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Sharma S, Nayak A, Thomas B, Kesavadas C. Synthetic MR: Clinical applications in neuroradiology. Neuroradiology 2025; 67:509-527. [PMID: 39888426 DOI: 10.1007/s00234-025-03547-8] [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: 07/05/2024] [Accepted: 01/13/2025] [Indexed: 02/01/2025]
Abstract
PURPOSE Synthetic MR is a quantitative MRI method that measures tissue relaxation times and generates multiple contrast-weighted images using suitable algorithms. The present article principally discusses the multiple dynamic multiple echo (MDME) technique of synthetic MR and briefly describes other quantitative MR sequences. METHODS Using illustrative cases, various applications of the MDME sequence in neuroradiology are explained. The MDME sequence allows rapid quantification of tissue relaxation times in a scan duration of 5-7 minutes for full brain coverage. It also has the additional advantages of myelin quantification and automatic segmentation of brain volumes. RESULTS Applications including reducing scan time, improved detection of demyelinating plaques in Multiple Sclerosis (MS), objective assessment and follow-up for brain atrophy in neurodegenerative MS and dementia cases, and applications in stroke imaging and neuro-oncology are discussed. Uses in the pediatric population, including assessment of brain development and progression of myelination in children, evaluation of white matter disorders, and evaluation of pediatric and adult epilepsy, are elaborated. Quantitative evaluation by synthetic MR is discussed, which allows homogenization and objectification of the radiology data and can serve as a valuable source for artificial intelligence and future multicentre studies. A brief discussion on the technique, other quantitative MR methods, and limitations of the MDME sequence is also presented. CONCLUSION The article intends to provide an explicit and comprehensive review of the applications of synthetic MR in neuroradiology, exploring its potential as a routine sequence in daily neuroimaging practice.
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Affiliation(s)
- Smily Sharma
- Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, 695011, Kerala, India.
| | - Abhishek Nayak
- Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, 695011, Kerala, India
| | - Bejoy Thomas
- Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, 695011, Kerala, India
| | - Chandrasekharan Kesavadas
- Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, 695011, Kerala, India
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Wang K, Liu WV, Yang R, Li L, Lu X, Lei H, Jiang J, Zha Y. Super-resolution synthetic MRI using deep learning reconstruction for accurate diagnosis of knee osteoarthritis. Insights Imaging 2025; 16:44. [PMID: 39961957 PMCID: PMC11832993 DOI: 10.1186/s13244-025-01911-z] [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: 09/26/2024] [Accepted: 01/22/2025] [Indexed: 02/20/2025] Open
Abstract
OBJECTIVE To assess the accuracy of deep learning reconstruction (DLR) technique on synthetic MRI (SyMRI) including T2 measurements and diagnostic performance of DLR synthetic MRI (SyMRIDL) in patients with knee osteoarthritis (KOA) using conventional MRI as standard reference. MATERIALS AND METHODS This prospective study recruited 36 volunteers and 70 patients with suspected KOA from May to October 2023. DLR and non-DLR synthetic T2 measurements (T2-SyMRIDL, T2-SyMRI) for phantom and in vivo knee cartilage were compared with multi-echo fast-spin-echo (MESE) sequence acquired standard T2 values (T2MESE). The inter-reader agreement on qualitative evaluation of SyMRIDL and the positive percent agreement (PPA) and negative percentage agreement (NPA) were analyzed using routine images as standard diagnosis. RESULTS DLR significantly narrowed the quantitative differences between T2-SyMRIDL and T2MESE for 0.8 ms with 95% LOA [-5.5, 7.1]. The subjective assessment between DLR synthetic MR images and conventional MRI was comparable (all p > 0.05); Inter-reader agreement for SyMRIDL and conventional MRI was substantial to almost perfect with values between 0.62 and 0.88. SyMRIDL MOAKS had substantial inter-reader agreement and high PPA/NPA values (95%/99%) using conventional MRI as standard reference. Moreover, T2-SyMRIDL measurements instead of non-DLR ones significantly differentiated normal-appearing from injury-visible cartilages. CONCLUSION DLR synthetic knee MRI provided both weighted images for clinical diagnosis and accurate T2 measurements for more confidently identifying early cartilage degeneration from normal-appearing cartilages. CRITICAL RELEVANCE STATEMENT One-acquisition synthetic MRI based on deep learning reconstruction provided an accurate quantitative T2 map and morphologic images in relatively short scan time for more confidently identifying early cartilage degeneration from normal-appearing cartilages compared to the conventional morphologic knee sequences. KEY POINTS Deep learning reconstruction (DLR) synthetic knee cartilage T2 values showed no difference from conventional ones. DLR synthetic T1-, proton density-, STIR-weighted images had high positive percent agreement and negative percentage agreement using MRI OA Knee Score features. DLR synthetic T2 measurements could identify early cartilage degeneration from normal-appearing ones.
