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2,148 results on '"Neubauer, Stefan"'

1. LegoNet: Alternating Model Blocks for Medical Image Segmentation

Author

Sobirov, Ikboljon, Xie, Cheng, Siddique, Muhammad, Patel, Parijat, Chan, Kenneth, Halborg, Thomas, Kotanidis, Christos, Fatima, Zarqiash, West, Henry, Channon, Keith, Neubauer, Stefan, Antoniades, Charalambos, and Yaqub, Mohammad

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

Since the emergence of convolutional neural networks (CNNs), and later vision transformers (ViTs), the common paradigm for model development has always been using a set of identical block types with varying parameters/hyper-parameters. To leverage the benefits of different architectural designs (e.g. CNNs and ViTs), we propose to alternate structurally different types of blocks to generate a new architecture, mimicking how Lego blocks can be assembled together. Using two CNN-based and one SwinViT-based blocks, we investigate three variations to the so-called LegoNet that applies the new concept of block alternation for the segmentation task in medical imaging. We also study a new clinical problem which has not been investigated before, namely the right internal mammary artery (RIMA) and perivascular space segmentation from computed tomography angiography (CTA) which has demonstrated a prognostic value to major cardiovascular outcomes. We compare the model performance against popular CNN and ViT architectures using two large datasets (e.g. achieving 0.749 dice similarity coefficient (DSC) on the larger dataset). We evaluate the performance of the model on three external testing cohorts as well, where an expert clinician made corrections to the model segmented results (DSC>0.90 for the three cohorts). To assess our proposed model for suitability in clinical use, we perform intra- and inter-observer variability analysis. Finally, we investigate a joint self-supervised learning approach to assess its impact on model performance. The code and the pretrained model weights will be available upon acceptance., Comment: 12 pages, 5 figures, 4 tables

Published
2023

6. Fairness in Cardiac MR Image Analysis: An Investigation of Bias Due to Data Imbalance in Deep Learning Based Segmentation

Author

Puyol-Anton, Esther, Ruijsink, Bram, Piechnik, Stefan K., Neubauer, Stefan, Petersen, Steffen E., Razavi, Reza, and King, Andrew P.

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

The subject of "fairness" in artificial intelligence (AI) refers to assessing AI algorithms for potential bias based on demographic characteristics such as race and gender, and the development of algorithms to address this bias. Most applications to date have been in computer vision, although some work in healthcare has started to emerge. The use of deep learning (DL) in cardiac MR segmentation has led to impressive results in recent years, and such techniques are starting to be translated into clinical practice. However, no work has yet investigated the fairness of such models. In this work, we perform such an analysis for racial/gender groups, focusing on the problem of training data imbalance, using a nnU-Net model trained and evaluated on cine short axis cardiac MR data from the UK Biobank dataset, consisting of 5,903 subjects from 6 different racial groups. We find statistically significant differences in Dice performance between different racial groups. To reduce the racial bias, we investigated three strategies: (1) stratified batch sampling, in which batch sampling is stratified to ensure balance between racial groups; (2) fair meta-learning for segmentation, in which a DL classifier is trained to classify race and jointly optimized with the segmentation model; and (3) protected group models, in which a different segmentation model is trained for each racial group. We also compared the results to the scenario where we have a perfectly balanced database. To assess fairness we used the standard deviation (SD) and skewed error ratio (SER) of the average Dice values. Our results demonstrate that the racial bias results from the use of imbalanced training data, and that all proposed bias mitigation strategies improved fairness, with the best SD and SER resulting from the use of protected group models., Comment: MICCAI 2021 conference

Published
2021

7. Etiology and Phenotypes of Cardiomyopathy in Southern Africa: The IMHOTEP Multicenter Pilot Study

Author

Kraus, Sarah M., Cirota, Jacqui, Pandie, Shahiemah, Thomas, Kandathil, Thomas, Mookenthottathil, Makotoko, Makoali, Damasceno, Albertino, Yiga, Sarah, Greyling, Louwra, Hanekom, Hermanus A., Mateus, Angela, Novela, Celia, Laing, Nakita, September, Unita, Kerbelker, Zita, Suttle, Tessa, Chetwin, Emily, Smit, Francis E., Shaboodien, Gasnat, Chin, Ashley, Sliwa, Karen, Gumedze, Freedom, Neubauer, Stefan, Cooper, Leslie, Watkins, Hugh, and Ntusi, Ntobeko A.B.

