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4. Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence

Author

Bai, Xiang, Wang, Hanchen, Ma, Liya, Xu, Yongchao, Gan, Jiefeng, Fan, Ziwei, Yang, Fan, Ma, Ke, Yang, Jiehua, Bai, Song, Shu, Chang, Zou, Xinyu, Huang, Renhao, Zhang, Changzheng, Liu, Xiaowu, Tu, Dandan, Xu, Chuou, Zhang, Wenqing, Wang, Xi, Chen, Anguo, Zeng, Yu, Yang, Dehua, Wang, Ming-Wei, Holalkere, Nagaraj, Halin, Neil J., Kamel, Ihab R., Wu, Jia, Peng, Xuehua, Wang, Xiang, Shao, Jianbo, Mongkolwat, Pattanasak, Zhang, Jianjun, Liu, Weiyang, Roberts, Michael, Teng, Zhongzhao, Beer, Lucian, Sanchez, Lorena Escudero, Sala, Evis, Rubin, Daniel, Weller, Adrian, Lasenby, Joan, Zheng, Chuangsheng, Wang, Jianming, Li, Zhen, Schönlieb, Carola-Bibiane, and Xia, Tian

Subjects
Computer Science - Artificial Intelligence, Computer Science - Cryptography and Security
Abstract

Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses. However, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalised model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the AI model can be distributedly trained and independently executed at each host institution under a federated learning framework (FL) without data sharing. Here we show that our FL model outperformed all the local models by a large yield (test sensitivity /specificity in China: 0.973/0.951, in the UK: 0.730/0.942), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals leaving out the FL) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans (CTs) from 3,336 patients collected from 23 hospitals located in China and the UK. Collectively, our work advanced the prospects of utilising federated learning for privacy-preserving AI in digital health., Comment: Nature Machine Intelligence

Published
2021
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6. Identification of high risk clinical and imaging features for intracranial artery dissection using high-resolution cardiovascular magnetic resonance

Author

Shi, Zhang, Tian, Xia, Tian, Bing, Meddings, Zakaria, Zhang, Xuefeng, Li, Jing, Saloner, David, Liu, Qi, Teng, Zhongzhao, and Lu, Jianping

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Brain Disorders, Neurosciences, Cardiovascular, Biomedical Imaging, Clinical Research, Detection, screening and diagnosis, 4.2 Evaluation of markers and technologies, Aortic Dissection, Arteries, Dissection, Humans, Magnetic Resonance Angiography, Magnetic Resonance Imaging, Magnetic Resonance Spectroscopy, Predictive Value of Tests, Intracranial artery dissection, Stroke, Cardiovascular magnetic resonance, MRI, Intramural hematoma, Cardiorespiratory Medicine and Haematology, Nuclear Medicine & Medical Imaging, Cardiovascular medicine and haematology
Abstract

BackgroundIntracranial artery dissection (IAD) often causes headache and cerebral vascular ischemic events. The imaging characteristics of IAD remain unclear. This study aims to characterize the appearance of culprit and non-culprit IAD using high-resolution cardiovascular magnetic resonance imaging (hrCMR) and quantify the incremental value of hrCMR in identifying higher risk lesions.MethodsImaging data from patients who underwent intervention examination or treatment using digital subtraction angiography (DSA) and hrCMR using a 3 T CMR system within 30 days after the onset of neurological symptoms were collected. The CMR protocol included diffusion-weighted imaging (DWI), black blood T1-, T2- and contrast-enhanced T1-weighted sequences. Lesions were classified as culprit and non-culprit according to imaging findings and patient clinical presentations. Univariate and multivariate analyses were performed to assess the difference between culprit and non-culprit lesions and complementary value of hrCMR in identifying higher risk lesions.ResultsIn total, 75 patients were included in this study. According to the morphology, lesions could be classified into five types: Type I, classical dissection (n = 50); Type II, fusiform aneurysm (n = 1); Type III, long dissected aneurysm (n = 3); Type IV, dolichoectatic dissecting aneurysm (n = 9) and Type V, saccular aneurysm (n = 12). Regression analyses showed that age and hypertension were both associated with culprit lesions (age: OR, 0.83; 95% CI 0.75-0.92; p