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Affiliation(s)
- Kejun Wang
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China
| | | | - Renjie Yang
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Liang Li
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xuefang Lu
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Haoran Lei
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jiawei Jiang
- School of Computer Science, Wuhan University, Wuhan, China
| | - Yunfei Zha
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China.
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Hossain A, Islam MT, Rahman T, Chowdhury MEH, Tahir A, Kiranyaz S, Mat K, Beng GK, Soliman MS. Brain Tumor Segmentation and Classification from Sensor-Based Portable Microwave Brain Imaging System Using Lightweight Deep Learning Models. BIOSENSORS 2023; 13:302. [PMID: 36979514 PMCID: PMC10046629 DOI: 10.3390/bios13030302] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 02/07/2023] [Accepted: 02/15/2023] [Indexed: 06/18/2023]
Abstract
Automated brain tumor segmentation from reconstructed microwave (RMW) brain images and image classification is essential for the investigation and monitoring of the progression of brain disease. The manual detection, classification, and segmentation of tumors are extremely time-consuming but crucial tasks due to the tumor's pattern. In this paper, we propose a new lightweight segmentation model called MicrowaveSegNet (MSegNet), which segments the brain tumor, and a new classifier called the BrainImageNet (BINet) model to classify the RMW images. Initially, three hundred (300) RMW brain image samples were obtained from our sensors-based microwave brain imaging (SMBI) system to create an original dataset. Then, image preprocessing and augmentation techniques were applied to make 6000 training images per fold for a 5-fold cross-validation. Later, the MSegNet and BINet were compared to state-of-the-art segmentation and classification models to verify their performance. The MSegNet has achieved an Intersection-over-Union (IoU) and Dice score of 86.92% and 93.10%, respectively, for tumor segmentation. The BINet has achieved an accuracy, precision, recall, F1-score, and specificity of 89.33%, 88.74%, 88.67%, 88.61%, and 94.33%, respectively, for three-class classification using raw RMW images, whereas it achieved 98.33%, 98.35%, 98.33%, 98.33%, and 99.17%, respectively, for segmented RMW images. Therefore, the proposed cascaded model can be used in the SMBI system.