Published
2024
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8. Estimating Uncertainty in Neural Networks for Cardiac MRI Segmentation: A Benchmark Study

Author

Ng, Matthew, Guo, Fumin, Biswas, Labonny, Petersen, Steffen E., Piechnik, Stefan K., Neubauer, Stefan, and Wright, Graham

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

Objective: Convolutional neural networks (CNNs) have demonstrated promise in automated cardiac magnetic resonance image segmentation. However, when using CNNs in a large real-world dataset, it is important to quantify segmentation uncertainty and identify segmentations which could be problematic. In this work, we performed a systematic study of Bayesian and non-Bayesian methods for estimating uncertainty in segmentation neural networks. Methods: We evaluated Bayes by Backprop, Monte Carlo Dropout, Deep Ensembles, and Stochastic Segmentation Networks in terms of segmentation accuracy, probability calibration, uncertainty on out-of-distribution images, and segmentation quality control. Results: We observed that Deep Ensembles outperformed the other methods except for images with heavy noise and blurring distortions. We showed that Bayes by Backprop is more robust to noise distortions while Stochastic Segmentation Networks are more resistant to blurring distortions. For segmentation quality control, we showed that segmentation uncertainty is correlated with segmentation accuracy for all the methods. With the incorporation of uncertainty estimates, we were able to reduce the percentage of poor segmentation to 5% by flagging 31--48% of the most uncertain segmentations for manual review, substantially lower than random review without using neural network uncertainty (reviewing 75--78% of all images). Conclusion: This work provides a comprehensive evaluation of uncertainty estimation methods and showed that Deep Ensembles outperformed other methods in most cases. Significance: Neural network uncertainty measures can help identify potentially inaccurate segmentations and alert users for manual review., Comment: Accepted to IEEE Transactions on Biomedical Engineering. Copyright (c) 2022 IEEE. Personal use of this material is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending an email to pubs-permissions@ieee.org

Published
2020
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9. Fully Automated Myocardial Strain Estimation from CMR Tagged Images using a Deep Learning Framework in the UK Biobank

Author

Ferdian, Edward, Suinesiaputra, Avan, Fung, Kenneth, Aung, Nay, Lukaschuk, Elena, Barutcu, Ahmet, Maclean, Edd, Paiva, Jose, Piechnik, Stefan K., Neubauer, Stefan, Petersen, Steffen E, and Young, Alistair A.

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

Purpose: To demonstrate the feasibility and performance of a fully automated deep learning framework to estimate myocardial strain from short-axis cardiac magnetic resonance tagged images. Methods and Materials: In this retrospective cross-sectional study, 4508 cases from the UK Biobank were split randomly into 3244 training and 812 validation cases, and 452 test cases. Ground truth myocardial landmarks were defined and tracked by manual initialization and correction of deformable image registration using previously validated software with five readers. The fully automatic framework consisted of 1) a convolutional neural network (CNN) for localization, and 2) a combination of a recurrent neural network (RNN) and a CNN to detect and track the myocardial landmarks through the image sequence for each slice. Radial and circumferential strain were then calculated from the motion of the landmarks and averaged on a slice basis. Results: Within the test set, myocardial end-systolic circumferential Green strain errors were -0.001 +/- 0.025, -0.001 +/- 0.021, and 0.004 +/- 0.035 in basal, mid, and apical slices respectively (mean +/- std. dev. of differences between predicted and manual strain). The framework reproduced significant reductions in circumferential strain in diabetics, hypertensives, and participants with previous heart attack. Typical processing time was ~260 frames (~13 slices) per second on an NVIDIA Tesla K40 with 12GB RAM, compared with 6-8 minutes per slice for the manual analysis. Conclusions: The fully automated RNNCNN framework for analysis of myocardial strain enabled unbiased strain evaluation in a high-throughput workflow, with similar ability to distinguish impairment due to diabetes, hypertension, and previous heart attack., Comment: accepted in Radiology Cardiothoracic Imaging

Published
2020
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11. Ethnic differences in the indirect effects of the COVID-19 pandemic on clinical monitoring and hospitalisations for non-COVID conditions in England: a population-based, observational cohort study using the OpenSAFELY platform

