1
|
Huang LX, Wu XB, Liu YA, Guo X, Liu CC, Cai WQ, Wang SW, Luo B. High-resolution magnetic resonance vessel wall imaging in ischemic stroke and carotid artery atherosclerotic stenosis: A review. Heliyon 2024; 10:e27948. [PMID: 38571643 PMCID: PMC10987942 DOI: 10.1016/j.heliyon.2024.e27948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 03/02/2024] [Accepted: 03/08/2024] [Indexed: 04/05/2024] Open
Abstract
Ischemic stroke is a significant burden on human health worldwide. Carotid Atherosclerosis stenosis plays an important role in the comprehensive assessment and prevention of ischemic stroke patients. High-resolution vessel wall magnetic resonance imaging has emerged as a successful technique for assessing carotid atherosclerosis stenosis. This advanced imaging modality has shown promise in effectively displaying a wide range of characteristics associated with the condition, leading to a comprehensive evaluation. High-resolution vessel wall magnetic resonance imaging not only enables a comprehensive evaluation of the instability of carotid atherosclerosis stenosis plaques but also provides valuable information for understanding the pathogenesis and predicting the prognosis of ischemic stroke patients. The purpose of this article is to review the application of high-resolution magnetic resonance imaging in ischemic stroke and carotid atherosclerotic stenosis.
Collapse
Affiliation(s)
- Li-Xin Huang
- Department of Neurosurgery, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
- Department of Neurosurgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
- Department of Neurosurgery, The Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China
| | - Xiao-Bing Wu
- Department of Neurosurgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yi-Ao Liu
- Department of Neurosurgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
- Department of Neurosurgery, The Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China
| | - Xin Guo
- Department of Neurosurgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
- Department of Neurosurgery, The Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China
| | - Chi-Chen Liu
- Department of Neurosurgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
- Department of Neurosurgery, The Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China
| | - Wang-Qing Cai
- Department of Neurosurgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Sheng-Wen Wang
- Department of Neurosurgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Bin Luo
- Department of Neurosurgery, The Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China
| |
Collapse
|
2
|
Mossa-Basha M, Yuan C, Wasserman BA, Mikulis DJ, Hatsukami TS, Balu N, Gupta A, Zhu C, Saba L, Li D, DeMarco JK, Lehman VT, Qiao Y, Jager HR, Wintermark M, Brinjikji W, Hess CP, Saloner DA. Survey of the American Society of Neuroradiology Membership on the Use and Value of Extracranial Carotid Vessel Wall MRI. AJNR Am J Neuroradiol 2022; 43:1756-1761. [PMID: 36423951 DOI: 10.3174/ajnr.a7720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 10/10/2022] [Indexed: 11/25/2022]
Abstract
BACKGROUND AND PURPOSE Extracranial vessel wall MRI (EC-VWI) contributes to vasculopathy characterization. This survey study investigated EC-VWI adoption by American Society of Neuroradiology (ASNR) members and indications and barriers to implementation. MATERIALS AND METHODS The ASNR Vessel Wall Imaging Study Group survey on EC-VWI use, frequency, applications, MR imaging systems and field strength used, protocol development approaches, vendor engagement, reasons for not using EC-VWI, ordering provider interest, and impact on clinical care was distributed to the ASNR membership between April 2, 2019, to August 30, 2019. RESULTS There were 532 responses; 79 were excluded due to minimal, incomplete response and 42 due to redundant institutional responses, leaving 411 responses. Twenty-six percent indicated that their institution performed EC-VWI, with 66.3% performing it ≤1-2 times per month, most frequently on 3T MR imaging, with most using combined 3D and 2D protocols. Protocols most commonly included pre- and postcontrast T1-weighted imaging, TOF-MRA, and contrast-enhanced MRA. Inflammatory vasculopathy (63.3%), plaque vulnerability assessments (61.1%), intraplaque hemorrhage (61.1%), and dissection-detection/characterization (51.1%) were the most frequent applications. For those not performing EC-VWI, the reasons were a lack of ordering provider interest (63.9%), lack of radiologist time/interest (47.5%) or technical support (41.4%) for protocol development, and limited interpretation experience (44.9%) and knowledge of clinical applications (43.7%). Reasons given by 46.9% were that no providers approached radiology with interest in EC-VWI. If barriers were overcome, 51.1% of those not performing EC-VWI indicated they would perform it, and 40.6% were unsure; 48.6% did not think that EC-VWI had impacted patient management at their institution. CONCLUSIONS Only 26% of neuroradiology groups performed EC-VWI, most commonly due to limited clinician interest. Improved provider and radiologist education, protocols, processing techniques, technical support, and validation trials could increase adoption.
Collapse
Affiliation(s)
- M Mossa-Basha
- From the Department of Radiology (M.M.-B.), University of North Carolina, Chapel Hill, North Carolina .,Departments of Radiology (M.M.-B., N.B., C.Z.)
| | - C Yuan
- Department of Radiology (C.Y.), University of Utah, Salt Lake City, Utah
| | - B A Wasserman
- Department of Radiology (B.A.W.), University of Maryland, Baltimore, Maryland.,Department of Radiology (B.A.W., Y.Q.), Johns Hopkins University, Baltimore, Maryland
| | - D J Mikulis
- Joint Department of Medical Imaging (D.J.M.), The University Health Network and the University of Toronto, Toronto, Ontario, Canada
| | - T S Hatsukami
- Surgery (T.S.H.), University of Washington, Seattle, Washington
| | - N Balu
- Departments of Radiology (M.M.-B., N.B., C.Z.)
| | - A Gupta
- Department of Radiology (A.G.), Weill Cornell Medicine, New York, New York
| | - C Zhu
- Departments of Radiology (M.M.-B., N.B., C.Z.)
| | - L Saba
- Department of Radiology (L.S.), University of Cagliari, Cagliari, Sardinia, Italy
| | - D Li
- Biomedical Imaging Research Institute (D.L.), Cedars-Sinai Medical Center, Los Angeles, California
| | - J K DeMarco
- Department of Radiology (J.K.D.), Walter Reed National Military Medical Center, Bethesda, Maryland and Uniformed Services University of the Health Sciences, Bethesda, Maryland
| | - V T Lehman
- Department of Radiology (V.T.L., W.B.), Mayo Clinic, Rochester, Minnesota
| | - Y Qiao
- Department of Radiology (B.A.W., Y.Q.), Johns Hopkins University, Baltimore, Maryland
| | - H R Jager
- Neuroradiological Academic Unit (H.R.J.), Department of Brain Repair and Rehabilitation, University College London, Queen Square Institute of Neurology, London, UK
| | - M Wintermark
- Department of Neuroradiology (M.W.), MD Anderson Cancer Institute, Houston, Texas
| | - W Brinjikji
- Department of Radiology (V.T.L., W.B.), Mayo Clinic, Rochester, Minnesota
| | - C P Hess
- Department of Radiology and Biomedical Imaging (C.P.H., D.A.S.), University of California, San Francisco, San Francisco, California
| | - D A Saloner
- Department of Radiology and Biomedical Imaging (C.P.H., D.A.S.), University of California, San Francisco, San Francisco, California
| |
Collapse
|
3
|
Machine Learning Quantitation of Cardiovascular and Cerebrovascular Disease: A Systematic Review of Clinical Applications. Diagnostics (Basel) 2021; 11:diagnostics11030551. [PMID: 33808677 PMCID: PMC8003459 DOI: 10.3390/diagnostics11030551] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 03/12/2021] [Accepted: 03/16/2021] [Indexed: 01/10/2023] Open
Abstract
Research into machine learning (ML) for clinical vascular analysis, such as those useful for stroke and coronary artery disease, varies greatly between imaging modalities and vascular regions. Limited accessibility to large diverse patient imaging datasets, as well as a lack of transparency in specific methods, are obstacles to further development. This paper reviews the current status of quantitative vascular ML, identifying advantages and disadvantages common to all imaging modalities. Literature from the past 8 years was systematically collected from MEDLINE® and Scopus database searches in January 2021. Papers satisfying all search criteria, including a minimum of 50 patients, were further analysed and extracted of relevant data, for a total of 47 publications. Current ML image segmentation, disease risk prediction, and pathology quantitation methods have shown sensitivities and specificities over 70%, compared to expert manual analysis or invasive quantitation. Despite this, inconsistencies in methodology and the reporting of results have prevented inter-model comparison, impeding the identification of approaches with the greatest potential. The clinical potential of this technology has been well demonstrated in Computed Tomography of coronary artery disease, but remains practically limited in other modalities and body regions, particularly due to a lack of routine invasive reference measurements and patient datasets.
Collapse
|
4
|
Ziegler M, Alfraeus J, Bustamante M, Good E, Engvall J, de Muinck E, Dyverfeldt P. Automated segmentation of the individual branches of the carotid arteries in contrast-enhanced MR angiography using DeepMedic. BMC Med Imaging 2021; 21:38. [PMID: 33639893 PMCID: PMC7912466 DOI: 10.1186/s12880-021-00568-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 02/15/2021] [Indexed: 11/24/2022] Open
Abstract
Background Non-invasive imaging is of interest for tracking the progression of atherosclerosis in the carotid bifurcation, and segmenting this region into its constituent branch arteries is necessary for analyses. The purpose of this study was to validate and demonstrate a method for segmenting the carotid bifurcation into the common, internal, and external carotid arteries (CCA, ICA, ECA) in contrast-enhanced MR angiography (CE-MRA) data. Methods A segmentation pipeline utilizing a convolutional neural network (DeepMedic) was tailored and trained for multi-class segmentation of the carotid arteries in CE-MRA data from the Swedish CardioPulmonsary bioImage Study (SCAPIS). Segmentation quality was quantitatively assessed using the Dice similarity coefficient (DSC), Matthews Correlation Coefficient (MCC), F2, F0.5, and True Positive Ratio (TPR). Segmentations were also assessed qualitatively, by three observers using visual inspection. Finally, geometric descriptions of the carotid bifurcations were generated for each subject to demonstrate the utility of the proposed segmentation method. Results Branch-level segmentations scored DSC = 0.80 ± 0.13, MCC = 0.80 ± 0.12, F2 = 0.82 ± 0.14, F0.5 = 0.78 ± 0.13, and TPR = 0.84 ± 0.16, on average in a testing cohort of 46 carotid bifurcations. Qualitatively, 61% of segmentations were judged to be usable for analyses without adjustments in a cohort of 336 carotid bifurcations without ground-truth. Carotid artery geometry showed wide variation within the whole cohort, with CCA diameter 8.6 ± 1.1 mm, ICA 7.5 ± 1.4 mm, ECA 5.7 ± 1.0 mm and bifurcation angle 41 ± 21°. Conclusion The proposed segmentation method automatically generates branch-level segmentations of the carotid arteries that are suitable for use in further analyses and help enable large-cohort investigations.
Collapse
Affiliation(s)
- Magnus Ziegler
- Division of Cardiovascular Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden. .,Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.
| | - Jesper Alfraeus
- Division of Cardiovascular Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Mariana Bustamante
- Division of Cardiovascular Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Elin Good
- Division of Cardiovascular Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.,Department of Cardiology, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Jan Engvall
- Division of Cardiovascular Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.,Department of Clinical Physiology, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Ebo de Muinck
- Division of Cardiovascular Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.,Department of Cardiology, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Petter Dyverfeldt
- Division of Cardiovascular Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| |
Collapse
|