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Affiliation(s)
- Amran Hossain
- Centre for Advanced Electronic and Communication Engineering, Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
- Department of Computer Science and Engineering, Dhaka University of Engineering and Technology, Gazipur, Gazipur 1707, Bangladesh
| | - Mohammad Tariqul Islam
- Centre for Advanced Electronic and Communication Engineering, Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Tawsifur Rahman
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | | | - Anas Tahir
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | - Serkan Kiranyaz
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | - Kamarulzaman Mat
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Gan Kok Beng
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Mohamed S. Soliman
- Department of Electrical Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
- Department of Electrical Engineering, Faculty of Energy Engineering, Aswan University, Aswan 81528, Egypt
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Tissue Characteristics of Endometrial Carcinoma Analyzed by Quantitative Synthetic MRI and Diffusion-Weighted Imaging. Diagnostics (Basel) 2022; 12:diagnostics12122956. [PMID: 36552962 PMCID: PMC9776551 DOI: 10.3390/diagnostics12122956] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 11/08/2022] [Accepted: 11/24/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND This study investigates the association of T1, T2, proton density (PD) and the apparent diffusion coefficient (ADC) with histopathologic features of endometrial carcinoma (EC). METHODS One hundred and nine EC patients were prospectively enrolled from August 2019 to December 2020. Synthetic magnetic resonance imaging (MRI) was acquired through one acquisition, in addition to diffusion-weighted imaging (DWI) and other conventional sequences using 1.5T MRI. T1, T2, PD derived from synthetic MRI and ADC derived from DWI were compared among different histopathologic features, namely the depth of myometrial invasion (MI), tumor grade, cervical stromal invasion (CSI) and lymphovascular invasion (LVSI) of EC by the Mann-Whitney U test. Classification models based on the significant MRI metrics were constructed with their respective receiver operating characteristic (ROC) curves, and their micro-averaged ROC was used to evaluate the overall performance of these significant MRI metrics in determining aggressive histopathologic features of EC. RESULTS EC with MI had significantly lower T2, PD and ADC than those without MI (p = 0.007, 0.006 and 0.043, respectively). Grade 2-3 EC and EC with LVSI had significantly lower ADC than grade 1 EC and EC without LVSI, respectively (p = 0.005, p = 0.020). There were no differences in the MRI metrics in EC with or without CSI. Micro-averaged ROC of the three models had an area under the curve of 0.83. CONCLUSIONS Synthetic MRI provided quantitative metrics to characterize EC with one single acquisition. Low T2, PD and ADC were associated with aggressive histopathologic features of EC, offering excellent performance in determining aggressive histopathologic features of EC.
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André J, Barrit S, Jissendi P. Synthetic MRI for stroke: a qualitative and quantitative pilot study. Sci Rep 2022; 12:11552. [PMID: 35798771 PMCID: PMC9262877 DOI: 10.1038/s41598-022-15204-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Accepted: 06/20/2022] [Indexed: 11/09/2022] Open
Abstract
Synthetic MR provides qualitative and quantitative multi-parametric data about tissue properties in a single acquisition. Its use in stroke imaging is not yet established. We compared synthetic and conventional image quality and studied synthetic relaxometry of acute and chronic ischemic lesions to investigate its interest for stroke imaging. We prospectively acquired synthetic and conventional brain MR of 43 consecutive adult patients with suspected stroke. We studied a total of 136 lesions, of which 46 DWI-positive with restricted ADC (DWI + /rADC), 90 white matter T2/FLAIR hyperintensities (WMH) showing no diffusion restriction, and 430 normal brain regions (NBR). We assessed image quality for lesion definition according to a 3-level score by two readers of different experiences. We compared relaxometry of lesions and regions of interest. Synthetic images were superior to their paired conventional images for lesion definition except for sFLAIR (sT1 or sPSIR vs. cT1 and sT2 vs. cT2 for DWI + /rADC and WMH definition; p values < .001) with substantial to almost perfect inter-rater reliability (κ ranging from 0.711 to 0.932, p values < .001). We found significant differences in relaxometry between lesions and NBR and between acute and chronic lesions (T1, T2, and PD of DWI + /rADC or WMH vs. mirror NBR; p values < .001; T1 and PD of DWI + /rADC vs. WMH; p values of 0.034 and 0.008). Synthetic MR may contribute to stroke imaging by fast generating accessible weighted images for visual inspection derived from rapidly acquired relaxometry data. Moreover, this synthetic relaxometry could differentiate acute and chronic ischemic lesions.
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Affiliation(s)
- Joachim André
- Department of Radiology, Hôpital Erasme, Université Libre de Bruxelles (ULB), Route de Lennik, 808, 1070, Anderlecht, Brussels, Belgium.
| | - Sami Barrit
- Department of Neurosurgery, Hôpital Erasme, Université Libre de Bruxelles (ULB), Brussels, Belgium
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Gouel P, Hapdey S, Dumouchel A, Gardin I, Torfeh E, Hinault P, Vera P, Thureau S, Gensanne D. Synthetic MRI for Radiotherapy Planning for Brain and Prostate Cancers: Phantom Validation and Patient Evaluation. Front Oncol 2022; 12:841761. [PMID: 35515105 PMCID: PMC9065558 DOI: 10.3389/fonc.2022.841761] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 03/15/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose We aimed to evaluate the accuracy of T1 and T2 mappings derived from a multispectral pulse sequence (magnetic resonance image compilation, MAGiC®) on 1.5-T MRI and with conventional sequences [gradient echo with variable flip angle (GRE-VFA) and multi-echo spin echo (ME-SE)] compared to the reference values for the purpose of radiotherapy treatment planning. Methods The accuracy of T1 and T2 measurements was evaluated with 2 coils [head and neck unit (HNU) and BODY coils] on phantoms using descriptive statistics and Bland–Altman analysis. The reproducibility and repeatability of T1 and T2 measurements were performed on 15 sessions with the HNU coil. The T1 and T2 synthetic sequences obtained by both methods were evaluated according to quality assurance (QA) requirements for radiotherapy. T1 and T2in vivo measurements of the brain or prostate tissues of two groups of five subjects were also compared. Results The phantom results showed good agreement (mean bias, 8.4%) between the two measurement methods for T1 values between 490 and 2,385 ms and T2 values between 25 and 400 ms. MAGiC® gave discordant results for T1 values below 220 ms (bias with the reference values, from 38% to 1,620%). T2 measurements were accurately estimated below 400 ms (mean bias, 8.5%) by both methods. The QA assessments are in agreement with the recommendations of imaging for contouring purposes for radiotherapy planning. On patient data of the brain and prostate, the measurements of T1 and T2 by the two quantitative MRI (qMRI) methods were comparable (max difference, <7%). Conclusion This study shows that the accuracy, reproducibility, and repeatability of the multispectral pulse sequence (MAGiC®) were compatible with its use for radiotherapy treatment planning in a range of values corresponding to soft tissues. Even validated for brain imaging, MAGiC® could potentially be used for prostate qMRI.
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Affiliation(s)
- Pierrick Gouel
- Quantification en Imagerie Fonctionnelle-Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes Equipe d'accueil 4108 (QuantIF-LITIS EA4108), University of Rouen, Rouen, France.,Imaging Department, Henri Becquerel Cancer Center, Rouen, France
| | - Sebastien Hapdey
- Quantification en Imagerie Fonctionnelle-Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes Equipe d'accueil 4108 (QuantIF-LITIS EA4108), University of Rouen, Rouen, France.,Imaging Department, Henri Becquerel Cancer Center, Rouen, France
| | - Arthur Dumouchel
- Imaging Department, Henri Becquerel Cancer Center, Rouen, France
| | - Isabelle Gardin
- Quantification en Imagerie Fonctionnelle-Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes Equipe d'accueil 4108 (QuantIF-LITIS EA4108), University of Rouen, Rouen, France.,Imaging Department, Henri Becquerel Cancer Center, Rouen, France.,Radiotherapy Department, Henri Becquerel Cancer Center, Rouen, France
| | - Eva Torfeh
- Radiotherapy Department, Henri Becquerel Cancer Center, Rouen, France
| | - Pauline Hinault
- Quantification en Imagerie Fonctionnelle-Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes Equipe d'accueil 4108 (QuantIF-LITIS EA4108), University of Rouen, Rouen, France
| | - Pierre Vera
- Quantification en Imagerie Fonctionnelle-Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes Equipe d'accueil 4108 (QuantIF-LITIS EA4108), University of Rouen, Rouen, France.,Imaging Department, Henri Becquerel Cancer Center, Rouen, France
| | - Sebastien Thureau
- Quantification en Imagerie Fonctionnelle-Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes Equipe d'accueil 4108 (QuantIF-LITIS EA4108), University of Rouen, Rouen, France.,Imaging Department, Henri Becquerel Cancer Center, Rouen, France.,Radiotherapy Department, Henri Becquerel Cancer Center, Rouen, France