Author

Chaturvedi, Nishi, Park, Chloe, Carnemolla, Alisia, Williams, Dylan, Knueppel, Anika, Boyd, Andy, Turner, Emma L., Evans, Katharine M., Thomas, Richard, Berman, Samantha, McLachlan, Stela, Crane, Matthew, Whitehorn, Rebecca, Oakley, Jacqui, Foster, Diane, Woodward, Hannah, Campbell, Kirsteen C., Timpson, Nicholas, Kwong, Alex, Soares, Ana Goncalves, Griffith, Gareth, Toms, Renin, Jones, Louise, Annie, Herbert, Mitchell, Ruth, Palmer, Tom, Sterne, Jonathan, Walker, Venexia, Huntley, Lizzie, Fox, Laura, Denholm, Rachel, Knight, Rochelle, Northstone, Kate, Kanagaratnam, Arun, Horne, Elsie, Forbes, Harriet, North, Teri, Taylor, Kurt, Arab, Marwa A.L., Walker, Scott, Coronado, Jose I.C., Karthikeyan, Arun S., Ploubidis, George, Moltrecht, Bettina, Booth, Charlotte, Parsons, Sam, Wielgoszewska, Bozena, Bridger-Staatz, Charis, Steves, Claire, Thompson, Ellen, Garcia, Paz, Cheetham, Nathan, Bowyer, Ruth, Freydin, Maxim, Roberts, Amy, Goldacre, Ben, Walker, Alex, Morley, Jess, Hulme, William, Nab, Linda, Fisher, Louis, MacKenna, Brian, Andrews, Colm, Curtis, Helen, Hopcroft, Lisa, Green, Amelia, Patalay, Praveetha, Maddock, Jane, Patel, Kishan, Stafford, Jean, Jacques, Wels, Tilling, Kate, Macleod, John, McElroy, Eoin, Shah, Anoop, Silverwood, Richard, Denaxas, Spiros, Flaig, Robin, McCartney, Daniel, Campbell, Archie, Tomlinson, Laurie, Tazare, John, Zheng, Bang, Smeeth, Liam, Herrett, Emily, Cowling, Thomas, Mansfield, Kate, Costello, Ruth E., Wang, Kevin, Mansfield, Kathryn, Mahalingasivam, Viyaasan, Douglas, Ian, Langan, Sinead, Brophy, Sinead, Parker, Michael, Kennedy, Jonathan, McEachan, Rosie, Wright, John, Willan, Kathryn, Badrick, Ellena, Santorelli, Gillian, Yang, Tiffany, Hou, Bo, Steptoe, Andrew, Giorgio, Di Gessa, Zhu, Jingmin, Zaninotto, Paola, Wood, Angela, Cezard, Genevieve, Ip, Samantha, Bolton, Tom, Sampri, Alexia, Rafeti, Elena, Almaghrabi, Fatima, Sheikh, Aziz, Shah, Syed A., Katikireddi, Vittal, Shaw, Richard, Hamilton, Olivia, Green, Michael, Kromydas, Theocharis, Kopasker, Daniel, Greaves, Felix, Willans, Robert, Glen, Fiona, Sharp, Steve, Hughes, Alun, Wong, Andrew, Howes, Lee Hamill, Rapala, Alicja, Nigrelli, Lidia, McArdle, Fintan, Beckford, Chelsea, Raman, Betty, Dobson, Richard, Folarin, Amos, Stewart, Callum, Ranjan, Yatharth, Carpentieri, Jd, Sheard, Laura, Fang, Chao, Baz, Sarah, Gibson, Andy, Kellas, John, Neubauer, Stefan, Piechnik, Stefan, Lukaschuk, Elena, Saunders, Laura C., Wild, James M., Smith, Stephen, Jezzard, Peter, Tunnicliffe, Elizabeth, Sanders, Zeena-Britt, Finnigan, Lucy, Ferreira, Vanessa, Green, Mark, Rhead, Rebecca, Kibble, Milla, Wei, Yinghui, Lemanska, Agnieszka, Perez-Reche, Francisco, Piehlmaier, Dominik, Teece, Lucy, Parker, Edward, Walker, Alex J., Inglesby, Peter, Curtis, Helen J., Morton, Caroline E., Morley, Jessica, Mehrkar, Amir, Bacon, Sebastian C.J., Hickman, George, Croker, Richard, Evans, David, Ward, Tom, DeVito, Nicholas J., Green, Amelia C.A., Massey, Jon, Smith, Rebecca M., Hulme, William J., Davy, Simon, Andrews, Colm D., Hopcroft, Lisa E.M., Drysdale, Henry, Dillingham, Iain, Park, Robin Y., Higgins, Rose, Cunningham, Christine, Wiedemann, Milan, Maude, Steven, Macdonald, Orla, Butler-Cole, Ben F.C., O'Dwyer, Thomas, Stables, Catherine L., Wood, Christopher, Brown, Andrew D., Speed, Victoria, Bridges, Lucy, Schaffer, Andrea L., Walters, Caroline E., Rentsch, Christopher T., Bhaskaran, Krishnan, Schultze, Anna, Williamson, Elizabeth J., McDonald, Helen I., Tomlinson, Laurie A., Mathur, Rohini, Eggo, Rosalind M., Wing, Kevin, Wong, Angel Y.S., Grieve, Richard, Grint, Daniel J., Mansfield, Kathryn E., Douglas, Ian J., Evans, Stephen J.W., Walker, Jemma L., Cowling, Thomas E., Herrett, Emily L., Parker, Edward P.K., Bates, Christopher, Cockburn, Jonathan, Parry, John, Hester, Frank, Harper, Sam, O'Hanlon, Shaun, Eavis, Alex, Jarvis, Richard, Avramov, Dima, Griffiths, Paul, Fowles, Aaron, Parkes, Nasreen, Nicholson, Brian, Perera, Rafael, Harrison, David, Khunti, Kamlesh, Sterne, Jonathan AC., Quint, Jennifer, Henderson, Alasdair D., Carreira, Helena, Bidulka, Patrick, Warren-Gash, Charlotte, Hayes, Joseph F., Quint, Jennifer K., Katikireddi, Srinivasa Vittal, and Langan, Sinéad M.

Published
2023
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13. Joint Motion Estimation and Segmentation from Undersampled Cardiac MR Image

Author

Qin, Chen, Bai, Wenjia, Schlemper, Jo, Petersen, Steffen E., Piechnik, Stefan K., Neubauer, Stefan, and Rueckert, Daniel

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

Accelerating the acquisition of magnetic resonance imaging (MRI) is a challenging problem, and many works have been proposed to reconstruct images from undersampled k-space data. However, if the main purpose is to extract certain quantitative measures from the images, perfect reconstructions may not always be necessary as long as the images enable the means of extracting the clinically relevant measures. In this paper, we work on jointly predicting cardiac motion estimation and segmentation directly from undersampled data, which are two important steps in quantitatively assessing cardiac function and diagnosing cardiovascular diseases. In particular, a unified model consisting of both motion estimation branch and segmentation branch is learned by optimising the two tasks simultaneously. Additional corresponding fully-sampled images are incorporated into the network as a parallel sub-network to enhance and guide the learning during the training process. Experimental results using cardiac MR images from 220 subjects show that the proposed model is robust to undersampled data and is capable of predicting results that are close to that from fully-sampled ones, while bypassing the usual image reconstruction stage., Comment: This work is published at MLMIR 2018: Machine Learning for Medical Image Reconstruction

Published
2019
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14. 3D Cardiac Shape Prediction with Deep Neural Networks: Simultaneous Use of Images and Patient Metadata

Author

Attar, Rahman, Pereanez, Marco, Bowles, Christopher, Piechnik, Stefan K., Neubauer, Stefan, Petersen, Steffen E., and Frangi, Alejandro F.

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

Large prospective epidemiological studies acquire cardiovascular magnetic resonance (CMR) images for pre-symptomatic populations and follow these over time. To support this approach, fully automatic large-scale 3D analysis is essential. In this work, we propose a novel deep neural network using both CMR images and patient metadata to directly predict cardiac shape parameters. The proposed method uses the promising ability of statistical shape models to simplify shape complexity and variability together with the advantages of convolutional neural networks for the extraction of solid visual features. To the best of our knowledge, this is the first work that uses such an approach for 3D cardiac shape prediction. We validated our proposed CMR analytics method against a reference cohort containing 500 3D shapes of the cardiac ventricles. Our results show broadly significant agreement with the reference shapes in terms of the estimated volume of the cardiac ventricles, myocardial mass, 3D Dice, and mean and Hausdorff distance., Comment: Accepted for publication in MICCAI 2019

Published
2019

15. Improving the generalizability of convolutional neural network-based segmentation on CMR images

Author

Chen, Chen, Bai, Wenjia, Davies, Rhodri H., Bhuva, Anish N., Manisty, Charlotte, Moon, James C., Aung, Nay, Lee, Aaron M., Sanghvi, Mihir M., Fung, Kenneth, Paiva, Jose Miguel, Petersen, Steffen E., Lukaschuk, Elena, Piechnik, Stefan K., Neubauer, Stefan, and Rueckert, Daniel

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

Convolutional neural network (CNN) based segmentation methods provide an efficient and automated way for clinicians to assess the structure and function of the heart in cardiac MR images. While CNNs can generally perform the segmentation tasks with high accuracy when training and test images come from the same domain (e.g. same scanner or site), their performance often degrades dramatically on images from different scanners or clinical sites. We propose a simple yet effective way for improving the network generalization ability by carefully designing data normalization and augmentation strategies to accommodate common scenarios in multi-site, multi-scanner clinical imaging data sets. We demonstrate that a neural network trained on a single-site single-scanner dataset from the UK Biobank can be successfully applied to segmenting cardiac MR images across different sites and different scanners without substantial loss of accuracy. Specifically, the method was trained on a large set of 3,975 subjects from the UK Biobank. It was then directly tested on 600 different subjects from the UK Biobank for intra-domain testing and two other sets for cross-domain testing: the ACDC dataset (100 subjects, 1 site, 2 scanners) and the BSCMR-AS dataset (599 subjects, 6 sites, 9 scanners). The proposed method produces promising segmentation results on the UK Biobank test set which are comparable to previously reported values in the literature, while also performing well on cross-domain test sets, achieving a mean Dice metric of 0.90 for the left ventricle, 0.81 for the myocardium and 0.82 for the right ventricle on the ACDC dataset; and 0.89 for the left ventricle, 0.83 for the myocardium on the BSCMR-AS dataset. The proposed method offers a potential solution to improve CNN-based model generalizability for the cross-scanner and cross-site cardiac MR image segmentation task., Comment: 15 pages, 8 figures

Published
2019
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16. Automated Quality Control in Image Segmentation: Application to the UK Biobank Cardiac MR Imaging Study

Author

Robinson, Robert, Valindria, Vanya V., Bai, Wenjia, Oktay, Ozan, Kainz, Bernhard, Suzuki, Hideaki, Sanghvi, Mihir M., Aung, Nay, Paiva, Jos$é$ Miguel, Zemrak, Filip, Fung, Kenneth, Lukaschuk, Elena, Lee, Aaron M., Carapella, Valentina, Kim, Young Jin, Piechnik, Stefan K., Neubauer, Stefan, Petersen, Steffen E., Page, Chris, Matthews, Paul M., Rueckert, Daniel, and Glocker, Ben

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Background: The trend towards large-scale studies including population imaging poses new challenges in terms of quality control (QC). This is a particular issue when automatic processing tools, e.g. image segmentation methods, are employed to derive quantitative measures or biomarkers for later analyses. Manual inspection and visual QC of each segmentation isn't feasible at large scale. However, it's important to be able to automatically detect when a segmentation method fails so as to avoid inclusion of wrong measurements into subsequent analyses which could lead to incorrect conclusions. Methods: To overcome this challenge, we explore an approach for predicting segmentation quality based on Reverse Classification Accuracy, which enables us to discriminate between successful and failed segmentations on a per-cases basis. We validate this approach on a new, large-scale manually-annotated set of 4,800 cardiac magnetic resonance scans. We then apply our method to a large cohort of 7,250 cardiac MRI on which we have performed manual QC. Results: We report results used for predicting segmentation quality metrics including Dice Similarity Coefficient (DSC) and surface-distance measures. As initial validation, we present data for 400 scans demonstrating 99% accuracy for classifying low and high quality segmentations using predicted DSC scores. As further validation we show high correlation between real and predicted scores and 95% classification accuracy on 4,800 scans for which manual segmentations were available. We mimic real-world application of the method on 7,250 cardiac MRI where we show good agreement between predicted quality metrics and manual visual QC scores. Conclusions: We show that RCA has the potential for accurate and fully automatic segmentation QC on a per-case basis in the context of large-scale population imaging as in the UK Biobank Imaging Study., Comment: 14 pages, 7 figures, Journal of Cardiovascular Magnetic Resonance

Published
2019

17. High Throughput Computation of Reference Ranges of Biventricular Cardiac Function on the UK Biobank Population Cohort

Author

Attar, Rahman, Pereanez, Marco, Gooya, Ali, Alba, Xenia, Zhang, Le, Piechnik, Stefan K., Neubauer, Stefan, Petersen, Steffen E., and Frangi, Alejandro F.

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

The exploitation of large-scale population data has the potential to improve healthcare by discovering and understanding patterns and trends within this data. To enable high throughput analysis of cardiac imaging data automatically, a pipeline should comprise quality monitoring of the input images, segmentation of the cardiac structures, assessment of the segmentation quality, and parsing of cardiac functional indexes. We present a fully automatic, high throughput image parsing workflow for the analysis of cardiac MR images, and test its performance on the UK Biobank (UKB) cardiac dataset. The proposed pipeline is capable of performing end-to-end image processing including: data organisation, image quality assessment, shape model initialisation, segmentation, segmentation quality assessment, and functional parameter computation; all without any user interaction. To the best of our knowledge,this is the first paper tackling the fully automatic 3D analysis of the UKB population study, providing reference ranges for all key cardiovascular functional indexes, from both left and right ventricles of the heart. We tested our workflow on a reference cohort of 800 healthy subjects for which manual delineations, and reference functional indexes exist. Our results show statistically significant agreement between the manually obtained reference indexes, and those automatically computed using our framework., Comment: Accepted in STACOM workshop of MICCAI2018

Published
2019

18. Deep-Learning for Epicardial Adipose Tissue Assessment With Computed Tomography: Implications for Cardiovascular Risk Prediction

Author

West, Henry W., Siddique, Muhammad, Williams, Michelle C., Volpe, Lucrezia, Desai, Ria, Lyasheva, Maria, Thomas, Sheena, Dangas, Katerina, Kotanidis, Christos P., Tomlins, Pete, Mahon, Ciara, Kardos, Attila, Adlam, David, Graby, John, Rodrigues, Jonathan C.L., Shirodaria, Cheerag, Deanfield, John, Mehta, Nehal N., Neubauer, Stefan, Channon, Keith M., Desai, Milind Y., Nicol, Edward D., Newby, David E., and Antoniades, Charalambos

Published
2023
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20. Myocardial Injury on CMR in Patients With COVID-19 and Suspected Cardiac Involvement

Author

Vidula, Mahesh K., Rajewska-Tabor, Justyna, Cao, J. Jane, Kang, Yu, Craft, Jason, Mei, Winifred, Chandrasekaran, Preethi S., Clark, Daniel E., Poenar, Ana-Maria, Gorecka, Miroslawa, Malahfji, Maan, Cowan, Eilidh, Kwan, Jennifer M., Reinhardt, Samuel W., Al-Tabatabaee, Sarah, Doeblin, Patrick, Villa, Adriana D.M., Karagodin, Ilya, Alvi, Nazia, Christia, Panagiota, Spetko, Nicholas, Cassar, Mark Philip, Park, Christine, Nambiar, Lakshmi, Turgut, Alper, Azad, Mahan Roosta, Lambers, Moritz, Wong, Timothy C., Salerno, Michael, Kim, Jiwon, Elliott, Michael, Raman, Betty, Neubauer, Stefan, Tsao, Connie W., LaRocca, Gina, Patel, Amit R., Chiribiri, Amedeo, Kelle, Sebastian, Baldassarre, Lauren A., Shah, Dipan J., Hughes, Sean G., Tong, Matthew S., Pyda, Malgorzata, Simonetti, Orlando P., Plein, Sven, and Han, Yuchi

Published
2023
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21. 3-Dimensional Strain Analysis of Hypertrophic Cardiomyopathy: Insights From the NHLBI International HCM Registry

Author

Heydari, Bobak, Satriano, Alessandro, Jerosch-Herold, Michael, Kolm, Paul, Kim, Dong-Yun, Cheng, Kathleen, Choi, Yuna L., Antiochos, Panagiotis, White, James A., Mahmod, Masliza, Chan, Kenneth, Raman, Betty, Desai, Milind Y., Ho, Carolyn Y., Dolman, Sarahfaye F., Desvigne-Nickens, Patrice, Maron, Martin S., Friedrich, Matthias G., Schulz-Menger, Jeanette, Piechnik, Stefan K., Appelbaum, Evan, Weintraub, William S., Neubauer, Stefan, Kramer, Christopher M., and Kwong, Raymond Y.

Published
2023
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23. The cardiac sympathetic co-transmitter neuropeptide Y is pro-arrhythmic following ST-elevation myocardial infarction despite beta-blockade

Author

Kalla, Manish, Hao, Guoliang, Tapoulal, Nidi, Tomek, Jakub, Liu, Kun, Woodward, Lavinia, Dall’Armellina, Erica, Banning, Adrian P, Choudhury, Robin P, Neubauer, Stefan, Kharbanda, Rajesh K, Channon, Keith M, Ajijola, Olujimi A, Shivkumar, Kalyanam, Paterson, David J, and Herring, Neil

Subjects
Medical Physiology, Biomedical and Clinical Sciences, Heart Disease, Neurosciences, Heart Disease - Coronary Heart Disease, Cardiovascular, Prevention, Animals, Heart, Humans, Neuropeptide Y, Percutaneous Coronary Intervention, Rats, ST Elevation Myocardial Infarction, Ventricular Fibrillation, Myocardial infarction, Percutaneous coronary intervention, Ventricular tachycardia, Ventricular fibrillation, ‘Oxford Acute Myocardial Infarction (OxAMI) Study’, Cardiorespiratory Medicine and Haematology, Clinical Sciences, Cardiovascular System & Hematology, Cardiovascular medicine and haematology, Clinical sciences
Abstract

AimsST-elevation myocardial infarction is associated with high levels of cardiac sympathetic drive and release of the co-transmitter neuropeptide Y (NPY). We hypothesized that despite beta-blockade, NPY promotes arrhythmogenesis via ventricular myocyte receptors.Methods and resultsIn 78 patients treated with primary percutaneous coronary intervention, sustained ventricular tachycardia (VT) or fibrillation (VF) occurred in 6 (7.7%) within 48 h. These patients had significantly (P

Published
2020

24. Automatic Assessment of Full Left Ventricular Coverage in Cardiac Cine Magnetic Resonance Imaging with Fisher-Discriminative 3D CNN

Author

Zhang, Le, Gooya, Ali, Pereanez, Marco, Dong, Bo, Piechnik, Stefan K., Neubauer, Stefan, Petersen, Steffen E., and Frangi, Alejandro F.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Cardiac magnetic resonance (CMR) images play a growing role in the diagnostic imaging of cardiovascular diseases. Full coverage of the left ventricle (LV), from base to apex, is a basic criterion for CMR image quality and necessary for accurate measurement of cardiac volume and functional assessment. Incomplete coverage of the LV is identified through visual inspection, which is time-consuming and usually done retrospectively in the assessment of large imaging cohorts. This paper proposes a novel automatic method for determining LV coverage from CMR images by using Fisher-discriminative three-dimensional (FD3D) convolutional neural networks (CNNs). In contrast to our previous method employing 2D CNNs, this approach utilizes spatial contextual information in CMR volumes, extracts more representative high-level features and enhances the discriminative capacity of the baseline 2D CNN learning framework, thus achieving superior detection accuracy. A two-stage framework is proposed to identify missing basal and apical slices in measurements of CMR volume. First, the FD3D CNN extracts high-level features from the CMR stacks. These image representations are then used to detect the missing basal and apical slices. Compared to the traditional 3D CNN strategy, the proposed FD3D CNN minimizes within-class scatter and maximizes between-class scatter. We performed extensive experiments to validate the proposed method on more than 5,000 independent volumetric CMR scans from the UK Biobank study, achieving low error rates for missing basal/apical slice detection (4.9\%/4.6\%). The proposed method can also be adopted for assessing LV coverage for other types of CMR image data., Comment: 12 pages, 5 figures, accepted by IEEE Transactions on Biomedical Engineering

Published
2018

26. Long COVID and cardiovascular disease: a prospective cohort study

Author

Lawson, Claire Alexandra, primary, Moss, Alastair James, additional, Arnold, Jayanth Ranjit, additional, Bagot, Catherine, additional, Banerjee, Amitava, additional, Berry, Colin, additional, Greenwood, John, additional, Hughes, Alun D, additional, Khunti, Kamlesh, additional, Mills, Nicholas L, additional, Neubauer, Stefan, additional, Raman, Betty, additional, Sattar, Naveed, additional, Leavy, Olivia C, additional, Richardson, Matthew, additional, Elneima, Omer, additional, McAuley, Hamish JC, additional, Shikotra, Aarti, additional, Singapuri, Amisha, additional, Sereno, Marco, additional, Saunders, Ruth, additional, Harris, Victoria, additional, Houchen-Wolloff, Linzy, additional, Greening, Neil J, additional, Harrison, Ewen, additional, Docherty, Annemarie B, additional, Lone, Nazir I, additional, Quint, Jennifer Kathleen, additional, Chalmers, James, additional, Ho, Ling-Pei, additional, Horsley, Alex, additional, Marks, Michael, additional, Poinasamy, Krisnah, additional, Evans, Rachael, additional, Wain, Louise V, additional, Brightling, Chris, additional, and McCann, Gerry P, additional

Published
2024
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27. Hypertrophic cardiomyopathy detection with artificial intelligence electrocardiography in international cohorts: an external validation study

Author

Siontis, Konstantinos C, primary, Wieczorek, Mikolaj A, additional, Maanja, Maren, additional, Hodge, David O, additional, Kim, Hyung-Kwan, additional, Lee, Hyun-Jung, additional, Lee, Heesun, additional, Lim, Jaehyun, additional, Park, Chan Soon, additional, Ariga, Rina, additional, Raman, Betty, additional, Mahmod, Masliza, additional, Watkins, Hugh, additional, Neubauer, Stefan, additional, Windecker, Stephan, additional, Siontis, George C M, additional, Gersh, Bernard J, additional, Ackerman, Michael J, additional, Attia, Zachi I, additional, Friedman, Paul A, additional, and Noseworthy, Peter A, additional

Published
2024
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28. Real-time Prediction of Segmentation Quality

Author

Robinson, Robert, Oktay, Ozan, Bai, Wenjia, Valindria, Vanya, Sanghvi, Mihir, Aung, Nay, Paiva, José, Zemrak, Filip, Fung, Kenneth, Lukaschuk, Elena, Lee, Aaron, Carapella, Valentina, Kim, Young Jin, Kainz, Bernhard, Piechnik, Stefan, Neubauer, Stefan, Petersen, Steffen, Page, Chris, Rueckert, Daniel, and Glocker, Ben

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Recent advances in deep learning based image segmentation methods have enabled real-time performance with human-level accuracy. However, occasionally even the best method fails due to low image quality, artifacts or unexpected behaviour of black box algorithms. Being able to predict segmentation quality in the absence of ground truth is of paramount importance in clinical practice, but also in large-scale studies to avoid the inclusion of invalid data in subsequent analysis. In this work, we propose two approaches of real-time automated quality control for cardiovascular MR segmentations using deep learning. First, we train a neural network on 12,880 samples to predict Dice Similarity Coefficients (DSC) on a per-case basis. We report a mean average error (MAE) of 0.03 on 1,610 test samples and 97% binary classification accuracy for separating low and high quality segmentations. Secondly, in the scenario where no manually annotated data is available, we train a network to predict DSC scores from estimated quality obtained via a reverse testing strategy. We report an MAE=0.14 and 91% binary classification accuracy for this case. Predictions are obtained in real-time which, when combined with real-time segmentation methods, enables instant feedback on whether an acquired scan is analysable while the patient is still in the scanner. This further enables new applications of optimising image acquisition towards best possible analysis results., Comment: Accepted at MICCAI 2018

Published
2018

29. Joint Learning of Motion Estimation and Segmentation for Cardiac MR Image Sequences

Author

Qin, Chen, Bai, Wenjia, Schlemper, Jo, Petersen, Steffen E., Piechnik, Stefan K., Neubauer, Stefan, and Rueckert, Daniel

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Cardiac motion estimation and segmentation play important roles in quantitatively assessing cardiac function and diagnosing cardiovascular diseases. In this paper, we propose a novel deep learning method for joint estimation of motion and segmentation from cardiac MR image sequences. The proposed network consists of two branches: a cardiac motion estimation branch which is built on a novel unsupervised Siamese style recurrent spatial transformer network, and a cardiac segmentation branch that is based on a fully convolutional network. In particular, a joint multi-scale feature encoder is learned by optimizing the segmentation branch and the motion estimation branch simultaneously. This enables the weakly-supervised segmentation by taking advantage of features that are unsupervisedly learned in the motion estimation branch from a large amount of unannotated data. Experimental results using cardiac MRI images from 220 subjects show that the joint learning of both tasks is complementary and the proposed models outperform the competing methods significantly in terms of accuracy and speed., Comment: accepted by MICCAI 2018

Published
2018

32. Constructing custom-made radiotranscriptomic signatures of vascular inflammation from routine CT angiograms: a prospective outcomes validation study in COVID-19

Author

Ahern, David J, Ai, Zhichao, Ainsworth, Mark, Allan, Chris, Allcock, Alice, Angus, Brian, Ansari, M Azim, Arancibia-Cárcamo, Carolina, Aschenbrenner, Dominik, Attar, Moustafa, Baillie, J Kenneth, Barnes, Eleanor, Bashford-Rogers, Rachael, Bashyal, Archana, Beer, Sally, Berridge, Georgina, Beveridge, Amy, Bibi, Sagida, Bicanic, Tihana, Blackwell, Luke, Bowness, Paul, Brent, Andrew, Brown, Andrew, Broxholme, John, Buck, David, Burnham, Katie, Byrne, Helen, Camara, Susana, Ferreira, Ivan Candido, Charles, Philip, Chen, Wentao, Chen, Yi-Ling, Chong, Amanda, Clutterbuck, Elizabeth, Coles, Mark, Conlon, Christopher, Cornall, Richard, Cribbs, Adam, Curion, Fabiola, Davenport, Emma, Davidson, Neil, Davis, Simon, Dendrou, Calliope, Dequaire, Julie, Dib, Lea, Docker, James, Dold, Christina, Dong, Tao, Downes, Damien, Drakesmith, Hal, Dunachie, Susanna, Duncan, David, Eijsbouts, Chris, Esnouf, Robert, Espinosa, Alexis, Etherington, Rachel, Fairfax, Benjamin, Fairhead, Rory, Fang, Hai, Fassih, Shayan, Felle, Sally, Fernandez Mendoza, Maria, Ferreira, Ricardo, Fischer, Roman, Foord, Thomas, Forrow, Aden, Frater, John, Fries, Anastasia, Gallardo Sanchez, Veronica, Garner, Lucy, Geeves, Clementine, Georgiou, Dominique, Godfrey, Leila, Golubchik, Tanya, Gomez Vazquez, Maria, Green, Angie, Harper, Hong, Harrington, Heather, Heilig, Raphael, Hester, Svenja, Hill, Jennifer, Hinds, Charles, Hird, Clare, Ho, Ling-Pei, Hoekzema, Renee, Hollis, Benjamin, Hughes, Jim, Hutton, Paula, Jackson-Wood, Matthew, Jainarayanan, Ashwin, James-Bott, Anna, Jansen, Kathrin, Jeffery, Katie, Jones, Elizabeth, Jostins, Luke, Kerr, Georgina, Kim, David, Klenerman, Paul, Knight, Julian, Kumar, Vinod, Kumar Sharma, Piyush, Kurupati, Prathiba, Kwok, Andrew, Lee, Angela, Linder, Aline, Lockett, Teresa, Lonie, Lorne, Lopopolo, Maria, Lukoseviciute, Martyna, Luo, Jian, Marinou, Spyridoula, Marsden, Brian, Martinez, Jose, Matthews, Philippa, Mazurczyk, Michalina, McGowan, Simon, McKechnie, Stuart, Mead, Adam, Mentzer, Alexander, Mi, Yuxin, Monaco, Claudia, Montadon, Ruddy, Napolitani, Giorgio, Nassiri, Isar, Novak, Alex, O'Brien, Darragh, O'Connor, Daniel, O'Donnell, Denise, Ogg, Graham, Overend, Lauren, Park, Inhye, Pavord, Ian, Peng, Yanchun, Penkava, Frank, Pereira Pinho, Mariana, Perez, Elena, Pollard, Andrew, Powrie, Fiona, Psaila, Bethan, Quan, T Phuong, Repapi, Emmanouela, Revale, Santiago, Silva-Reyes, Laura, Richard, Jean-Baptiste, Rich-Griffin, Charlotte, Ritter, Thomas, Rollier, Christine, Rowland, Matthew, Ruehle, Fabian, Salio, Mariolina, Sansom, Stephen Nicholas, Sanches Peres, Raphael, Santos Delgado, Alberto, Sauka-Spengler, Tatjana, Schwessinger, Ron, Scozzafava, Giuseppe, Screaton, Gavin, Seigal, Anna, Semple, Malcolm, Sergeant, Martin, Simoglou Karali, Christina, Sims, David, Skelly, Donal, Slawinski, Hubert, Sobrinodiaz, Alberto, Sousos, Nikolaos, Stafford, Lizzie, Stockdale, Lisa, Strickland, Marie, Sumray, Otto, Sun, Bo, Taylor, Chelsea, Taylor, Stephen, Taylor, Adan, Thongjuea, Supat, Thraves, Hannah, Todd, John, Tomic, Adriana, Tong, Orion, Trebes, Amy, Trzupek, Dominik, Tucci, Felicia Anna, Turtle, Lance, Udalova, Irina, Uhlig, Holm, van Grinsven, Erinke, Vendrell, Iolanda, Verheul, Marije, Voda, Alexandru, Wang, Guanlin, Wang, Lihui, Wang, Dapeng, Watkinson, Peter, Watson, Robert, Weinberger, Michael, Whalley, Justin, Witty, Lorna, Wray, Katherine, Xue, Luzheng, Yuen Yeung, Hing, