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1. Robust Semi-supervised Multimodal Medical Image Segmentation via Cross Modality Collaboration

Author

Zhou, Xiaogen, Sun, Yiyou, Deng, Min, Chu, Winnie Chiu Wing, and Dou, Qi

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Multimodal learning leverages complementary information derived from different modalities, thereby enhancing performance in medical image segmentation. However, prevailing multimodal learning methods heavily rely on extensive well-annotated data from various modalities to achieve accurate segmentation performance. This dependence often poses a challenge in clinical settings due to limited availability of such data. Moreover, the inherent anatomical misalignment between different imaging modalities further complicates the endeavor to enhance segmentation performance. To address this problem, we propose a novel semi-supervised multimodal segmentation framework that is robust to scarce labeled data and misaligned modalities. Our framework employs a novel cross modality collaboration strategy to distill modality-independent knowledge, which is inherently associated with each modality, and integrates this information into a unified fusion layer for feature amalgamation. With a channel-wise semantic consistency loss, our framework ensures alignment of modality-independent information from a feature-wise perspective across modalities, thereby fortifying it against misalignments in multimodal scenarios. Furthermore, our framework effectively integrates contrastive consistent learning to regulate anatomical structures, facilitating anatomical-wise prediction alignment on unlabeled data in semi-supervised segmentation tasks. Our method achieves competitive performance compared to other multimodal methods across three tasks: cardiac, abdominal multi-organ, and thyroid-associated orbitopathy segmentations. It also demonstrates outstanding robustness in scenarios involving scarce labeled data and misaligned modalities.

Published
2024

2. Towards Non-invasive and Personalized Management of Breast Cancer Patients from Multiparametric MRI via A Large Mixture-of-Modality-Experts Model

Author

Luo, Luyang, Wu, Mingxiang, Li, Mei, Xin, Yi, Wang, Qiong, Vardhanabhuti, Varut, Chu, Winnie CW, Li, Zhenhui, Zhou, Juan, Rajpurkar, Pranav, and Chen, Hao

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

Breast magnetic resonance imaging (MRI) is the imaging technique with the highest sensitivity for detecting breast cancer and is routinely used for women at high risk. Despite the comprehensive multiparametric protocol of breast MRI, existing artificial intelligence-based studies predominantly rely on single sequences and have limited validation. Here we report a large mixture-of-modality-experts model (MOME) that integrates multiparametric MRI information within a unified structure, offering a noninvasive method for personalized breast cancer management. We have curated the largest multiparametric breast MRI dataset, involving 5,205 patients from three hospitals in the north, southeast, and southwest of China, for the development and extensive evaluation of our model. MOME demonstrated accurate and robust identification of breast cancer. It achieved comparable performance for malignancy recognition to that of four senior radiologists and significantly outperformed a junior radiologist, with 0.913 AUROC, 0.948 AUPRC, 0.905 F1 score, and 0.723 MCC. Our findings suggest that MOME could reduce the need for biopsies in BI-RADS 4 patients with a ratio of 7.3%, classify triple-negative breast cancer with an AUROC of 0.709, and predict pathological complete response to neoadjuvant chemotherapy with an AUROC of 0.694. The model further supports scalable and interpretable inference, adapting to missing modalities and providing decision explanations by highlighting lesions and measuring modality contributions. MOME exemplifies a discriminative, robust, scalable, and interpretable multimodal model, paving the way for noninvasive, personalized management of breast cancer patients based on multiparametric breast imaging data., Comment: 27 pages, 8 figures, 10 tables

Published
2024

3. Chemical Shift Encoding based Double Bonds Quantification in Triglycerides using Deep Image Prior

Author

Huang, Chaoxing, Yu, Ziqiang, Gao, Zijian, Shen, Qiuyi, Chan, Queenie, Wong, Vincent Wai-Sun, Chu, Winnie Chiu-Wing, and Chen, Weitian

Subjects
Physics - Medical Physics, Computer Science - Computer Vision and Pattern Recognition
Abstract

This study evaluated a deep learning-based method using Deep Image Prior (DIP) to quantify triglyceride double bonds from chemical-shift encoded multi-echo gradient echo images without network training. We employed a cost function based on signal constraints to iteratively update the neural network on a single dataset. The method was validated using phantom experiments and in vivo scans. Results showed close alignment between measured and reference double bond values, with phantom experiments yielding a Pearson correlation coefficient of 0.96 (p = .0005). In vivo results demonstrated good agreement in subcutaneous fat. We conclude that Deep Image Prior shows feasibility for quantifying double bonds and fatty acid content from chemical-shift encoded multi-echo MRI.

Published
2024

4. Automatic Ultrasound Curve Angle Measurement via Affinity Clustering for Adolescent Idiopathic Scoliosis Evaluation

Author

Zhou, Yihao, Lee, Timothy Tin-Yan, Lai, Kelly Ka-Lee, Wu, Chonglin, Lau, Hin Ting, Yang, De, Chan, Chui-Yi, Chu, Winnie Chiu-Wing, Cheng, Jack Chun-Yiu, Lam, Tsz-Ping, and Zheng, Yong-Ping

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Physics - Medical Physics
Abstract

The current clinical gold standard for evaluating adolescent idiopathic scoliosis (AIS) is X-ray radiography, using Cobb angle measurement. However, the frequent monitoring of the AIS progression using X-rays poses a challenge due to the cumulative radiation exposure. Although 3D ultrasound has been validated as a reliable and radiation-free alternative for scoliosis assessment, the process of measuring spinal curvature is still carried out manually. Consequently, there is a considerable demand for a fully automatic system that can locate bony landmarks and perform angle measurements. To this end, we introduce an estimation model for automatic ultrasound curve angle (UCA) measurement. The model employs a dual-branch network to detect candidate landmarks and perform vertebra segmentation on ultrasound coronal images. An affinity clustering strategy is utilized within the vertebral segmentation area to illustrate the affinity relationship between candidate landmarks. Subsequently, we can efficiently perform line delineation from a clustered affinity map for UCA measurement. As our method is specifically designed for UCA calculation, this method outperforms other state-of-the-art methods for landmark and line detection tasks. The high correlation between the automatic UCA and Cobb angle (R$^2$=0.858) suggests that our proposed method can potentially replace manual UCA measurement in ultrasound scoliosis assessment.

