841 DOI: 10.1088/1361-6579/ad5bbb] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 06/25/2024] [Indexed: 06/27/2024]
Abstract
Objective.The cerebral vasculature is formed of an intricate network of blood vessels over many different length scales. Changes in their structure and connection are implicated in multiple cerebrovascular and neurological disorders. In this study, we present a novel approach to the quantitative analysis of the cerebral macrovasculature using computational and mathematical tools in a large dataset.Approach.We analysed a publicly available vessel dataset from a cohort of 56 (32/24F/M) healthy subjects. This dataset includes digital reconstructions of human brain macrovasculatures. We then propose a new mathematical model to compute blood flow dynamics and pressure distributions within these 56-representative cerebral macrovasculatures and quantify the results across this cohort.Main results.Statistical analysis showed that the steady state level of cerebrovascular resistance (CVR) gradually increases with age in both men and women. These age-related changes in CVR are in good agreement with previously reported values. All subjects were found to have only small phase angles (<6°) between blood pressure and blood flow at the cardiac frequency.Significance.These results showed that the dynamic component of blood flow adds very little phase shift at the cardiac frequency, which implies that the cerebral macrocirculation can be regarded as close to steady state in its behaviour, at least in healthy populations, irrespective of age or sex. This implies that the phase shift observed in measurements of blood flow in cerebral vessels is caused by behaviour further down the vascular bed. This behaviour is important for future statistical models of the dynamic maintenance of oxygen and nutrient supply to the brain.
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Affiliation(s)
- Yuan-Chung Chou
- Institute of Applied Mechanics, National Taiwan University, Taipei, Taiwan
| | - Stephen Payne
- Institute of Applied Mechanics, National Taiwan University, Taipei, Taiwan
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Gogulski J, Cline CC, Ross JM, Parmigiani S, Keller CJ. Reliability of the TMS-evoked potential in dorsolateral prefrontal cortex. Cereb Cortex 2024; 34:bhae130. [PMID: 38596882 PMCID: PMC11004671 DOI: 10.1093/cercor/bhae130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 03/06/2024] [Accepted: 03/07/2024] [Indexed: 04/11/2024] Open
Abstract
We currently lack a reliable method to probe cortical excitability noninvasively from the human dorsolateral prefrontal cortex (dlPFC). We recently found that the strength of early and local dlPFC transcranial magnetic stimulation (TMS)-evoked potentials (EL-TEPs) varied widely across dlPFC subregions. Despite these differences in response amplitude, reliability at each target is unknown. Here we quantified within-session reliability of dlPFC EL-TEPs after TMS to six left dlPFC subregions in 15 healthy subjects. We evaluated reliability (concordance correlation coefficient [CCC]) across targets, time windows, quantification methods, regions of interest, sensor- vs. source-space, and number of trials. On average, the medial target was most reliable (CCC = 0.78) and the most anterior target was least reliable (CCC = 0.24). However, all targets except the most anterior were reliable (CCC > 0.7) using at least one combination of the analytical parameters tested. Longer (20 to 60 ms) and later (30 to 60 ms) windows increased reliability compared to earlier and shorter windows. Reliable EL-TEPs (CCC up to 0.86) were observed using only 25 TMS trials at a medial dlPFC target. Overall, medial dlPFC targeting, wider windows, and peak-to-peak quantification improved reliability. With careful selection of target and analytic parameters, highly reliable EL-TEPs can be extracted from the dlPFC after only a small number of trials.
