1
|
Liu S, Qin P, Wang Z, Liu Y. Improved hypertensive stroke classification based on multi-scale feature fusion of head axial CT angiogram and multimodal learning. Phys Med 2024; 121:103359. [PMID: 38688073 DOI: 10.1016/j.ejmp.2024.103359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 03/22/2024] [Accepted: 04/19/2024] [Indexed: 05/02/2024] Open
Abstract
PURPOSE Strokes are severe cardiovascular and circulatory diseases with two main types: ischemic and hemorrhagic. Clinically, brain images such as computed tomography (CT) and computed tomography angiography (CTA) are widely used to recognize stroke types. However, few studies have combined imaging and clinical data to classify stroke or consider a factor as an Independent etiology. METHODS In this work, we propose a classification model that automatically distinguishes stroke types with hypertension as an independent etiology based on brain imaging and clinical data. We first present a preprocessing workflow for head axial CT angiograms, including noise reduction and feature enhancement of the images, followed by an extraction of regions of interest. Next, we develop a multi-scale feature fusion model that combines the location information of position features and the semantic information of deep features. Furthermore, we integrate brain imaging with clinical information through a multimodal learning model to achieve more reliable results. RESULTS Experimental results show our proposed models outperform state-of-the-art models on real imaging and clinical data, which reveals the potential of multimodal learning in brain disease diagnosis. CONCLUSION The proposed methodologies can be extended to create AI-driven diagnostic assistance technology for categorizing strokes.
Collapse
Affiliation(s)
- Shuting Liu
- School of Control Science and Engineering, Dalian University of Technology, Dalian, Liaoning 116024, China.
| | - Pan Qin
- School of Control Science and Engineering, Dalian University of Technology, Dalian, Liaoning 116024, China
| | - Zeyuan Wang
- School of Control Science and Engineering, Dalian University of Technology, Dalian, Liaoning 116024, China
| | - Yi Liu
- Central Hospital of Dalian University of Technology, Dalian, Liaoning 116033, China.
| |
Collapse
|
2
|
Miyamoto N, Ueno Y, Yamashiro K, Hira K, Kijima C, Kitora N, Iwao Y, Okuda K, Mishima S, Takahashi D, Ono K, Asari M, Miyazaki K, Hattori N. Stroke classification and treatment support system artificial intelligence for usefulness of stroke diagnosis. Front Neurol 2023; 14:1295642. [PMID: 38156087 PMCID: PMC10753815 DOI: 10.3389/fneur.2023.1295642] [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: 09/17/2023] [Accepted: 12/01/2023] [Indexed: 12/30/2023] Open
Abstract
Background and aims It is important to diagnose cerebral infarction at an early stage and select an appropriate treatment method. The number of stroke-trained physicians is unevenly distributed; thus, a shortage of specialists is a major problem in some regions. In this retrospective design study, we tested whether an artificial intelligence (AI) we built using computer-aided detection/diagnosis may help medical physicians to classify stroke for the appropriate treatment. Methods To build the Stroke Classification and Treatment Support System AI, the clinical data of 231 hospitalized patients with ischemic stroke from January 2016 to December 2017 were used for training the AI. To verify the diagnostic accuracy, 151 patients who were admitted for stroke between January 2018 and December 2018 were also enrolled. Results By utilizing multimodal data, such as DWI and ADC map images, as well as patient examination data, we were able to construct an AI that can explain the analysis results with a small amount of training data. Furthermore, the AI was able to classify with high accuracy (Cohort 1, evaluation data 88.7%; Cohort 2, validation data 86.1%). Conclusion In recent years, the treatment options for cerebral infarction have increased in number and complexity, making it even more important to provide appropriate treatment according to the initial diagnosis. This system could be used for initial treatment to automatically diagnose and classify strokes in hospitals where stroke-trained physicians are not available and improve the prognosis of cerebral infarction.