| | - David Gensanne
- Quantification en Imagerie Fonctionnelle-Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes Equipe d'accueil 4108 (QuantIF-LITIS EA4108), University of Rouen, Rouen, France.,Imaging Department, Henri Becquerel Cancer Center, Rouen, France.,Radiotherapy Department, Henri Becquerel Cancer Center, Rouen, France
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Wang Q, Wang G, Sun Q, Sun DH. Application of MAGnetic resonance imaging compilation in acute ischemic stroke. World J Clin Cases 2021; 9:10828-10837. [PMID: 35047594 PMCID: PMC8678888 DOI: 10.12998/wjcc.v9.i35.10828] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 08/31/2021] [Accepted: 10/20/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Synthetic magnetic resonance imaging (MRI) MAGnetic resonance imaging compilation (MAGiC) is a new MRI technology. Conventional T1, T2, T2-fluid-attenuated inversion recovery (FLAIR) contrast images, quantitative images of T1 and T2 mapping, and MAGiC phase sensitive inversion recovery (PSIR) Vessel cerebrovascular images can be obtained simultaneously through post-processing at the same time after completing a scan. In recent years, studies have reported that MAGiC can be applied to patients with acute ischemic stroke. We hypothesized that the synthetic MRI vascular screening scheme can evaluate the degree of cerebral artery stenosis in patients with acute ischemic stroke. AIM To explore the application value of vascular images obtained by synthetic MRI in diagnosing acute ischemic stroke. METHODS A total of 64 patients with acute ischemic stroke were selected and examined by MRI in the current retrospective cohort study. The scanning sequences included traditional T1, T2, and T2-FLAIR, three-dimensional time-of-flight magnetic resonance angiography (3D TOF MRA), diffusion-weighted imaging (DWI), and synthetic MRI. Conventional contrast images (T1, T2, and T2-FLAIR) and intracranial vessel images (MAGiC PSIR Vessel] were automatically reconstructed using synthetic MRI raw data. The contrast-to-noise ratio (CNR) values of traditional T1, T2, and T2-FLAIR images and MAGiC reconstructed T1, T2, and T2-FLAIR images in DWI diffusion restriction areas were measured and compared. MAGiC PSIR Vessel and TOF MRA images were used to measure and calculate the stenosis degree of bilateral middle cerebral artery stenosis areas. The consistency of MAGiC PSIR Vessel and TOF MRA in displaying the degree of vascular stenosis with computed tomography angiography (CTA) was compared. RESULTS Among the 64 patients with acute ischemic stroke, 79 vascular stenosis areas showed that the correlation between MAGiC PSIR Vessel and CTA (r = 0.90, P < 0.01) was higher than that between TOF MRA and CTA (r = 0.84, P < 0.01). With a degree of vascular stenosis > 50% assessed by CTA as a reference, the area under the receiver operating characteristic (ROC) curve of MAGiC PSIR Vessel [area under the curve (AUC) = 0.906, P < 0.01] was higher than that of TOF MRA (AUC = 0.790, P < 0.01). Among the 64 patients with acute ischemic stroke, 39 were scanned for traditional T1, T2, and T2-FLAIR images and MAGiC images simultaneously, and CNR values in DWI diffusion restriction areas were measured, which were: Traditional T2 = 21.2, traditional T1 = -6.7, and traditional T2-FLAIR = 11.9; and MAGiC T2 = 7.1, MAGiC T1 = -3.9, and MAGiC T2-FLAIR = 4.5. CONCLUSION The synthetic MRI vascular screening scheme for patients with acute ischemic stroke can accurately evaluate the degree of bilateral middle cerebral artery stenosis, which is of great significance to early thrombolytic interventional therapy and improving patients' quality of life.
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Affiliation(s)
- Qi Wang
- Department of Radiology, The Stroke Hospital of Liaoning Province, Shenyang 110101, Liaoning Province, China
| | - Gang Wang
- Department of Radiology, The Stroke Hospital of Liaoning Province, Shenyang 110101, Liaoning Province, China
| | - Qiang Sun
- Department of Radiology, The Stroke Hospital of Liaoning Province, Shenyang 110101, Liaoning Province, China
| | - Di-He Sun
- Department of Radiology, The Stroke Hospital of Liaoning Province, Shenyang 110101, Liaoning Province, China
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