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Affiliation(s)
- Juha Gogulski
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, 401 Quarry Road, Stanford, CA 94305, United States
- Wu Tsai Neurosciences Institute, Stanford University, 290 Jane Stanford Way, Stanford, CA 94305, United States
- Department of Clinical Neurophysiology, HUS Diagnostic Center, Clinical Neurosciences, Helsinki University Hospital and University of Helsinki, Haartmaninkatu 4, Helsinki FI-00029, Finland
| | - Christopher C Cline
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, 401 Quarry Road, Stanford, CA 94305, United States
- Wu Tsai Neurosciences Institute, Stanford University, 290 Jane Stanford Way, Stanford, CA 94305, United States
| | - Jessica M Ross
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, 401 Quarry Road, Stanford, CA 94305, United States
- Wu Tsai Neurosciences Institute, Stanford University, 290 Jane Stanford Way, Stanford, CA 94305, United States
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), 3801 Miranda Avenue, Palo Alto, CA 94394, United States
| | - Sara Parmigiani
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, 401 Quarry Road, Stanford, CA 94305, United States
- Wu Tsai Neurosciences Institute, Stanford University, 290 Jane Stanford Way, Stanford, CA 94305, United States
| | - Corey J Keller
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, 401 Quarry Road, Stanford, CA 94305, United States
- Wu Tsai Neurosciences Institute, Stanford University, 290 Jane Stanford Way, Stanford, CA 94305, United States
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), 3801 Miranda Avenue, Palo Alto, CA 94394, United States
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Fonseca N, Bowerman J, Askari P, Proskovec AL, Feltrin FS, Veltkamp D, Early H, Wagner BC, Davenport EM, Maldjian JA. Magnetoencephalography Atlas Viewer for Dipole Localization and Viewing. J Imaging 2024; 10:80. [PMID: 38667978 PMCID: PMC11051542 DOI: 10.3390/jimaging10040080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 03/19/2024] [Accepted: 03/27/2024] [Indexed: 04/28/2024] Open
Abstract
Magnetoencephalography (MEG) is a noninvasive neuroimaging technique widely recognized for epilepsy and tumor mapping. MEG clinical reporting requires a multidisciplinary team, including expert input regarding each dipole's anatomic localization. Here, we introduce a novel tool, the "Magnetoencephalography Atlas Viewer" (MAV), which streamlines this anatomical analysis. The MAV normalizes the patient's Magnetic Resonance Imaging (MRI) to the Montreal Neurological Institute (MNI) space, reverse-normalizes MNI atlases to the native MRI, identifies MEG dipole files, and matches dipoles' coordinates to their spatial location in atlas files. It offers a user-friendly and interactive graphical user interface (GUI) for displaying individual dipoles, groups, coordinates, anatomical labels, and a tri-planar MRI view of the patient with dipole overlays. It evaluated over 273 dipoles obtained in clinical epilepsy subjects. Consensus-based ground truth was established by three neuroradiologists, with a minimum agreement threshold of two. The concordance between the ground truth and MAV labeling ranged from 79% to 84%, depending on the normalization method. Higher concordance rates were observed in subjects with minimal or no structural abnormalities on the MRI, ranging from 80% to 90%. The MAV provides a straightforward MEG dipole anatomic localization method, allowing a nonspecialist to prepopulate a report, thereby facilitating and reducing the time of clinical reporting.
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Affiliation(s)
- N.C.d. Fonseca
- MEG Center of Excellence, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (P.A.); (A.L.P.); (F.S.F.); (D.V.); (H.E.); (E.M.D.); (J.A.M.)
- Advanced Neuroscience Imaging Research (ANSIR) Laboratory, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (J.B.); (B.C.W.)
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Jason Bowerman
- Advanced Neuroscience Imaging Research (ANSIR) Laboratory, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (J.B.); (B.C.W.)
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Pegah Askari
- MEG Center of Excellence, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (P.A.); (A.L.P.); (F.S.F.); (D.V.); (H.E.); (E.M.D.); (J.A.M.)
- Advanced Neuroscience Imaging Research (ANSIR) Laboratory, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (J.B.); (B.C.W.)
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Biomedical Engineering Department, University of Texas Arlington, Arlington, TX 76019, USA
- Biomedical Engineering Department, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Amy L. Proskovec
- MEG Center of Excellence, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (P.A.); (A.L.P.); (F.S.F.); (D.V.); (H.E.); (E.M.D.); (J.A.M.)
- Advanced Neuroscience Imaging Research (ANSIR) Laboratory, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (J.B.); (B.C.W.)
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Fabricio Stewan Feltrin
- MEG Center of Excellence, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (P.A.); (A.L.P.); (F.S.F.); (D.V.); (H.E.); (E.M.D.); (J.A.M.)
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Daniel Veltkamp
- MEG Center of Excellence, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (P.A.); (A.L.P.); (F.S.F.); (D.V.); (H.E.); (E.M.D.); (J.A.M.)
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Heather Early
- MEG Center of Excellence, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (P.A.); (A.L.P.); (F.S.F.); (D.V.); (H.E.); (E.M.D.); (J.A.M.)
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Ben C. Wagner
- Advanced Neuroscience Imaging Research (ANSIR) Laboratory, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (J.B.); (B.C.W.)
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Elizabeth M. Davenport
- MEG Center of Excellence, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (P.A.); (A.L.P.); (F.S.F.); (D.V.); (H.E.); (E.M.D.); (J.A.M.)
- Advanced Neuroscience Imaging Research (ANSIR) Laboratory, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (J.B.); (B.C.W.)
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Biomedical Engineering Department, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Joseph A. Maldjian
- MEG Center of Excellence, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (P.A.); (A.L.P.); (F.S.F.); (D.V.); (H.E.); (E.M.D.); (J.A.M.)
- Advanced Neuroscience Imaging Research (ANSIR) Laboratory, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (J.B.); (B.C.W.)
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Biomedical Engineering Department, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
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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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Nader R, Bourcier R, Autrusseau F. Using deep learning for an automatic detection and classification of the vascular bifurcations along the Circle of Willis. Med Image Anal 2023; 89:102919. [PMID: 37619447 DOI: 10.1016/j.media.2023.102919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 06/01/2023] [Accepted: 07/31/2023] [Indexed: 08/26/2023]
Abstract
Most of the intracranial aneurysms (ICA) occur on a specific portion of the cerebral vascular tree named the Circle of Willis (CoW). More particularly, they mainly arise onto fifteen of the major arterial bifurcations constituting this circular structure. Hence, for an efficient and timely diagnosis it is critical to develop some methods being able to accurately recognize each Bifurcation of Interest (BoI). Indeed, an automatic extraction of the bifurcations presenting the higher risk of developing an ICA would offer the neuroradiologists a quick glance at the most alarming areas. Due to the recent efforts on Artificial Intelligence, Deep Learning turned out to be the best performing technology for many pattern recognition tasks. Moreover, various methods have been particularly designed for medical image analysis purposes. This study intends to assist the neuroradiologists to promptly locate any bifurcation presenting a high risk of ICA occurrence. It can be seen as a Computer Aided Diagnosis scheme, where the Artificial Intelligence facilitates the access to the regions of interest within the MRI. In this work, we propose a method for a fully automatic detection and recognition of the bifurcations of interest forming the Circle of Willis. Several neural networks architectures have been tested, and we thoroughly evaluate the bifurcation recognition rate.
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Affiliation(s)
- Rafic Nader
- Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du thorax, F-44000 Nantes, France
| | - Romain Bourcier
- Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du thorax, F-44000 Nantes, France
| | - Florent Autrusseau
- Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du thorax, F-44000 Nantes, France; Nantes Université, Polytech'Nantes, LTeN, U-6607, Rue Ch. Pauc, 44306, Nantes, France.
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Gogulski J, Cline CC, Ross JM, Parmigiani S, Keller CJ. Reliability of the TMS-evoked potential in dorsolateral prefrontal cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.04.556283. [PMID: 37732239 PMCID: PMC10508735 DOI: 10.1101/2023.09.04.556283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
Background We currently lack a robust and reliable method to probe cortical excitability noninvasively from the human dorsolateral prefrontal cortex (dlPFC), a region heavily implicated in psychiatric disorders. We recently found that the strength of early and local dlPFC single pulse transcranial magnetic stimulation (TMS)-evoked potentials (EL-TEPs) varied widely depending on the anatomical subregion probed, with more medial regions eliciting stronger responses than anterolateral sites. Despite these differences in amplitude of response, the reliability at each target is not known. Objective To evaluate the reliability of EL-TEPs across the dlPFC. Methods In 15 healthy subjects, we quantified within-session reliability of dlPFC EL-TEPs after single pulse TMS to six dlPFC subregions. We evaluated the concordance correlation coefficient (CCC) across targets and analytical parameters including time window, quantification method, region of interest, sensor-vs. source-space, and number of trials. Results At least one target in the anterior and posterior dlPFC produced reliable EL-TEPs (CCC>0.7). The medial target was most reliable (CCC = 0.78) and the most anterior target was least reliable (CCC = 0.24). ROI size and type (sensor vs. source space) did not affect reliability. Longer (20-60 ms, CCC = 0.62) and later (30-60 ms, CCC = 0.61) time windows resulted in higher reliability compared to earlier and shorter (20-40 ms, CCC 0.43; 20-50 ms, CCC = 0.55) time windows. Peak-to-peak quantification resulted in higher reliability than the mean of the absolute amplitude. Reliable EL-TEPs (CCC up to 0.86) were observed using only 25 TMS trials for a medial dlPFC target. Conclusions Medial TMS location, wider time window (20-60ms), and peak-to-peak quantification improved reliability. Highly reliable EL-TEPs can be extracted from dlPFC after only a small number of trials. Highlights Medial dlPFC target improved EL-TEP reliability compared to anterior targets.After optimizing analytical parameters, at least one anterior and one posterior target was reliable (CCC>0.7).Longer (20-60 ms) and later (30-60 ms) time windows were more reliable than earlier and shorter (20-40 ms or 20-50 ms) latencies.Peak-to-peak quantification resulted in higher reliability compared to the mean of the absolute amplitude.As low as 25 trials can yield reliable EL-TEPs from the dlPFC.
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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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10
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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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11
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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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12
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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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13
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Phan TG, Ma H, Goyal M, Hilton J, Sinnott M, Srikanth V, Beare R. Computer Modeling of Clot Retrieval-Circle of Willis. Front Neurol 2020; 11:773. [PMID: 32849226 PMCID: PMC7427049 DOI: 10.3389/fneur.2020.00773] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Accepted: 06/24/2020] [Indexed: 12/22/2022] Open
Abstract
Endovascular clot retrieval, often referred to as mechanical thrombectomy, has transformed the treatment of patients with ischemic stroke based on an underlying large cerebral vessel occlusion, ranging from the extracranial internal carotid artery (ICA) to the M1 (proximal) segment of the middle cerebral artery (MCA). The aim of this study was to evaluate the effect of a progressive occlusion of the extracranial portion of the ICA on the cerebral blood flow either with a conventional guiding catheter or a balloon-guiding catheter, which enables the operator to completely occlude the parent artery by inflating the balloon around the tip of this type of guiding catheter. We evaluated the impact of flow reduction in the ICA in the setting of ipsilateral MCA occlusion given the different configurations of the circle of Willis (CoW). The computer model of cerebral arteries was based on anatomical works by Rhoton (1) and van der Eecken (2). The interactive experimental results are available on the web at https://gntem3.shinyapps.io/ecrsim. In the setting of left MCA occlusion, compensation from the anterior and posterior communicating artery preserved the flow in the left anterior cerebral artery (ACA) but not the left MCA branches. Under selected CoW configurations, such as classic, missing Acom, or missing A1 segment of the ACA and concurrent right ICA occlusion, there was a progressive decrease of flow in the left ACA to a minimum of 78% when the simulated catheter fully occluded the left ICA. Flow collapsed (<10%) in the left ACA and MCA branches under CoW configurations, such as bilateral fetal PCA. In summary, compensatory flow collapsed under certain clot retrieval scenarios and unusual configurations of CoW.
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Affiliation(s)
- Thanh G Phan
- Stroke and Aging Research Group, Clinical Trials, Imaging and Informatics Division, School of Clinical Sciences at Monash Health, Monash University, Melbourne, VIC, Australia.,Department of Neurology, Monash Health, Melbourne, VIC, Australia
| | - Henry Ma
- Stroke and Aging Research Group, Clinical Trials, Imaging and Informatics Division, School of Clinical Sciences at Monash Health, Monash University, Melbourne, VIC, Australia.,Department of Neurology, Monash Health, Melbourne, VIC, Australia
| | - Mayank Goyal
- Departments of Clinical Neuroscience and Radiology, Cummings School of Medicine, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - James Hilton
- Data 61, CSIRO, Innovation Hub, Docklands, VIC, Australia
| | | | - Velandai Srikanth
- Department of Medicine, Peninsula Clinical School, Central Clinical School, Frankston Hospital, Monash University, Melbourne, VIC, Australia
| | - Richard Beare
- Departments of Clinical Neuroscience and Radiology, Cummings School of Medicine, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
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14
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Direct comparison of activation maps during galvanic vestibular stimulation: A hybrid H2[15 O] PET-BOLD MRI activation study. PLoS One 2020; 15:e0233262. [PMID: 32413079 PMCID: PMC7228124 DOI: 10.1371/journal.pone.0233262] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2019] [Accepted: 05/01/2020] [Indexed: 12/17/2022] Open
Abstract
Previous unimodal PET and fMRI studies in humans revealed a reproducible vestibular brain activation pattern, but with variations in its weighting and expansiveness. Hybrid studies minimizing methodological variations at baseline conditions are rare and still lacking for task-based designs. Thus, we applied for the first time hybrid 3T PET-MRI scanning (Siemens mMR) in healthy volunteers using galvanic vestibular stimulation (GVS) in healthy volunteers in order to directly compare H215O-PET and BOLD MRI responses. List mode PET acquisition started with the injection of 750 MBq H215O simultaneously to MRI EPI sequences. Group-level statistical parametric maps were generated for GVS vs. rest contrasts of PET, MR-onset (event-related), and MR-block. All contrasts showed a similar bilateral vestibular activation pattern with remarkable proximity of activation foci. Both BOLD contrasts gave more bilateral wide-spread activation clusters than PET; no area showed contradictory signal responses. PET still confirmed the right-hemispheric lateralization of the vestibular system, whereas BOLD-onset revealed only a tendency. The reciprocal inhibitory visual-vestibular interaction concept was confirmed by PET signal decreases in primary and secondary visual cortices, and BOLD-block decreases in secondary visual areas. In conclusion, MRI activation maps contained a mixture of CBF measured using H215O-PET and additional non-CBF effects, and the activation-deactivation pattern of the BOLD-block appears to be more similar to the H215O-PET than the BOLD-onset.
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15
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Grange S, Grange R, Garnier P, Varvat J, Marinescu D, Barral FG, Boutet C, Schneider FC. Boundary and vulnerability estimation of the internal borderzone using ischemic stroke lesion mapping. Sci Rep 2020; 10:1662. [PMID: 32015357 PMCID: PMC6997399 DOI: 10.1038/s41598-020-58480-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Accepted: 01/13/2020] [Indexed: 01/06/2023] Open
Abstract
Distinction between deep and superficial middle cerebral artery (MCA) territories and their junctional vascular area (the internal borderzone or IBZ) constitutes a predictor of stroke patient outcome. However, the IBZ boundaries are not well-defined because of substantial anatomical variance. Here, we built a statistical estimate of the IBZ and tested its vulnerability to ischemia using an independent sample. First, we used delineated lesions of 122 patients suffering of chronic ischemic stroke grouped in deep, superficial and territorial topographies and statistical comparisons to generate a probabilistic estimate of the IBZ. The IBZ extended from the insular cortex to the internal capsule and the anterior part of the caudate nucleus head. The IBZ showed the highest lesion frequencies (~30% on average across IBZ voxels) in our chronic stroke patients but also in an independent sample of 87 acute patients. Additionally, the most important apparent diffusion coefficient reductions (−6%), which reflect stroke severity, were situated within our IBZ estimate. The IBZ was most severely injured in case of a territorial infarction. Then, our results are in favour of an increased IBZ vulnerability to ischemia. Moreover, our probabilistic estimates of deep, superficial and IBZ regions can help the everyday spatial classification of lesions.
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Affiliation(s)
- Sylvain Grange
- Department of Radiology, University Hospital of Saint Etienne, Saint-Priest-en-Jarez, France
| | - Rémi Grange
- Department of Radiology, University Hospital of Saint Etienne, Saint-Priest-en-Jarez, France
| | - Pierre Garnier
- Stroke Unit, University Hospital of Saint Etienne, Saint-Priest-en-Jarez, France
| | - Jérôme Varvat
- Stroke Unit, University Hospital of Saint Etienne, Saint-Priest-en-Jarez, France
| | - Doina Marinescu
- Stroke Unit, University Hospital of Saint Etienne, Saint-Priest-en-Jarez, France
| | - Fabrice-Guy Barral
- Department of Radiology, University Hospital of Saint Etienne, Saint-Priest-en-Jarez, France.,TAPE EA7423, University of Saint Etienne, Saint-Priest-en-Jarez, France
| | - Claire Boutet
- Department of Radiology, University Hospital of Saint Etienne, Saint-Priest-en-Jarez, France
| | - Fabien C Schneider
- Department of Radiology, University Hospital of Saint Etienne, Saint-Priest-en-Jarez, France.
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16
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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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17
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Holmgren M, Wåhlin A, Dunås T, Malm J, Eklund A. Assessment of Cerebral Blood Flow Pulsatility and Cerebral Arterial Compliance With 4D Flow MRI. J Magn Reson Imaging 2019; 51:1516-1525. [PMID: 31713964 PMCID: PMC7216927 DOI: 10.1002/jmri.26978] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Revised: 10/08/2019] [Accepted: 10/08/2019] [Indexed: 12/12/2022] Open
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 2 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:1516-1525.
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Affiliation(s)
| | - Anders Wåhlin
- Department of Radiation Sciences, Umeå University, Umeå, Sweden.,Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden
| | - Tora Dunås
- Department of Radiation Sciences, Umeå University, Umeå, Sweden.,Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden.,Center for Demographic and Aging Research, 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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18
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Biondetti E, Rojas-Villabona A, Sokolska M, Pizzini FB, Jäger HR, Thomas DL, Shmueli K. Investigating the oxygenation of brain arteriovenous malformations using quantitative susceptibility mapping. Neuroimage 2019; 199:440-453. [DOI: 10.1016/j.neuroimage.2019.05.014] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Revised: 04/23/2019] [Accepted: 05/06/2019] [Indexed: 02/07/2023] Open
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19
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A statistical atlas of cerebral arteries generated using multi-center MRA datasets from healthy subjects. Sci Data 2019; 6:29. [PMID: 30975990 PMCID: PMC6472360 DOI: 10.1038/s41597-019-0034-5] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Accepted: 03/05/2019] [Indexed: 11/08/2022] Open
Abstract
Magnetic resonance angiography (MRA) can capture the variation of cerebral arteries with high spatial resolution. These measurements include valuable information about the morphology, geometry, and density of brain arteries, which may be useful to identify risk factors for cerebrovascular and neurological diseases at an early time point. However, this requires knowledge about the distribution and morphology of vessels in healthy subjects. The statistical arterial brain atlas described in this work is a free and public neuroimaging resource that can be used to identify vascular morphological changes. The atlas was generated based on 544 freely available multi-center MRA and T1-weighted MRI datasets. The arteries were automatically segmented in each MRA dataset and used for vessel radius quantification. The binary segmentation and vessel size information were non-linearly registered to the MNI brain atlas using the T1-weighted MRI datasets to construct atlases of artery occurrence probability, mean artery radius, and artery radius standard deviation. This public neuroimaging resource improves the understanding of the distribution and size of arteries in the healthy human brain.
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20
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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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21
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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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22
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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
We have recently witnessed an explosion of large-scale initiatives and projects addressing mapping, modeling, simulation and atlasing of the human brain, including the BRAIN Initiative, the Human Brain Project, the Human Connectome Project (HCP), the Big Brain, the Blue Brain Project, the Allen Brain Atlas, the Brainnetome, among others. Besides these large and international initiatives, there are numerous mid-size and small brain atlas-related projects. My contribution to these global efforts has been to create adult human brain atlases in health and disease, and to develop atlas-based applications. For over two decades with my R&D lab I developed 35 brain atlases, licensed to 67 companies and made available in about 100 countries. This paper has two objectives. First, it provides an overview of the state of the art in brain atlasing. Second, as it is already 20 years from the release of our first brain atlas, I summarise my past and present efforts, share my experience in atlas creation, validation and commercialisation, compare with the state of the art, and propose future directions.
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Affiliation(s)
- Wieslaw L Nowinski
- John Paull II Center for Virtual Anatomy and Surgical Simulation, University of Cardinal Stefan Wyszynski in Warsaw, Poland
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