Published
2021

8. Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

Author

Roberts, Michael, Driggs, Derek, Thorpe, Matthew, Gilbey, Julian, Yeung, Michael, Ursprung, Stephan, Aviles-Rivero, Angelica I., Etmann, Christian, McCague, Cathal, Beer, Lucian, Weir-McCall, Jonathan R., Teng, Zhongzhao, Gkrania-Klotsas, Effrossyni, Rudd, James H. F., Sala, Evis, and Schönlieb, Carola-Bibiane

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing, Statistics - Machine Learning
Abstract

Machine learning methods offer great promise for fast and accurate detection and prognostication of COVID-19 from standard-of-care chest radiographs (CXR) and computed tomography (CT) images. Many articles have been published in 2020 describing new machine learning-based models for both of these tasks, but it is unclear which are of potential clinical utility. In this systematic review, we search EMBASE via OVID, MEDLINE via PubMed, bioRxiv, medRxiv and arXiv for published papers and preprints uploaded from January 1, 2020 to October 3, 2020 which describe new machine learning models for the diagnosis or prognosis of COVID-19 from CXR or CT images. Our search identified 2,212 studies, of which 415 were included after initial screening and, after quality screening, 61 studies were included in this systematic review. Our review finds that none of the models identified are of potential clinical use due to methodological flaws and/or underlying biases. This is a major weakness, given the urgency with which validated COVID-19 models are needed. To address this, we give many recommendations which, if followed, will solve these issues and lead to higher quality model development and well documented manuscripts., Comment: 35 pages, 3 figures, 2 tables, updated to the period 1 January 2020 - 3 October 2020

Published
2020
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9. A Bayesian Updating Scheme for Pandemics: Estimating the Infection Dynamics of COVID-19

Author

Wang, Shuo, Yang, Xian, Li, Ling, Nadler, Philip, Arcucci, Rossella, Huang, Yuan, Teng, Zhongzhao, and Guo, Yike

Subjects
Quantitative Biology - Populations and Evolution, Physics - Physics and Society
Abstract

Epidemic models play a key role in understanding and responding to the emerging COVID-19 pandemic. Widely used compartmental models are static and are of limited use to evaluate intervention strategies with the emerging pandemic. Applying the technology of data assimilation, we propose a Bayesian updating approach for estimating epidemiological parameters using observable information for the purpose of assessing the impacts of different intervention strategies. We adopt a concise renewal model and propose new parameters by disentangling the reduction of instantaneous reproduction number Rt into mitigation and suppression factors for quantifying intervention impacts at a finer granularity. Then we developed a data assimilation framework for estimating these parameters including constructing an observation function and developing a Bayesian updating scheme. A statistical analysis framework is then built to quantify the impact of intervention strategies by monitoring the evolution of these estimated parameters. By Investigating the impacts of intervention measures of European countries, the United States and Wuhan with the framework, we reveal the effects of interventions in these countries and the resurgence risk in the USA., Comment: This work is submitted to IEEE Computational Intelligence Magzine Special Issues on Computational Intelligence for Combating COVID-19

Published
2020
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10. Efficient Deep Representation Learning by Adaptive Latent Space Sampling

Author

Mo, Yuanhan, Wang, Shuo, Dai, Chengliang, Zhou, Rui, Teng, Zhongzhao, Bai, Wenjia, and Guo, Yike

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Supervised deep learning requires a large amount of training samples with annotations (e.g. label class for classification task, pixel- or voxel-wised label map for segmentation tasks), which are expensive and time-consuming to obtain. During the training of a deep neural network, the annotated samples are fed into the network in a mini-batch way, where they are often regarded of equal importance. However, some of the samples may become less informative during training, as the magnitude of the gradient start to vanish for these samples. In the meantime, other samples of higher utility or hardness may be more demanded for the training process to proceed and require more exploitation. To address the challenges of expensive annotations and loss of sample informativeness, here we propose a novel training framework which adaptively selects informative samples that are fed to the training process. The adaptive selection or sampling is performed based on a hardness-aware strategy in the latent space constructed by a generative model. To evaluate the proposed training framework, we perform experiments on three different datasets, including MNIST and CIFAR-10 for image classification task and a medical image dataset IVUS for biophysical simulation task. On all three datasets, the proposed framework outperforms a random sampling method, which demonstrates the effectiveness of proposed framework.

Published
2020

15. Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Author

Bakas, Spyridon, Reyes, Mauricio, Jakab, Andras, Bauer, Stefan, Rempfler, Markus, Crimi, Alessandro, Shinohara, Russell Takeshi, Berger, Christoph, Ha, Sung Min, Rozycki, Martin, Prastawa, Marcel, Alberts, Esther, Lipkova, Jana, Freymann, John, Kirby, Justin, Bilello, Michel, Fathallah-Shaykh, Hassan, Wiest, Roland, Kirschke, Jan, Wiestler, Benedikt, Colen, Rivka, Kotrotsou, Aikaterini, Lamontagne, Pamela, Marcus, Daniel, Milchenko, Mikhail, Nazeri, Arash, Weber, Marc-Andre, Mahajan, Abhishek, Baid, Ujjwal, Gerstner, Elizabeth, Kwon, Dongjin, Acharya, Gagan, Agarwal, Manu, Alam, Mahbubul, Albiol, Alberto, Albiol, Antonio, Albiol, Francisco J., Alex, Varghese, Allinson, Nigel, Amorim, Pedro H. A., Amrutkar, Abhijit, Anand, Ganesh, Andermatt, Simon, Arbel, Tal, Arbelaez, Pablo, Avery, Aaron, Azmat, Muneeza, B., Pranjal, Bai, W, Banerjee, Subhashis, Barth, Bill, Batchelder, Thomas, Batmanghelich, Kayhan, Battistella, Enzo, Beers, Andrew, Belyaev, Mikhail, Bendszus, Martin, Benson, Eze, Bernal, Jose, Bharath, Halandur Nagaraja, Biros, George, Bisdas, Sotirios, Brown, James, Cabezas, Mariano, Cao, Shilei, Cardoso, Jorge M., Carver, Eric N, Casamitjana, Adrià, Castillo, Laura Silvana, Catà, Marcel, Cattin, Philippe, Cerigues, Albert, Chagas, Vinicius S., Chandra, Siddhartha, Chang, Yi-Ju, Chang, Shiyu, Chang, Ken, Chazalon, Joseph, Chen, Shengcong, Chen, Wei, Chen, Jefferson W, Chen, Zhaolin, Cheng, Kun, Choudhury, Ahana Roy, Chylla, Roger, Clérigues, Albert, Colleman, Steven, Colmeiro, Ramiro German Rodriguez, Combalia, Marc, Costa, Anthony, Cui, Xiaomeng, Dai, Zhenzhen, Dai, Lutao, Daza, Laura Alexandra, Deutsch, Eric, Ding, Changxing, Dong, Chao, Dong, Shidu, Dudzik, Wojciech, Eaton-Rosen, Zach, Egan, Gary, Escudero, Guilherme, Estienne, Théo, Everson, Richard, Fabrizio, Jonathan, Fan, Yong, Fang, Longwei, Feng, Xue, Ferrante, Enzo, Fidon, Lucas, Fischer, Martin, French, Andrew P., Fridman, Naomi, Fu, Huan, Fuentes, David, Gao, Yaozong, Gates, Evan, Gering, David, Gholami, Amir, Gierke, Willi, Glocker, Ben, Gong, Mingming, González-Villá, Sandra, Grosges, T., Guan, Yuanfang, Guo, Sheng, Gupta, Sudeep, Han, Woo-Sup, Han, Il Song, Harmuth, Konstantin, He, Huiguang, Hernández-Sabaté, Aura, Herrmann, Evelyn, Himthani, Naveen, Hsu, Winston, Hsu, Cheyu, Hu, Xiaojun, Hu, Xiaobin, Hu, Yan, Hu, Yifan, Hua, Rui, Huang, Teng-Yi, Huang, Weilin, Van Huffel, Sabine, Huo, Quan, HV, Vivek, Iftekharuddin, Khan M., Isensee, Fabian, Islam, Mobarakol, Jackson, Aaron S., Jambawalikar, Sachin R., Jesson, Andrew, Jian, Weijian, Jin, Peter, Jose, V Jeya Maria, Jungo, Alain, Kainz, B, Kamnitsas, Konstantinos, Kao, Po-Yu, Karnawat, Ayush, Kellermeier, Thomas, Kermi, Adel, Keutzer, Kurt, Khadir, Mohamed Tarek, Khened, Mahendra, Kickingereder, Philipp, Kim, Geena, King, Nik, Knapp, Haley, Knecht, Urspeter, Kohli, Lisa, Kong, Deren, Kong, Xiangmao, Koppers, Simon, Kori, Avinash, Krishnamurthi, Ganapathy, Krivov, Egor, Kumar, Piyush, Kushibar, Kaisar, Lachinov, Dmitrii, Lambrou, Tryphon, Lee, Joon, Lee, Chengen, Lee, Yuehchou, Lee, M, Lefkovits, Szidonia, Lefkovits, Laszlo, Levitt, James, Li, Tengfei, Li, Hongwei, Li, Wenqi, Li, Hongyang, Li, Xiaochuan, Li, Yuexiang, Li, Heng, Li, Zhenye, Li, Xiaoyu, Li, Zeju, Li, XiaoGang, Lin, Zheng-Shen, Lin, Fengming, Lio, Pietro, Liu, Chang, Liu, Boqiang, Liu, Xiang, Liu, Mingyuan, Liu, Ju, Liu, Luyan, Llado, Xavier, Lopez, Marc Moreno, Lorenzo, Pablo Ribalta, Lu, Zhentai, Luo, Lin, Luo, Zhigang, Ma, Jun, Ma, Kai, Mackie, Thomas, Madabushi, Anant, Mahmoudi, Issam, Maier-Hein, Klaus H., Maji, Pradipta, Mammen, CP, Mang, Andreas, Manjunath, B. S., Marcinkiewicz, Michal, McDonagh, S, McKenna, Stephen, McKinley, Richard, Mehl, Miriam, Mehta, Sachin, Mehta, Raghav, Meier, Raphael, Meinel, Christoph, Merhof, Dorit, Meyer, Craig, Miller, Robert, Mitra, Sushmita, Moiyadi, Aliasgar, Molina-Garcia, David, Monteiro, Miguel A. B., Mrukwa, Grzegorz, Myronenko, Andriy, Nalepa, Jakub, Ngo, Thuyen, Nie, Dong, Ning, Holly, Niu, Chen, Nuechterlein, Nicholas K, Oermann, Eric, Oliveira, Arlindo, Oliveira, Diego D. C., Oliver, Arnau, Osman, Alexander F. I., Ou, Yu-Nian, Ourselin, Sebastien, Paragios, Nikos, Park, Moo Sung, Paschke, Brad, Pauloski, J. Gregory, Pawar, Kamlesh, Pawlowski, Nick, Pei, Linmin, Peng, Suting, Pereira, Silvio M., Perez-Beteta, Julian, Perez-Garcia, Victor M., Pezold, Simon, Pham, Bao, Phophalia, Ashish, Piella, Gemma, Pillai, G. N., Piraud, Marie, Pisov, Maxim, Popli, Anmol, Pound, Michael P., Pourreza, Reza, Prasanna, Prateek, Prkovska, Vesna, Pridmore, Tony P., Puch, Santi, Puybareau, Élodie, Qian, Buyue, Qiao, Xu, Rajchl, Martin, Rane, Swapnil, Rebsamen, Michael, Ren, Hongliang, Ren, Xuhua, Revanuru, Karthik, Rezaei, Mina, Rippel, Oliver, Rivera, Luis Carlos, Robert, Charlotte, Rosen, Bruce, Rueckert, Daniel, Safwan, Mohammed, Salem, Mostafa, Salvi, Joaquim, Sanchez, Irina, Sánchez, Irina, Santos, Heitor M., Sartor, Emmett, Schellingerhout, Dawid, Scheufele, Klaudius, Scott, Matthew R., Scussel, Artur A., Sedlar, Sara, Serrano-Rubio, Juan Pablo, Shah, N. Jon, Shah, Nameetha, Shaikh, Mazhar, Shankar, B. Uma, Shboul, Zeina, Shen, Haipeng, Shen, Dinggang, Shen, Linlin, Shen, Haocheng, Shenoy, Varun, Shi, Feng, Shin, Hyung Eun, Shu, Hai, Sima, Diana, Sinclair, M, Smedby, Orjan, Snyder, James M., Soltaninejad, Mohammadreza, Song, Guidong, Soni, Mehul, Stawiaski, Jean, Subramanian, Shashank, Sun, Li, Sun, Roger, Sun, Jiawei, Sun, Kay, Sun, Yu, Sun, Guoxia, Sun, Shuang, Suter, Yannick R, Szilagyi, Laszlo, Talbar, Sanjay, Tao, Dacheng, Teng, Zhongzhao, Thakur, Siddhesh, Thakur, Meenakshi H, Tharakan, Sameer, Tiwari, Pallavi, Tochon, Guillaume, Tran, Tuan, Tsai, Yuhsiang M., Tseng, Kuan-Lun, Tuan, Tran Anh, Turlapov, Vadim, Tustison, Nicholas, Vakalopoulou, Maria, Valverde, Sergi, Vanguri, Rami, Vasiliev, Evgeny, Ventura, Jonathan, Vera, Luis, Vercauteren, Tom, Verrastro, C. A., Vidyaratne, Lasitha, Vilaplana, Veronica, Vivekanandan, Ajeet, Wang, Guotai, Wang, Qian, Wang, Chiatse J., Wang, Weichung, Wang, Duo, Wang, Ruixuan, Wang, Yuanyuan, Wang, Chunliang, Wen, Ning, Wen, Xin, Weninger, Leon, Wick, Wolfgang, Wu, Shaocheng, Wu, Qiang, Wu, Yihong, Xia, Yong, Xu, Yanwu, Xu, Xiaowen, Xu, Peiyuan, Yang, Tsai-Ling, Yang, Xiaoping, Yang, Hao-Yu, Yang, Junlin, Yang, Haojin, Yang, Guang, Yao, Hongdou, Ye, Xujiong, Yin, Changchang, Young-Moxon, Brett, Yu, Jinhua, Yue, Xiangyu, Zhang, Songtao, Zhang, Angela, Zhang, Kun, Zhang, Xuejie, Zhang, Lichi, Zhang, Xiaoyue, Zhang, Yazhuo, Zhang, Lei, Zhang, Jianguo, Zhang, Xiang, Zhang, Tianhao, Zhao, Sicheng, Zhao, Yu, Zhao, Xiaomei, Zhao, Liang, Zheng, Yefeng, Zhong, Liming, Zhou, Chenhong, Zhou, Xiaobing, Zhou, Fan, Zhu, Hongtu, Zhu, Jin, Zhuge, Ying, Zong, Weiwei, Kalpathy-Cramer, Jayashree, Farahani, Keyvan, Davatzikos, Christos, van Leemput, Koen, and Menze, Bjoern

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset., Comment: The International Multimodal Brain Tumor Segmentation (BraTS) Challenge

Published
2018

17. Coronary CTA-based vascular radiomics predicts atherosclerosis development proximal to LAD myocardial bridging.

Author

Chen, Yan Chun, Zheng, Jin, Zhou, Fan, Tao, Xin Wei, Chen, Qian, Feng, Yun, Su, Yun Yan, Zhang, Yu, Liu, Tongyuan, Zhou, Chang Sheng, Tang, Chun Xiang, Weir-McCall, Jonathan, Teng, Zhongzhao, and Zhang, Long Jiang

Subjects
MYOCARDIAL infarction, PREDICTION models, RESEARCH funding, BLOOD vessels, COMPUTED tomography, RADIOMICS, ATHEROSCLEROSIS, TERTIARY care, DESCRIPTIVE statistics, LONGITUDINAL method, CORONARY angiography
Abstract

Aims Cardiac cycle morphological changes can accelerate plaque growth proximal to myocardial bridging (MB) in the left anterior descending artery (LAD). To assess coronary computed tomography angiography (CCTA)-based vascular radiomics for predicting proximal plaque development in LAD MB. Methods and results Patients with repeated CCTA scans showing LAD MB without proximal plaque in index CCTA were included from Jinling Hospital as a development set. They were divided into training and internal testing in an 8:2 ratio. Patients from four other tertiary hospitals were set as external validation set. The endpoint was proximal plaque development of LAD MB in follow-up CCTA. Four vascular radiomics models were built: MB centreline (MB CL), proximal MB CL (pMB CL), MB cross-section (MB CS), and proximal MB CS (pMB CS), whose performances were evaluated using area under the receiver operating characteristic curve (AUC), integrated discrimination improvement (IDI), and net reclassification improvement (NRI). In total, 295 patients were included in the development (n = 192; median age, 54 ± 11 years; 137 men) and external validation sets (n = 103; median age, 57 ± 9 years; 57 men). The pMB CS vascular radiomics model exhibited higher AUCs in training, internal test, and external sets (AUC = 0.78, 0.75, 0.75) than the clinical and anatomical model (all P < 0.05). Integration of the pMB CS vascular radiomics model significantly raised the AUC of the clinical and anatomical model from 0.56 to 0.75 (P = 0.002), along with enhanced NRI [0.76 (0.37–1.14), P < 0.001] and IDI [0.17 (0.07–0.26), P < 0.001] in the external validation set. Conclusion The CCTA-based pMB CS vascular radiomics model can predict plaque development in LAD MB. [ABSTRACT FROM AUTHOR]

Published
2024
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23. Ex-vivo imaging and plaque type classification of intracranial atherosclerotic plaque using high resolution MRI

Author

Jiang, Yuanliang, Zhu, Chengcheng, Peng, Wenjia, Degnan, Andrew J, Chen, Luguang, Wang, Xinrui, Liu, Qi, Wang, Yang, Xiang, Zhenzhen, Teng, Zhongzhao, Saloner, David, and Lu, Jianping

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Clinical Sciences, Aging, Cardiovascular, Biomedical Imaging, Neurosciences, Atherosclerosis, Heart Disease, Detection, screening and diagnosis, 4.2 Evaluation of markers and technologies, Aged, Basilar Artery, Cadaver, Carotid Artery Diseases, Cerebral Arteries, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, Middle Cerebral Artery, Observer Variation, Plaque, Atherosclerotic, Stroke, Magnetic resonance imaging, Ex-vivo, Vessel wall imaging, Cardiorespiratory Medicine and Haematology, Cardiovascular System & Hematology, Cardiovascular medicine and haematology, Clinical sciences
Abstract

Background and aimsRecent development of high resolution MRI techniques have enabled imaging of intracranial atherosclerotic plaque in vivo. However, identifying plaque composition remains challenging given the small size and the lack of histological validation. This study aims to quantify the relaxation times of intracranial plaque components ex vivo at 3 T and to determine whether multi-contrast MRI could classify intracranial plaque according to the American Heart Association classification with histological validation.MethodsA total of 53 intracranial arteries with atherosclerotic plaques from 20 cadavers (11 male, age 73.8 ± 10.9) were excised. Quantitative T1/T2/T2* mapping sequences and multi-contrast fast-spin echo sequences (T1, T2, proton-density weighted and short time inversion recovery) were acquired. Plaque components including: fibrous cap, lipid core, fibrous tissue, calcification, and healthy wall were segmented on histology, and their relaxation times were derived from quantitative images. Two radiologists independently classified plaque type blinded to the histology results.ResultsRelaxation times of plaque components are distinct and different. T2 and T2* values of lipid core are lower than fibrous cap (p = 0.026 & p

Published
2016

27. Author Correction: Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence

Author

Bai, Xiang, Wang, Hanchen, Ma, Liya, Xu, Yongchao, Gan, Jiefeng, Fan, Ziwei, Yang, Fan, Ma, Ke, Yang, Jiehua, Bai, Song, Shu, Chang, Zou, Xinyu, Huang, Renhao, Zhang, Changzheng, Liu, Xiaowu, Tu, Dandan, Xu, Chuou, Zhang, Wenqing, Wang, Xi, Chen, Anguo, Zeng, Yu, Yang, Dehua, Wang, Ming-Wei, Holalkere, Nagaraj, Halin, Neil J., Kamel, Ihab R., Wu, Jia, Peng, Xuehua, Wang, Xiang, Shao, Jianbo, Mongkolwat, Pattanasak, Zhang, Jianjun, Liu, Weiyang, Roberts, Michael, Teng, Zhongzhao, Beer, Lucian, Sanchez, Lorena E., Sala, Evis, Rubin, Daniel L., Weller, Adrian, Lasenby, Joan, Zheng, Chuansheng, Wang, Jianming, Li, Zhen, Schönlieb, Carola, and Xia, Tian

Published
2022
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29. Angiography-based hemodynamic features predict recurrent ischemic events after angioplasty and stenting of intracranial vertebrobasilar atherosclerotic stenosis

Author

Huang, Kangmo, primary, Yao, Weihe, additional, Zha, Mingming, additional, Qin, Shanmei, additional, Li, Yingle, additional, Xu, Yan, additional, Liu, Rui, additional, Ye, Ruidong, additional, Han, Yunfei, additional, Zhu, Wusheng, additional, Teng, Zhongzhao, additional, Du, Juan, additional, and Liu, Xinfeng, additional

Published
2023
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39. Clinical features, myocardial strain and tissue characteristics of heart failure with preserved ejection fraction in patients with obesity: a prospective cohort study

Author

He, Jian, primary, Yang, Wenjing, additional, Wu, Weichun, additional, Sun, Xiaoxin, additional, Li, Shuang, additional, Yin, Gang, additional, Zhuang, Baiyan, additional, Xu, Jing, additional, Zhou, Di, additional, Zhang, Yuhui, additional, Wang, Yining, additional, Zhu, Leyi, additional, Sharma, Piyush, additional, Sirajuddin, Arlene, additional, Teng, Zhongzhao, additional, Kureshi, Faraz, additional, Zhao, Shihua, additional, and Lu, Minjie, additional

Published
2023
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47. The effect of subbranch for the quantification of local hemodynamic environment in the coronary artery: a computed tomography angiography–based computational fluid dynamic analysis

Author

Shi, Yibing, primary, Zheng, Jin, additional, Yang, Ning, additional, Chen, Yang, additional, Sun, Jingxi, additional, Zhang, Ying, additional, Zhou, Xuanxuan, additional, Gao, Yongguang, additional, Li, Suqing, additional, Zhu, Haijing, additional, Acosta-Cabronero, Julio, additional, Xia, Ping, additional, and Teng, Zhongzhao, additional

Published
2022
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49. Predictors of ischemic events in patients with unilateral extracranial vertebral artery dissection: A single-center exploratory study

Author

Yan, Yanhong, Lu, Ziwei, Ding, Yafang, Pu, Jianhong, Hu, Chunhong, Teng, Zhongzhao, Hui, Pinjing, and Apollo - University of Cambridge Repository

Subjects
Neurology, vertebral artery dissection, color duplex ultrasonography, intraluminal thrombus, high resolution magnetic resonance imaging, Neurology (clinical), stroke
Abstract

ObjectiveExtracranial vertebral artery dissection (EVAD) is one of the main causes of stroke in young and middle-aged patients. However, the diagnosis is challenging. This study aimed to identify the characteristics of EVAD on color duplex ultrasonography (CDU) and high-resolution magnetic resonance imaging (hrMRI), hoping to improve the accuracy and determine the relative contribution of vessel findings and clinical factors to acute ischemic events.MethodsPatients with unilateral EVAD were recruited and divided into ischemia and non-ischemia groups. Clinical features of patients and the lesion location; a variety of signs which indicate dissection, including the presence of an intimal flap, double lumen, intramural hematoma, dissecting aneurysm, intraluminal thrombus, and irregular lumen; and other quantitative parameters of each dissected segment on CDU and hrMRI were reviewed, respectively. Multiple logistic regression was performed to explore the association between clinical, imaging characteristics, and ischemic events in patients with unilateral EVAD.ResultsNinety-six patients with unilateral EVAD who met the inclusion criteria were enrolled during a six-year period. Overall, 41 cases (42.7%) were confirmed as ischemic stroke (n = 40) or transient ischemic attack (n = 1) during the 48 h after the onset of symptoms. Men, infections during the last week, and smoking were more common in the ischemia group. Intraluminal thrombus and occlusion on CDU were more prevalent in patients with cerebral ischemia than in those without (36.6 vs. 5.5%; p < 0.001, and 39.0 vs. 9.1%; p = 0.001, respectively). On hrMRI, intraluminal thrombus and occlusion were also more frequent in the ischemia group than in the non-ischemia group (34.1 vs. 5.5%; p < 0.001, and 34.1 vs. 9.1%; p = 0.003, respectively). In addition, lesion length on hrMRI was significantly longer for patients with ischemia (81.5 ± 41.7 vs. 64.7 ± 30.8 mm; p = 0.025). In multivariable logistic regression analysis, male gender, infections during the last week, and the presence of intraluminal thrombus on CDU and hrMRI were independently associated with acute ischemic events.ConclusionMale sex, infections during the last week, and the presence of intraluminal thrombus due to dissection are associated with an increased risk of ischemic events in patients with unilateral EVAD.

Published
2022
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