Collapse
Affiliation(s)
- Nobukazu Miyamoto
- Department of Neurology, Juntendo University School of Medicine, Tokyo, Japan
| | - Yuji Ueno
- Department of Neurology, Juntendo University School of Medicine, Tokyo, Japan
| | - Kazuo Yamashiro
- Department of Neurology, Juntendo University School of Medicine, Tokyo, Japan
| | - Kenichiro Hira
- Department of Neurology, Juntendo University School of Medicine, Tokyo, Japan
| | - Chikage Kijima
- Department of Neurology, Juntendo University School of Medicine, Tokyo, Japan
| | | | | | | | | | | | - Kazuto Ono
- Ohara Pharmaceutical Co., Ltd., Tokyo, Japan
| | - Mika Asari
- PARKINSON Laboratories Co., Ltd., Tokyo, Japan
| | | | - Nobutaka Hattori
- Department of Neurology, Juntendo University School of Medicine, Tokyo, Japan
| |
Collapse
|
3
|
Jiang D, Liao J, Zhao C, Zhao X, Lin R, Yang J, Li Z, Zhou Y, Zhu Y, Liang D, Hu Z, Wang H. Recognizing Pediatric Tuberous Sclerosis Complex Based on Multi-Contrast MRI and Deep Weighted Fusion Network. Bioengineering (Basel) 2023; 10:870. [PMID: 37508897 PMCID: PMC10375986 DOI: 10.3390/bioengineering10070870] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 06/24/2023] [Accepted: 07/12/2023] [Indexed: 07/30/2023] Open
Abstract
Multi-contrast magnetic resonance imaging (MRI) is wildly applied to identify tuberous sclerosis complex (TSC) children in a clinic. In this work, a deep convolutional neural network with multi-contrast MRI is proposed to diagnose pediatric TSC. Firstly, by combining T2W and FLAIR images, a new synthesis modality named FLAIR3 was created to enhance the contrast between TSC lesions and normal brain tissues. After that, a deep weighted fusion network (DWF-net) using a late fusion strategy is proposed to diagnose TSC children. In experiments, a total of 680 children were enrolled, including 331 healthy children and 349 TSC children. The experimental results indicate that FLAIR3 successfully enhances the visibility of TSC lesions and improves the classification performance. Additionally, the proposed DWF-net delivers a superior classification performance compared to previous methods, achieving an AUC of 0.998 and an accuracy of 0.985. The proposed method has the potential to be a reliable computer-aided diagnostic tool for assisting radiologists in diagnosing TSC children.
Collapse
Affiliation(s)
- Dian Jiang
- Research Centre for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China; (D.J.); (J.Y.); (Z.L.); (Y.Z.); (D.L.)
- University of Chinese Academy of Sciences, Beijing 100049, China;
| | - Jianxiang Liao
- Department of Neurology, Shenzhen Children’s Hospital, Shenzhen 518000, China; (J.L.); (X.Z.)
| | - Cailei Zhao
- Department of Radiology, Shenzhen Children’s Hospital, Shenzhen 518000, China;
| | - Xia Zhao
- Department of Neurology, Shenzhen Children’s Hospital, Shenzhen 518000, China; (J.L.); (X.Z.)
| | - Rongbo Lin
- Department of Emergency, Shenzhen Children’s Hospital, Shenzhen 518000, China;
| | - Jun Yang
- Research Centre for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China; (D.J.); (J.Y.); (Z.L.); (Y.Z.); (D.L.)
- University of Chinese Academy of Sciences, Beijing 100049, China;
| | - Zhichen Li
- Research Centre for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China; (D.J.); (J.Y.); (Z.L.); (Y.Z.); (D.L.)
- University of Chinese Academy of Sciences, Beijing 100049, China;
| | - Yihang Zhou
- Research Centre for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China; (D.J.); (J.Y.); (Z.L.); (Y.Z.); (D.L.)
- Research Department, Hong Kong Sanatorium & Hospital, Hong Kong 999077, China
| | - Yanjie Zhu
- University of Chinese Academy of Sciences, Beijing 100049, China;
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China
| | - Dong Liang
- Research Centre for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China; (D.J.); (J.Y.); (Z.L.); (Y.Z.); (D.L.)
- University of Chinese Academy of Sciences, Beijing 100049, China;
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China
| | - Zhanqi Hu
- Department of Neurology, Shenzhen Children’s Hospital, Shenzhen 518000, China; (J.L.); (X.Z.)
| | - Haifeng Wang
- University of Chinese Academy of Sciences, Beijing 100049, China;
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China
| |
Collapse
|
4
|
Zhang Y, Feng W, Wu Z, Li W, Tao L, Liu X, Zhang F, Gao Y, Huang J, Guo X. Deep-Learning Model of ResNet Combined with CBAM for Malignant-Benign Pulmonary Nodules Classification on Computed Tomography Images. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:1088. [PMID: 37374292 DOI: 10.3390/medicina59061088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 05/10/2023] [Accepted: 05/17/2023] [Indexed: 06/29/2023]
Abstract
Background and Objectives: Lung cancer remains a leading cause of cancer mortality worldwide. Accurately classifying benign pulmonary nodules and malignant ones is crucial for early diagnosis and improved patient outcomes. The purpose of this study is to explore the deep-learning model of ResNet combined with a convolutional block attention module (CBAM) for the differentiation between benign and malignant lung cancer, based on computed tomography (CT) images, morphological features, and clinical information. Methods and materials: In this study, 8241 CT slices containing pulmonary nodules were retrospectively included. A random sample comprising 20% (n = 1647) of the images was used as the test set, and the remaining data were used as the training set. ResNet combined CBAM (ResNet-CBAM) was used to establish classifiers on the basis of images, morphological features, and clinical information. Nonsubsampled dual-tree complex contourlet transform (NSDTCT) combined with SVM classifier (NSDTCT-SVM) was used as a comparative model. Results: The AUC and the accuracy of the CBAM-ResNet model were 0.940 and 0.867, respectively, in test set when there were only images as inputs. By combining the morphological features and clinical information, CBAM-ResNet shows better performance (AUC: 0.957, accuracy: 0.898). In comparison, a radiomic analysis using NSDTCT-SVM achieved AUC and accuracy values of 0.807 and 0.779, respectively. Conclusions: Our findings demonstrate that deep-learning models, combined with additional information, can enhance the classification performance of pulmonary nodules. This model can assist clinicians in accurately diagnosing pulmonary nodules in clinical practice.
Collapse
Affiliation(s)
- Yanfei Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Wei Feng
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Zhiyuan Wu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Weiming Li
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Lixin Tao
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Xiangtong Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Feng Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Yan Gao
- Department of Nuclear Medicine, Xuanwu Hospital Capital Medical University, Beijing 100053, China
| | - Jian Huang
- School of Mathematical Sciences, University College Cork, T12 YN60 Cork, Ireland
| | - Xiuhua Guo
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| |
Collapse
|
5
|
Lee KY, Liu CC, Chen DYT, Weng CL, Chiu HW, Chiang CH. Automatic detection and vascular territory classification of hyperacute staged ischemic stroke on diffusion weighted image using convolutional neural networks. Sci Rep 2023; 13:404. [PMID: 36624122 PMCID: PMC9829896 DOI: 10.1038/s41598-023-27621-4] [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: 10/14/2022] [Accepted: 01/04/2023] [Indexed: 01/11/2023] Open
Abstract
Automated ischemic stroke detection and classification according to its vascular territory is an essential step in stroke image evaluation, especially at hyperacute stage where mechanical thrombectomy may improve patients' outcome. This study aimed to evaluate the performance of various convolutional neural network (CNN) models on hyperacute staged diffusion-weighted images (DWI) for detection of ischemic stroke and classification into anterior circulation infarct (ACI), posterior circulation infarct (PCI) and normal image slices. In this retrospective study, 253 cases of hyperacute staged DWI were identified, downloaded and reviewed. After exclusion, DWI from 127 cases were used and we created a dataset containing total of 2119 image slices, and separates it into three groups, namely ACI (618 slices), PCI (149 slices) and normal (1352 slices). Two transfer learning based CNN models, namely Inception-v3, EfficientNet-b0 and one self-derived modified LeNet model were used. The performance of the models was evaluated and activation maps using gradient-weighted class activation mapping (Grad-Cam) technique were made. Inception-v3 had the best overall accuracy (86.3%), weighted F1 score (86.2%) and kappa score (0.715), followed by the modified LeNet (85.2% accuracy, 84.7% weighted F1 score and 0.693 kappa score). The EfficientNet-b0 had the poorest performance of 83.6% accuracy, 83% weighted F1 score and 0.662 kappa score. The activation map showed that one possible explanation for misclassification is due to susceptibility artifact. A sufficiently high performance can be achieved by using CNN model to detect ischemic stroke on hyperacute staged DWI and classify it according to vascular territory.
Collapse
Affiliation(s)
- Kun-Yu Lee
- grid.412955.e0000 0004 0419 7197Department of Medical Image, Shuang Ho Hospital, Taipei Medical University, No. 291, Zhongzheng Road, Zhonghe District, New Taipei City, 23561 Taiwan, ROC ,grid.412896.00000 0000 9337 0481Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, No. 250 Wu-Hsing Street, Taipei City, 11031 Taiwan, ROC
| | - Chia-Chuan Liu
- grid.412955.e0000 0004 0419 7197Department of Medical Image, Shuang Ho Hospital, Taipei Medical University, No. 291, Zhongzheng Road, Zhonghe District, New Taipei City, 23561 Taiwan, ROC ,grid.412896.00000 0000 9337 0481Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, No. 250 Wu-Hsing Street, Taipei City, 11031 Taiwan, ROC
| | - David Yen-Ting Chen
- grid.412955.e0000 0004 0419 7197Department of Medical Image, Shuang Ho Hospital, Taipei Medical University, No. 291, Zhongzheng Road, Zhonghe District, New Taipei City, 23561 Taiwan, ROC ,grid.412896.00000 0000 9337 0481Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, No. 250 Wu-Hsing Street, Taipei City, 11031 Taiwan, ROC
| | - Chi-Lun Weng
- grid.413878.10000 0004 0572 9327Department of Radiology, Ditmanson Medical Foundation Chia-Yi Christian Hospital, No. 539, Zhongxiao Rd., East Dist., Chiayi City, 600566 Taiwan, ROC
| | - Hung-Wen Chiu
- grid.412896.00000 0000 9337 0481Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University TW, No. 250 Wu-Hsing Street, Taipei City, 11031 Taiwan, ROC
| | - Chen-Hua Chiang
- Department of Medical Image, Shuang Ho Hospital, Taipei Medical University, No. 291, Zhongzheng Road, Zhonghe District, New Taipei City, 23561, Taiwan, ROC. .,Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, No. 250 Wu-Hsing Street, Taipei City, 11031, Taiwan, ROC.
| |
Collapse
|
6
|
Kuo H, Liu TW, Huang YP, Chin SC, Ro LS, Kuo HC. Differential Diagnostic Value of Machine Learning-Based Models for Embolic Stroke. Clin Appl Thromb Hemost 2023; 29:10760296231203663. [PMID: 37728185 PMCID: PMC10515586 DOI: 10.1177/10760296231203663] [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: 05/29/2023] [Revised: 08/10/2023] [Accepted: 09/11/2023] [Indexed: 09/21/2023] Open
Abstract
Cancer-associated thrombosis (CAT) and atrial fibrillation (AF)-related stroke are two subtypes of acute embolic stroke with distinct lesion patterns on diffusion weighted imaging (DWI). This pilot study aimed to evaluate the feasibility and performance of DWI-based machine learning models for differentiating between CAT and AF-related stroke. Patients with CAT and AF-related stroke were enrolled. In this pilot study with a small sample size, DWI images were augmented by flipping and/or contrast shifting to build convolutional neural network (CNN) predicative models. DWI images from 29 patients, including 9 patients with CAT and 20 with AF-related stroke, were analyzed. Training and testing accuracies of the DWI-based CNN model were 87.1% and 78.6%, respectively. Training and testing accuracies were 95.2% and 85.7%, respectively, for the second CNN model that combined DWI images with demographic/clinical characteristics. There were no significant differences in sensitivity, specificity, accuracy, and AUC between two CNN models (all P = n.s.).The DWI-based CNN model using data augmentation may be useful for differentiating CAT from AF-related stroke.
Collapse
Affiliation(s)
- HsunYu Kuo
- Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
- Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | - Tsai-Wei Liu
- Department of Neurology, Chang Gung Memorial Hospital at Linkou Medical Center and Chang Gung University College of Medicine, Taoyuan, Taiwan
| | - Yo-Ping Huang
- Department of Electrical Engineering, National Taipei University of Technology, Taipei, Taiwan
- Department of Computer Science and Information Engineering, National Taipei University, New Taipei City, Taiwan
- Department of Electrical Engineering, National Penghu University of Science and Technology, Penghu, Taiwan
- Fellow of the Institute of Electrical and Electronics Engineers, Taipei, Taiwan
- Fellow of the Institution of Engineering and Technology, Taipei, Taiwan
- Fellow of Chinese Automatic Control Society, Taipei, Taiwan
| | - Shy-Chyi Chin
- Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou Medical Center and Chang Gung University College of Medicine, Taoyuan, Taiwan
| | - Long-Sun Ro
- Department of Neurology, Chang Gung Memorial Hospital at Linkou Medical Center and Chang Gung University College of Medicine, Taoyuan, Taiwan
| | - Hung-Chou Kuo
- Department of Neurology, Chang Gung Memorial Hospital at Linkou Medical Center and Chang Gung University College of Medicine, Taoyuan, Taiwan
| |
Collapse
|
7
|
Zeng Y, Long C, Zhao W, Liu J. Predicting the Severity of Neurological Impairment Caused by Ischemic Stroke Using Deep Learning Based on Diffusion-Weighted Images. J Clin Med 2022; 11:jcm11144008. [PMID: 35887776 PMCID: PMC9325315 DOI: 10.3390/jcm11144008] [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: 04/15/2022] [Revised: 06/23/2022] [Accepted: 07/05/2022] [Indexed: 02/01/2023] Open
Abstract
Purpose: To develop a preliminary deep learning model that uses diffusion-weighted imaging (DWI) images to classify the severity of neurological impairment caused by ischemic stroke. Materials and Methods: This retrospective study included 851 ischemic stroke patients (711 patients in the training set and 140 patients in the test set). The patients’ NIHSS scores, which reflect the severity of neurological impairment, were reviewed upon admission and on Day 7 of hospitalization and were classified into two stages (stage 1 for NIHSS < 5 and stage 2 for NIHSS ≥ 5). A 3D-CNN was trained to predict the stage of NIHSS based on different preprocessed DWI images. The performance in predicting the severity of anterior and posterior circulation stroke was also investigated. The AUC, specificity, and sensitivity were calculated to evaluate the performance of the model. Results: Our proposed model obtained better performance in predicting the NIHSS stage on Day 7 of hospitalization than that at admission (best AUC 0.895 vs. 0.846). Model D trained with DWI images (normalized with z-score and resized to 256 × 256 × 64 voxels) achieved the best AUC of 0.846 in predicting the NIHSS stage at admission. Model E rained with DWI images (normalized with maximum−minimum and resized to 128 × 128 × 32 voxels) achieved the best AUC of 0.895 in predicting the NIHSS stage on Day 7 of hospitalization. Our model also showed promising performance in predicting the NIHSS stage on Day 7 of hospitalization for anterior and posterior circulation stroke, with the best AUCs of 0.905 and 0.903, respectively. Conclusions: Our proposed 3D-CNN model can effectively predict the neurological severity of IS using DWI images and performs better in predicting the NIHSS stage on Day 7 of hospitalization. The model also obtained promising performance in subgroup analysis, which can potentially help clinical decision making.
Collapse
Affiliation(s)
- Ying Zeng
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China;
- Department of Radiology, Xiangtan Central Hospital, Xiangtan 411199, China
| | - Chen Long
- Department of Stroke Unit, Xiangtan Central Hospital, Xiangtan 411199, China;
| | - Wei Zhao
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China;
- Clinical Research Center for Medical Imaging, Changsha 410011, China
- Correspondence: (W.Z.); (J.L.)
| | - Jun Liu
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China;
- Clinical Research Center for Medical Imaging, Changsha 410011, China
- Department of Radiology Quality Control Center, Changsha 410011, China
- Correspondence: (W.Z.); (J.L.)
| |
Collapse
|