Published
2024

6. Assessment of malalignment at early stage in adolescent idiopathic scoliosis: a longitudinal cohort study

Author

Langlais, Tristan, Vergari, Claudio, Rougereau, Gregoire, Gaume, Mathilde, Gajny, Laurent, Abelin-Genevois, Kariman, Bernard, Jean Claude, Hu, Zongshan, Cheng, Jack Chun Yiu, Chu, Winnie Chiu Wing, Assi, Ayman, Karam, Mohamad, Ghanem, Ismat, Bassani, Tito, Galbusera, Fabio, Sconfienza, Luca Maria, Brayda-Bruno, Marco, Courtois, Isabelle, Ebermeyer, Eric, Vialle, Raphael, Dubousset, Jean, and Skalli, Wafa

Published
2024
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7. An Uncertainty Aided Framework for Learning based Liver $T_1\rho$ Mapping and Analysis

Author

Huang, Chaoxing, Wong, Vincent Wai Sun, Chan, Queenie, Chu, Winnie Chiu Wing, and Chen, Weitian

Subjects
Physics - Medical Physics, Computer Science - Computer Vision and Pattern Recognition
Abstract

Objective: Quantitative $T_1\rho$ imaging has potential for assessment of biochemical alterations of liver pathologies. Deep learning methods have been employed to accelerate quantitative $T_1\rho$ imaging. To employ artificial intelligence-based quantitative imaging methods in complicated clinical environment, it is valuable to estimate the uncertainty of the predicated $T_1\rho$ values to provide the confidence level of the quantification results. The uncertainty should also be utilized to aid the post-hoc quantitative analysis and model learning tasks. Approach: To address this need, we propose a parametric map refinement approach for learning-based $T_1\rho$ mapping and train the model in a probabilistic way to model the uncertainty. We also propose to utilize the uncertainty map to spatially weight the training of an improved $T_1\rho$ mapping network to further improve the mapping performance and to remove pixels with unreliable $T_1\rho$ values in the region of interest. The framework was tested on a dataset of 51 patients with different liver fibrosis stages. Main results: Our results indicate that the learning-based map refinement method leads to a relative mapping error of less than 3% and provides uncertainty estimation simultaneously. The estimated uncertainty reflects the actual error level, and it can be used to further reduce relative $T_1\rho$ mapping error to 2.60% as well as removing unreliable pixels in the region of interest effectively. Significance: Our studies demonstrate the proposed approach has potential to provide a learning-based quantitative MRI system for trustworthy $T_1\rho$ mapping of the liver.

Published
2023

8. Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions

Author

Luo, Luyang, Wang, Xi, Lin, Yi, Ma, Xiaoqi, Tan, Andong, Chan, Ronald, Vardhanabhuti, Varut, Chu, Winnie CW, Cheng, Kwang-Ting, and Chen, Hao

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

Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to improve the outcome of breast cancer patients. In the past decade, deep learning has shown remarkable progress in breast cancer imaging analysis, holding great promise in interpreting the rich information and complex context of breast imaging modalities. Considering the rapid improvement in deep learning technology and the increasing severity of breast cancer, it is critical to summarize past progress and identify future challenges to be addressed. This paper provides an extensive review of deep learning-based breast cancer imaging research, covering studies on mammogram, ultrasound, magnetic resonance imaging, and digital pathology images over the past decade. The major deep learning methods and applications on imaging-based screening, diagnosis, treatment response prediction, and prognosis are elaborated and discussed. Drawn from the findings of this survey, we present a comprehensive discussion of the challenges and potential avenues for future research in deep learning-based breast cancer imaging., Comment: IEEE RBME 2024

Published
2023

10. Uncertainty-weighted Multi-tasking for $T_{1\rho}$ and T$_2$ Mapping in the Liver with Self-supervised Learning

Author

Huang, Chaoxing, Qian, Yurui, Hou, Jian, Jiang, Baiyan, Chan, Queenie, Wong, Vincent WS, Chu, Winnie CW, and Chen, Weitian

Subjects
Physics - Medical Physics, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Multi-parametric mapping of MRI relaxations in liver has the potential of revealing pathological information of the liver. A self-supervised learning based multi-parametric mapping method is proposed to map T$T_{1\rho}$ and T$_2$ simultaneously, by utilising the relaxation constraint in the learning process. Data noise of different mapping tasks is utilised to make the model uncertainty-aware, which adaptively weight different mapping tasks during learning. The method was examined on a dataset of 51 patients with non-alcoholic fatter liver disease. Results showed that the proposed method can produce comparable parametric maps to the traditional multi-contrast pixel wise fitting method, with a reduced number of images and less computation time. The uncertainty weighting also improves the model performance. It has the potential of accelerating MRI quantitative imaging.

Published
2023

11. Uncertainty-Aware Self-supervised Neural Network for Liver $T_{1\rho}$ Mapping with Relaxation Constraint

Author

Huang, Chaoxing, Qian, Yurui, Yu, Simon Chun Ho, Hou, Jian, Jiang, Baiyan, Chan, Queenie, Wong, Vincent Wai-Sun, Chu, Winnie Chiu-Wing, and Chen, Weitian

Subjects
Quantitative Biology - Tissues and Organs, Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing, Physics - Medical Physics
Abstract

$T_{1\rho}$ mapping is a promising quantitative MRI technique for the non-invasive assessment of tissue properties. Learning-based approaches can map $T_{1\rho}$ from a reduced number of $T_{1\rho}$ weighted images, but requires significant amounts of high quality training data. Moreover, existing methods do not provide the confidence level of the $T_{1\rho}$ estimation. To address these problems, we proposed a self-supervised learning neural network that learns a $T_{1\rho}$ mapping using the relaxation constraint in the learning process. Epistemic uncertainty and aleatoric uncertainty are modelled for the $T_{1\rho}$ quantification network to provide a Bayesian confidence estimation of the $T_{1\rho}$ mapping. The uncertainty estimation can also regularize the model to prevent it from learning imperfect data. We conducted experiments on $T_{1\rho}$ data collected from 52 patients with non-alcoholic fatty liver disease. The results showed that our method outperformed the existing methods for $T_{1\rho}$ quantification of the liver using as few as two $T_{1\rho}$-weighted images. Our uncertainty estimation provided a feasible way of modelling the confidence of the self-supervised learning based $T_{1\rho}$ estimation, which is consistent with the reality in liver $T_{1\rho}$ imaging., Comment: Provisionally accepted by Physics in Medicine and Biology

Published
2022
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17. Frequency drift in MR spectroscopy at 3T.

Author

Hui, Steve CN, Mikkelsen, Mark, Zöllner, Helge J, Ahluwalia, Vishwadeep, Alcauter, Sarael, Baltusis, Laima, Barany, Deborah A, Barlow, Laura R, Becker, Robert, Berman, Jeffrey I, Berrington, Adam, Bhattacharyya, Pallab K, Blicher, Jakob Udby, Bogner, Wolfgang, Brown, Mark S, Calhoun, Vince D, Castillo, Ryan, Cecil, Kim M, Choi, Yeo Bi, Chu, Winnie CW, Clarke, William T, Craven, Alexander R, Cuypers, Koen, Dacko, Michael, de la Fuente-Sandoval, Camilo, Desmond, Patricia, Domagalik, Aleksandra, Dumont, Julien, Duncan, Niall W, Dydak, Ulrike, Dyke, Katherine, Edmondson, David A, Ende, Gabriele, Ersland, Lars, Evans, C John, Fermin, Alan SR, Ferretti, Antonio, Fillmer, Ariane, Gong, Tao, Greenhouse, Ian, Grist, James T, Gu, Meng, Harris, Ashley D, Hat, Katarzyna, Heba, Stefanie, Heckova, Eva, Hegarty, John P, Heise, Kirstin-Friederike, Honda, Shiori, Jacobson, Aaron, Jansen, Jacobus FA, Jenkins, Christopher W, Johnston, Stephen J, Juchem, Christoph, Kangarlu, Alayar, Kerr, Adam B, Landheer, Karl, Lange, Thomas, Lee, Phil, Levendovszky, Swati Rane, Limperopoulos, Catherine, Liu, Feng, Lloyd, William, Lythgoe, David J, Machizawa, Maro G, MacMillan, Erin L, Maddock, Richard J, Manzhurtsev, Andrei V, Martinez-Gudino, María L, Miller, Jack J, Mirzakhanian, Heline, Moreno-Ortega, Marta, Mullins, Paul G, Nakajima, Shinichiro, Near, Jamie, Noeske, Ralph, Nordhøy, Wibeke, Oeltzschner, Georg, Osorio-Duran, Raul, Otaduy, Maria CG, Pasaye, Erick H, Peeters, Ronald, Peltier, Scott J, Pilatus, Ulrich, Polomac, Nenad, Porges, Eric C, Pradhan, Subechhya, Prisciandaro, James Joseph, Puts, Nicolaas A, Rae, Caroline D, Reyes-Madrigal, Francisco, Roberts, Timothy PL, Robertson, Caroline E, Rosenberg, Jens T, Rotaru, Diana-Georgiana, O'Gorman Tuura, Ruth L, Saleh, Muhammad G, Sandberg, Kristian, Sangill, Ryan, and Schembri, Keith

Subjects
3T, Frequency drift, Magnetic resonance spectroscopy, Multi-site, Multi-vendor, Press, Brain, Data Analysis, Databases, Factual, Humans, Magnetic Resonance Imaging, Magnetic Resonance Spectroscopy, Clinical Research, Biomedical Imaging, Neurology & Neurosurgery, Medical and Health Sciences, Psychology and Cognitive Sciences
Abstract

PurposeHeating of gradient coils and passive shim components is a common cause of instability in the B0 field, especially when gradient intensive sequences are used. The aim of the study was to set a benchmark for typical drift encountered during MR spectroscopy (MRS) to assess the need for real-time field-frequency locking on MRI scanners by comparing field drift data from a large number of sites.MethodA standardized protocol was developed for 80 participating sites using 99 3T MR scanners from 3 major vendors. Phantom water signals were acquired before and after an EPI sequence. The protocol consisted of: minimal preparatory imaging; a short pre-fMRI PRESS; a ten-minute fMRI acquisition; and a long post-fMRI PRESS acquisition. Both pre- and post-fMRI PRESS were non-water suppressed. Real-time frequency stabilization/adjustment was switched off when appropriate. Sixty scanners repeated the protocol for a second dataset. In addition, a three-hour post-fMRI MRS acquisition was performed at one site to observe change of gradient temperature and drift rate. Spectral analysis was performed using MATLAB. Frequency drift in pre-fMRI PRESS data were compared with the first 5:20 minutes and the full 30:00 minutes of data after fMRI. Median (interquartile range) drifts were measured and showed in violin plot. Paired t-tests were performed to compare frequency drift pre- and post-fMRI. A simulated in vivo spectrum was generated using FID-A to visualize the effect of the observed frequency drifts. The simulated spectrum was convolved with the frequency trace for the most extreme cases. Impacts of frequency drifts on NAA and GABA were also simulated as a function of linear drift. Data from the repeated protocol were compared with the corresponding first dataset using Pearson's and intraclass correlation coefficients (ICC).ResultsOf the data collected from 99 scanners, 4 were excluded due to various reasons. Thus, data from 95 scanners were ultimately analyzed. For the first 5:20 min (64 transients), median (interquartile range) drift was 0.44 (1.29) Hz before fMRI and 0.83 (1.29) Hz after. This increased to 3.15 (4.02) Hz for the full 30 min (360 transients) run. Average drift rates were 0.29 Hz/min before fMRI and 0.43 Hz/min after. Paired t-tests indicated that drift increased after fMRI, as expected (p < 0.05). Simulated spectra convolved with the frequency drift showed that the intensity of the NAA singlet was reduced by up to 26%, 44 % and 18% for GE, Philips and Siemens scanners after fMRI, respectively. ICCs indicated good agreement between datasets acquired on separate days. The single site long acquisition showed drift rate was reduced to 0.03 Hz/min approximately three hours after fMRI.DiscussionThis study analyzed frequency drift data from 95 3T MRI scanners. Median levels of drift were relatively low (5-min average under 1 Hz), but the most extreme cases suffered from higher levels of drift. The extent of drift varied across scanners which both linear and nonlinear drifts were observed.

Published
2021

19. Association between cognitive impairment and functional limitations in everyday life in patients with haemophilia in Hong Kong

Author

Cheung, Yin Ting, primary, Ma, Chung Tin, additional, Weishang, Deng, additional, Lam, Henry Hon Wai, additional, Ling, Siu Cheung, additional, Kwok, Kevin, additional, Li, Chak Ho, additional, Ha, Chung Yin, additional, Yip, Sze Fai, additional, Siu Ming Wong, Raymond, additional, Chu, Winnie Chiu Wing, additional, and Li, Chi Kong, additional

Published
2024
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21. Clinical Characteristics and Kidney Outcomes in Chinese Patients with Autosomal Dominant Polycystic Kidney Disease

Author

Fung, Winston Wing-Shing, primary, Szeto, Cheuk-Chun, additional, Chow, Kai-Ming, additional, Cheng, Phyllis Mei-Shan, additional, Kwong, Vickie Wai-Ki, additional, Lau, Sam Lik-Fung, additional, Pang, Wing-Fai, additional, Chu, Winnie Chiu-Wing, additional, Ong, Albert Chee Meng, additional, Devuyst, Olivier, additional, and Li, Philip Kam-Tao, additional

Published
2024
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25. Subglacial lakes and their changing role in a warming climate

Author

Livingstone, Stephen J., Li, Yan, Rutishauser, Anja, Sanderson, Rebecca J., Winter, Kate, Mikucki, Jill A., Björnsson, Helgi, Bowling, Jade S., Chu, Winnie, Dow, Christine F., Fricker, Helen A., McMillan, Malcolm, Ng, Felix S. L., Ross, Neil, Siegert, Martin J., Siegfried, Matthew, and Sole, Andrew J.

Published
2022
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29. Clinical Associations and Prognostic Value of MRI-Visible Perivascular Spaces in Patients With Ischemic Stroke or TIA:A Pooled Analysis

Author

Best, Jonathan G., Ambler, Gareth, Wilson, Duncan, Du, Houwei, Lee, Keon Joo, Lim, Jae Sung, Teo, Kay Cheong, Mak, Henry, Kim, Young Dae, Song, Tae Jin, Selcuk Demirelli, Derya, Nishihara, Masashi, Yoshikawa, Masaaki, Kubacka, Marta, Zietz, Annaelle, Al-Shahi Salman, Rustam, Jäger, Hans Rolf, Lip, Gregory Y.H., Panos, Leonidas, Goeldlin, Martina B., Slater, Lee Anne, Karayiannis, Christopher Charles, Phan, Thanh G., Bellut, Maximilian, Abrigo, Jill, Cheng, Cyrus, Leung, Thomas W., Chu, Winnie, Chappell, Francesca, Makin, Stephen D.J., Van Dam-Nolen, Dianne H.K., Kooi, M. Eline, Köhler, Sebastian, Staals, Julie, Kuchcinski, Grégory, Bordet, Régis, Dubost, Florian, Wardlaw, Joanna M., Soo, Yannie O.Y., Fluri, Felix, Srikanth, Velandai K., Jung, Simon, Peters, Nils, Hara, Hideo, Yakushiji, Yusuke, Necioglu Orken, Dilek, Heo, Ji Hoe, Lau, Gary Kui Kai, Bae, Hee Joon, Werring, David J., Best, Jonathan G., Ambler, Gareth, Wilson, Duncan, Du, Houwei, Lee, Keon Joo, Lim, Jae Sung, Teo, Kay Cheong, Mak, Henry, Kim, Young Dae, Song, Tae Jin, Selcuk Demirelli, Derya, Nishihara, Masashi, Yoshikawa, Masaaki, Kubacka, Marta, Zietz, Annaelle, Al-Shahi Salman, Rustam, Jäger, Hans Rolf, Lip, Gregory Y.H., Panos, Leonidas, Goeldlin, Martina B., Slater, Lee Anne, Karayiannis, Christopher Charles, Phan, Thanh G., Bellut, Maximilian, Abrigo, Jill, Cheng, Cyrus, Leung, Thomas W., Chu, Winnie, Chappell, Francesca, Makin, Stephen D.J., Van Dam-Nolen, Dianne H.K., Kooi, M. Eline, Köhler, Sebastian, Staals, Julie, Kuchcinski, Grégory, Bordet, Régis, Dubost, Florian, Wardlaw, Joanna M., Soo, Yannie O.Y., Fluri, Felix, Srikanth, Velandai K., Jung, Simon, Peters, Nils, Hara, Hideo, Yakushiji, Yusuke, Necioglu Orken, Dilek, Heo, Ji Hoe, Lau, Gary Kui Kai, Bae, Hee Joon, and Werring, David J.

Abstract

Background and Objectives: Visible perivascular spaces are an MRI marker of cerebral small vessel disease and might predict future stroke. However, results from existing studies vary. We aimed to clarify this through a large collaborative multicenter analysis. Methods: We pooled individual patient data from a consortium of prospective cohort studies. Participants had recent ischemic stroke or transient ischemic attack (TIA), underwent baseline MRI, and were followed up for ischemic stroke and symptomatic intracranial hemorrhage (ICH). Perivascular spaces in the basal ganglia (BGPVS) and perivascular spaces in the centrum semiovale (CSOPVS) were rated locally using a validated visual scale. We investigated clinical and radiologic associations cross-sectionally using multinomial logistic regression and prospective associations with ischemic stroke and ICH using Cox regression. Results: We included 7,778 participants (mean age 70.6 years; 42.7% female) from 16 studies, followed up for a median of 1.44 years. Eighty ICH and 424 ischemic strokes occurred. BGPVS were associated with increasing age, hypertension, previous ischemic stroke, previous ICH, lacunes, cerebral microbleeds, and white matter hyperintensities. CSOPVS showed consistently weaker associations. Prospectively, after adjusting for potential confounders including cerebral microbleeds, increasing BGPVS burden was independently associated with future ischemic stroke (versus 0-10 BGPVS, 11-20 BGPVS: HR 1.19, 95% CI 0.93-1.53; 21+ BGPVS: HR 1.50, 95% CI 1.10-2.06; p = 0.040). Higher BGPVS burden was associated with increased ICH risk in univariable analysis, but not in adjusted analyses. CSOPVS were not significantly associated with either outcome. Discussion: In patients with ischemic stroke or TIA, increasing BGPVS burden is associated with more severe cerebral small vessel disease and higher ischemic stroke risk. Neither BGPVS nor CSOPVS were independently associated with future ICH.

Published
2024

30. Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions

Author

Luo, Luyang, Wang, Xi, Lin, Yi, Ma, Xiaoqi, Tan, Andong, Chan, Ronald, Vardhanabhuti, Varut, Chu, Winnie CW, Cheng, Kwang Ting, Chen, Hao, Luo, Luyang, Wang, Xi, Lin, Yi, Ma, Xiaoqi, Tan, Andong, Chan, Ronald, Vardhanabhuti, Varut, Chu, Winnie CW, Cheng, Kwang Ting, and Chen, Hao

Abstract

Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to improve the outcome of breast cancer patients. In the past decade, deep learning has shown remarkable progress in breast cancer imaging analysis, holding great promise in interpreting the rich information and complex context of breast imaging modalities. Considering the rapid improvement in deep learning technology and the increasing severity of breast cancer, it is critical to summarize past progress and identify future challenges to be addressed. This paper provides an extensive review of deep learning-based breast cancer imaging research, covering studies on mammograms, ultrasound, magnetic resonance imaging, and digital pathology images over the past decade. The major deep learning methods and applications on imaging-based screening, diagnosis, treatment response prediction, and prognosis are elaborated and discussed. Drawn from the findings of this survey, we present a comprehensive discussion of the challenges and potential avenues for future research in deep learning-based breast cancer imaging.

Published
2024

32. Investigation of lacrimal gland and extraocular muscle in thyroid eye disease patients with severe subjective dry eye disease: a multiparametric magnetic resonance imaging study.

Author

Liao, Xulin, Aljufairi, Fatema Mohamed Ali Abdulla, Sebastian, Jake Uy, Jia, Ruofan, Wong, Hanson Yiu Man, Lai, Kenneth Ka Hei, Chen, Wanxue, Hu, Zhichao, Wei, Yingying, Chu, Winnie Chiu Wing, Tham, Clement Chee Yung, Pang, Chi Pui, and Chong, Kelvin Kam Lung

Subjects
EYE diseases, THYROID eye disease, DRY eye syndromes, LACRIMAL apparatus, MAGNETIC resonance imaging, EYE muscles, RADIOLOGIC technology, ORTHOPEDIC shoes
Abstract

Purpose: To analyze the radiological features of the lacrimal gland (LG) and extraocular muscle (EOM) in thyroid eye disease (TED) patients with severe subjective dry eye disease (DED) using magnetic resonance imaging (MRI) measurements. Methods: In this cross-sectional study, mechanical ocular exposure, dry eye assessment and MRI data were collected. Patients were classified into non-severe subjective DED group with ocular surface disease index (OSDI) < 33 and severe subjective DED group with OSDI ≥ 33. Linear regression model was applied for comparing the OSDI < 33 and OSDI ≥ 33 group in TED patients. The predictive performance of MRI parameters and models was assessed by receiver operating characteristic curve (ROC) analysis. Results: Consecutive 88 TED patients (176 eyes) were included in this study. In the OSDI < 33 group, 52 TED patients (104 eyes) with a mean clinical activity score (CAS) of 0.63 ± 0.75. In the OSDI ≥ 33 group, there are 36 TED patients (72 eyes), with a mean CAS of 1.50 ± 1.54. The age and sex of the patients were matched between the two groups. The OSDI ≥ 33 group had shorter tear break-up time, larger levator palpebrae superioris / superior rectus (LPS/SR), inferior rectus and lateral rectus, smaller LG, more inflammatory LPS/SR and inferior rectus than OSDI < 33 DED group (P < 0.05). In the linear regression analysis, compare to the OSDI < 33 DED group, the OSDI ≥ 33 group had larger medial rectus cross-sectional area (β = 0.06, 95%CI: (0.02, 0.10), P = 0.008), larger inferior rectus cross-sectional area (β = 0.06, 95%CI: (0.00, 0.12), P = 0.048), smaller LG cross-sectional area (β = -0.14, 95%CI: (-0.25, -0.04), P = 0.008). In the ROC analysis, the area under curve of medial rectus, inferior rectus, LG, and combined model are 0.625, 0.640, 0.661 and 0.716, respectively. Conclusion: Multiparametric MRI parameters of the LG and EOM in TED patients with severe subjective DED were significantly altered. Novel models combining the cross-sectional area of LG, medial rectus and inferior rectus showed good predictive performance in TED patients with severe subjective DED. [ABSTRACT FROM AUTHOR]

Published
2024
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33. Corporate Social Responsibility and Social Needs in Health Care Sectors—A Critical Analysis of Social Innovation in the Health Sector in Taiwan.

Author

Chu, Winnie and Chu, Nain-Feng

Subjects
CORPORATE culture, TAIWANESE people, HEALTH services accessibility, DIFFUSION of innovations, RURAL health, INTERPROFESSIONAL relations, HEALTH policy, SOCIAL responsibility, PRIVATE sector, PUBLIC relations, CORPORATIONS, HEALTH care industry, MEDICAL needs assessment, ORGANIZATIONAL goals, URBAN health, MEDICAL care costs
Abstract

Background: Social innovation is often used as a mechanism to jump-start public–private partnerships to leverage resources to achieve social impact; the analysis of sustainability and the impact of corporate social responsibility (CSR) cannot be emphasized enough. Due to advances in the information and communication technology industry in Taiwan, this paper aims to explore whether these advancements drive CSR as a form of social innovation to meet health needs in Taiwan. Methodology: This paper uses a case study to look at CSR programs in the health sector in Taiwan. Corporations with diverse missions and different CSR approaches that are available on the internet are selected. The analysis of the case study takes a qualitative, exploratory approach to shed light on current initiatives. Results: The majority of CSR programs in Taiwan are private sector activities that emerged during the COVID-19 pandemic; current CSR activities in Taiwan are driven by awards, public relations, and external interests. Corporations in Taiwan have the potential to address the health care gaps of urban–rural health utilization among Taiwanese indigenous communities. It is recommended for corporations to (1) develop partnerships with public health experts or to (2) employ CSR personnel with health care backgrounds who can navigate the intersection between health, business, and policies to develop CSR strategies. Conclusions: Further evaluation of the projects mentioned in this paper to assess the direct and indirect impact on health outcomes could provide a more comprehensive understanding of the field of CSR in the health sector in Taiwan. [ABSTRACT FROM AUTHOR]

Published
2024
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34. Patterns and Implications of Intracranial Atherosclerosis in Anterior and Posterior Circulation Identified by High-Resolution Vessel Wall Imaging.

Author

Zheng, Lu, Li, Jia, Yang, Wenjie, Lam, Ho-Ching Chloe, Wong, K.S. Lawrence, Chu, Winnie, Leung, Thomas Wai Hong, and Chen, Xiangyan

Subjects
TRANSIENT ischemic attack, MAGNETIC resonance angiography, STROKE patients, DISEASE risk factors, BASILAR artery
Abstract

Introduction: Anterior and posterior circulation atheroscleroses differ in vascular risk factors and stroke patterns. Posterior circulation stroke has worse clinical outcomes. However, few studies described the differentiation of plaque features between anterior and posterior circulation atheroscleroses via high-resolution vessel wall imaging (HR-VWI). We aimed to compare the plaque imaging features between anterior and posterior circulations to highlight the relevance of plaque imaging features to clinical events of ischemic stroke. Methods: Prospective data from a HR-VWI cohort of adult patients with acute ischemic stroke or transient ischemic attack were retrospectively analyzed. Quantitative and qualitative measurements of atherosclerotic plaques along the middle cerebral arteries (MCAs), the basilar artery (BA), and the vertebral arteries (VAs) were conducted on HR-VWI. Vessels with stenotic degrees over 30% were identified on the matched time-of-flight magnetic resonance angiography (TOF-MRA) and visually classified into normal, irregular, stenotic, and occluded. The sensitivity, specificity, positive and negative predictive values for TOF-MRA in detecting abnormal vessels were calculated by using quantification on the basis of HR-VWI findings as the reference standard. Results: One hundred and one patients (median age, 64 years old; 62.4% males) were included in this study. A total of 292 plaques were identified, with 152 in the MCAs, 35 in the BA, and 105 in the VAs. The VAs possessed significantly higher enhancement index (EI) (median 38.37 vs. 18.40, p <0.001), more plaques with positive remodeling (76.2% vs. 57.2%, p = 0.002) and intraplaque hypo-intensity (43.8% vs. 12.5%, p <0.001) than the MCAs. The MCAs presented with more intraplaque hemorrhage (IPH) (20.4% vs. 8.6%, p = 0.014) than the VAs. The sensitivity and specificity of TOF-MRA for evaluating luminal stenosis were 89.0 (82.5–93.4) and 66.7 (24.1–94.0) in anterior circulation, respectively, and were 75.2 (66.7–82.2) and 27.3 (7.3–60.7) in posterior circulation, respectively. Conclusion: Our findings might elucidate the clinical events and outcomes in anterior and posterior circulation stroke. Posterior circulation atherosclerosis had higher EI and more plaques with hypo-intensity, suggesting a heavier atherosclerosis burden. Positive remodeling pattern in posterior circulation atherosclerosis might create an impression of "wider" vascular lumen, leading to possible underestimation of atherosclerosis burden of posterior circulation on TOF-MRA as compared to HR-VWI. Besides, anterior circulation atherosclerosis with IPH might be associated with plaque rupture and artery-to-artery embolism. Future studies are needed to verify these findings. [ABSTRACT FROM AUTHOR]

Published
2024
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35. A newly digitized ice-penetrating radar data set acquired over the Greenland ice sheet in 1971–1979.

Author

Karlsson, Nanna B., Schroeder, Dustin M., Sørensen, Louise Sandberg, Chu, Winnie, Dall, Jørgen, Andersen, Natalia H., Dobson, Reese, Mackie, Emma J., Köhn, Simon J., Steinmetz, Jillian E., Tarzona, Angelo S., Teisberg, Thomas O., and Skou, Niels

Subjects
GREENLAND ice, DATA libraries, ICE sheets, OPTICAL films, TECHNICAL reports, DIGITIZATION
Abstract

We present an ice-penetrating radar data set acquired over the Greenland ice sheet by aircraft during the years 1971, 1972, 1974, 1978, and 1979. The data set comprises over 177 000 km of flight lines and contains a wealth of information on the state of the Greenland ice sheet, including information on ice thickness and englacial properties. During data collection in the 1970s, the data were recorded on optical film rolls, and in this paper, we document the digitization of these film rolls and their associated geographical information. Our data digitization enables interaction with and analysis of the data and facilitates comparison with modern-day radar observations. The complete data set in full resolution is available in the Stanford Digital Repository (10.25740/wm135gp2721;), with the associated technical reports. Part of the data set is available as low-resolution JPG files at the Technical University of Denmark's data repository (10.6084/m9.figshare.c.7235299.v1;), with associated technical reports and digitized geographical information. The Stanford Digital Repository serves as long-term storage, providing archival historic preservation in perpetuity, and is not intended as a primary data access point. The Technical University of Denmark (DTU) data repository serves as a primary entry point for data access, with files organized according to acquisition year and flight line in a simple folder structure. Here, we release the full data sets to enable the larger community to access and interact with the data. [ABSTRACT FROM AUTHOR]

Published
2024
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36. Effect of curve location on the severity index for adolescent idiopathic scoliosis: a longitudinal cohort study

Author

Vergari, Claudio, Skalli, Wafa, Abelin-Genevois, Kariman, Bernard, Jean Claude, Hu, Zongshan, Cheng, Jack Chun Yiu, Chu, Winnie Chiu Wing, Assi, Ayman, Karam, Mohammad, Ghanem, Ismat, Bassani, Tito, Galbusera, Fabio, Sconfienza, Luca Maria, Brayda-Bruno, Marco, Courtois, Isabelle, Ebermeyer, Eric, Vialle, Raphael, Langlais, Tristan, and Dubousset, Jean

Published
2021
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39. Frequency drift in MR spectroscopy at 3T

Author

Hui, Steve C.N., Mikkelsen, Mark, Zöllner, Helge J., Ahluwalia, Vishwadeep, Alcauter, Sarael, Baltusis, Laima, Barany, Deborah A., Barlow, Laura R., Becker, Robert, Berman, Jeffrey I., Berrington, Adam, Bhattacharyya, Pallab K., Blicher, Jakob Udby, Bogner, Wolfgang, Brown, Mark S., Calhoun, Vince D., Castillo, Ryan, Cecil, Kim M., Choi, Yeo Bi, Chu, Winnie C.W., Clarke, William T., Craven, Alexander R., Cuypers, Koen, Dacko, Michael, de la Fuente-Sandoval, Camilo, Desmond, Patricia, Domagalik, Aleksandra, Dumont, Julien, Duncan, Niall W., Dydak, Ulrike, Dyke, Katherine, Edmondson, David A., Ende, Gabriele, Ersland, Lars, Evans, C. John, Fermin, Alan S.R., Ferretti, Antonio, Fillmer, Ariane, Gong, Tao, Greenhouse, Ian, Grist, James T., Gu, Meng, Harris, Ashley D., Hat, Katarzyna, Heba, Stefanie, Heckova, Eva, Hegarty, John P., II, Heise, Kirstin-Friederike, Honda, Shiori, Jacobson, Aaron, Jansen, Jacobus F.A., Jenkins, Christopher W., Johnston, Stephen J., Juchem, Christoph, Kangarlu, Alayar, Kerr, Adam B., Landheer, Karl, Lange, Thomas, Lee, Phil, Levendovszky, Swati Rane, Limperopoulos, Catherine, Liu, Feng, Lloyd, William, Lythgoe, David J., Machizawa, Maro G., MacMillan, Erin L., Maddock, Richard J., Manzhurtsev, Andrei V., Martinez-Gudino, María L., Miller, Jack J., Mirzakhanian, Heline, Moreno-Ortega, Marta, Mullins, Paul G., Nakajima, Shinichiro, Near, Jamie, Noeske, Ralph, Nordhøy, Wibeke, Oeltzschner, Georg, Osorio-Duran, Raul, Otaduy, Maria C.G., Pasaye, Erick H., Peeters, Ronald, Peltier, Scott J., Pilatus, Ulrich, Polomac, Nenad, Porges, Eric C., Pradhan, Subechhya, Prisciandaro, James Joseph, Puts, Nicolaas A, Rae, Caroline D., Reyes-Madrigal, Francisco, Roberts, Timothy P.L., Robertson, Caroline E., Rosenberg, Jens T., Rotaru, Diana-Georgiana, O'Gorman Tuura, Ruth L, Saleh, Muhammad G., Sandberg, Kristian, Sangill, Ryan, Schembri, Keith, Schrantee, Anouk, Semenova, Natalia A., Singel, Debra, Sitnikov, Rouslan, Smith, Jolinda, Song, Yulu, Stark, Craig, Stoffers, Diederick, Swinnen, Stephan P., Tain, Rongwen, Tanase, Costin, Tapper, Sofie, Tegenthoff, Martin, Thiel, Thomas, Thioux, Marc, Truong, Peter, van Dijk, Pim, Vella, Nolan, Vidyasagar, Rishma, Vovk, Andrej, Wang, Guangbin, Westlye, Lars T., Wilbur, Timothy K., Willoughby, William R., Wilson, Martin, Wittsack, Hans-Jörg, Woods, Adam J., Wu, Yen-Chien, Xu, Junqian, Lopez, Maria Yanez, Yeung, David K.W., Zhao, Qun, Zhou, Xiaopeng, Zupan, Gasper, and Edden, Richard A.E.

Published
2021
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41. Clinical Associations and Prognostic Value of MRI-Visible Perivascular Spaces in Patients With Ischemic Stroke or TIA

Author

Best, Jonathan G., primary, Ambler, Gareth, additional, Wilson, Duncan, additional, Du, Houwei, additional, Lee, Keon-Joo, additional, Lim, Jae-Sung, additional, Teo, Kay Cheong, additional, Mak, Henry, additional, Kim, Young Dae, additional, Song, Tae-Jin, additional, Selcuk Demirelli, Derya, additional, Nishihara, Masashi, additional, Yoshikawa, Masaaki, additional, Kubacka, Marta, additional, Zietz, Annaelle, additional, Al-Shahi Salman, Rustam, additional, Jäger, Hans Rolf, additional, Lip, Gregory Y.H., additional, Panos, Leonidas, additional, Goeldlin, Martina B., additional, Slater, Lee-Anne, additional, Karayiannis, Christopher Charles, additional, Phan, Thanh G., additional, Bellut, Maximilian, additional, Abrigo, Jill, additional, Cheng, Cyrus, additional, Leung, Thomas W., additional, Chu, Winnie, additional, Chappell, Francesca, additional, Makin, Stephen D.J., additional, van Dam-Nolen, Dianne H.K., additional, Kooi, M. Eline, additional, Köhler, Sebastian, additional, Staals, Julie, additional, Kuchcinski, Grégory, additional, Bordet, Régis, additional, Dubost, Florian, additional, Wardlaw, Joanna M., additional, Soo, Yannie O.Y., additional, Fluri, Felix, additional, Srikanth, Velandai K., additional, Jung, Simon, additional, Peters, Nils, additional, Hara, Hideo, additional, Yakushiji, Yusuke, additional, Necioglu Orken, Dilek, additional, Heo, Ji-Hoe, additional, Lau, Gary Kui Kai, additional, Bae, Hee-Joon, additional, and Werring, David J., additional

Published
2024
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47. Development of imaging-based risk scores for prediction of intracranial haemorrhage and ischaemic stroke in patients taking antithrombotic therapy after ischaemic stroke or transient ischaemic attack: a pooled analysis of individual patient data from cohort studies

Author

Harkness, Kirsty, Shaw, Louise, Sword, Jane, Mohd Nor, Azlisham, Sharma, Pankaj, Kelly, Deborah, Harrington, Frances, Randall, Marc, Smith, Matthew, Mahawish, Karim, Elmarim, Abduelbaset, Esisi, Bernard, Cullen, Claire, Nallasivam, Arumug, Price, Christopher, Barry, Adrian, Roffe, Christine, Coyle, John, Hassan, Ahamad, Birns, Jonathan, Cohen, David, Sekaran, Lakshmanan, Parry-Jones, Adrian, Parry, Anthea, Hargroves, David, Proschel, Harald, Datta, Prabel, Darawil, Khaled, Manoj, Aravindakshan, Burn, Mathew, Patterson, Chris, Giallombardo, Elio, Smyth, Nigel, Mansoor, Syed, Anwar, Ijaz, Marsh, Rachel, Ispoglou, Sissi, Chadha, Dinesh, Prabhakaran, Mathuri, Meenakishundaram, Sanjeevikumar, O'Connell, Janice, Scott, Jon, Krishnamurthy, Vinodh, Aghoram, Prasanna, McCormick, Michael, Sprigg, Nikola, O'Mahony, Paul, Cooper, Martin, Choy, Lillian, Wilkinson, Peter, Leach, Simon, Caine, Sarah, Burger, Ilse, Gunathilagan, Gunaratam, Guyler, Paul, Emsley, Hedley, Davis, Michelle, Manawadu, Dulka, Pasco, Kath, Mamun, Maam, Luder, Robert, Sajid, Mahmud, Okwera, James, Warburton, Elizabeth, Saastamoinen, Kari, England, Timothy, Putterill, Janet, Flossman, Enrico, Power, Michael, Dani, Krishna, Mangion, David, Suman, Appu, Corrigan, John, Lawrence, Enas, Vahidassr, Djamil, Shakeshaft, Clare, Brown, Martin, Charidimou, Andreas, Cohen, Hannah, Banerjee, Gargi, Houlden, Henry, White, Mark, Yousry, Tarek, Flossmann, Enrico, Muir, Keith, Gratz, Pascal, Mattle, Heinrich, Panos, Leonidas, Korczyn, Amos, Kliper, Efrat, Maeder, Philippe, Gass, Achim, Pachai, Chahin, Bracoub, Luc, Douste-Blazy, Marie-Yvonne, Fratacci, Marie Dominique, Vicaut, Eric, Sato, Shoichiro, Miwa, Kaori, Fujita, Kyohei, Ide, Toshihiro, Ma, Henry, Ly, John, Singhal, Shaloo, Chandra, Ronil, Slater, Lee-Anne, Soufan, Cathy, Moran, Christopher, Traenka, Christopher, Thilemann, Sebastian, Fladt, Joachim, Gensicke, Henrik, Bonati, Leo, Kim, Beom Joon, Han, Moon-Ku, Kang, Jihoon, Ko, Eunbin, Yang, Mi Hwa, Jang, Myung Suk, Murphy, Sean, Carty, Fiona, Akijian, Layan, Thornton, John, Schembri, Mark, Douven, Elles, Delgado-Mederos, Raquel, Marín, Rebeca, Camps-Renom, Pol, Guisado-Alonso, Daniel, Nuñez, Fidel, Medrano-Martorell, Santiago, Merino, Elisa, Iida, Kotaro, Ikeda, Syuhei, Irie, Hiroyuki, Demirelli, Derya Selcuk, Medanta, Jayesh Modi, Zerna, Charlotte, Hernández, Maria Valdés, Armitage, Paul, Heye, Anna, Muñoz-Maniega, Susana, Sakka, Eleni, Thrippleton, Michael, Dennis, Martin, Beigneux, Ysoline, Silva, Mauro, Venketasubramanian, Narayanaswamy, Ho, Shu Leung, Cheung, Raymond Tak Fai, Chan, Koon Ho, Teo, Kay Cheong, Hui, Edward, Kwan, Joseph Shiu Kwong, Chang, Richard, Tse, Man Yu, Hoi, Chu Peng, Chan, Chung Yan, Chan, Oi Ling, Cheung, Ryan Hoi Kit, Wong, Edmund Ka Ming, Leung, Kam Tat, Tsang, Suk Fung, Ip, Hing Lung, Ma, Sze Ho, Ma, Karen, Fong, Wing Chi, Li, Siu Hung, Li, Richard, Ng, Ping Wing, Wong, Kwok Kui, Liu, Wenyan, Wong, Lawrence, Ramos, Lino, De Schryver, Els, Jöbsis, Joost, van der Sande, Jaap, Brouwers, Paul, Roos, Yvo, Stam, Jan, Bakker, Stef, Verbiest, Henk, Schoonewille, Wouter, Linn, Cisca, Hertzberger, Leopold, van Gemert, Maarten, Berntsen, Paul, Van Dam-Nolen, Dianne, Kooi, M Eline, Van der Lugt, Aad, Koudstaal, Peter, Leff, Alexander, Ward, Nicholas, Nachev, Parashkev, Perry, Richard, Ozkan, Hatice, Mitchell, John, Best, Jonathan G, Ambler, Gareth, Wilson, Duncan, Lee, Keon-Joo, Lim, Jae-Sung, Shiozawa, Masayuki, Koga, Masatoshi, Li, Linxin, Lovelock, Caroline, Chabriat, Hugues, Hennerici, Michael, Wong, Yuen Kwun, Mak, Henry Ka Fung, Prats-Sanchez, Luis, Martínez-Domeño, Alejandro, Inamura, Shigeru, Yoshifuji, Kazuhisa, Arsava, Ethem Murat, Horstmann, Solveig, Purrucker, Jan, Lam, Bonnie Yin Ka, Wong, Adrian, Kim, Young Dae, Song, Tae-Jin, Lemmens, Robin, Eppinger, Sebastian, Gattringer, Thomas, Uysal, Ender, Tanriverdi, Zeynep, Bornstein, Natan M, Ben Assayag, Einor, Hallevi, Hen, Molad, Jeremy, Nishihara, Masashi, Tanaka, Jun, Coutts, Shelagh B, Polymeris, Alexandros, Wagner, Benjamin, Seiffge, David J, Lyrer, Philippe, Algra, Ale, Kappelle, L Jaap, Al-Shahi Salman, Rustam, Jäger, Hans R, Lip, Gregory Y H, Fischer, Urs, El-Koussy, Marwan, Mas, Jean-Louis, Legrand, Laurence, Karayiannis, Christopher, Phan, Thanh, Gunkel, Sarah, Christ, Nicolas, Abrigo, Jill, Leung, Thomas, Chu, Winnie, Chappell, Francesca, Makin, Stephen, Hayden, Derek, Williams, David J, Mess, Werner H, Nederkoorn, Paul J, Barbato, Carmen, Browning, Simone, Wiegertjes, Kim, Tuladhar, Anil M, Maaijwee, Noortje, Guevarra, Anne Cristine, Yatawara, Chathuri, Mendyk, Anne-Marie, Delmaire, Christine, Köhler, Sebastian, van Oostenbrugge, Robert, Zhou, Ying, Xu, Chao, Hilal, Saima, Gyanwali, Bibek, Chen, Christopher, Lou, Min, Staals, Julie, Bordet, Régis, Kandiah, Nagaendran, de Leeuw, Frank-Erik, Simister, Robert, Hendrikse, Jeroen, Kelly, Peter J, Wardlaw, Joanna, Soo, Yannie, Fluri, Felix, Srikanth, Velandai, Calvet, David, Jung, Simon, Kwa, Vincent I H, Engelter, Stefan T, Peters, Nils, Smith, Eric E, Hara, Hideo, Yakushiji, Yusuke, Orken, Dilek Necioglu, Fazekas, Franz, Thijs, Vincent, Heo, Ji Hoe, Mok, Vincent, Veltkamp, Roland, Ay, Hakan, Imaizumi, Toshio, Gomez-Anson, Beatriz, Lau, Kui Kai, Jouvent, Eric, Rothwell, Peter M, Toyoda, Kazunori, Bae, Hee-Joon, Marti-Fabregas, Joan, and Werring, David J

Published
2021
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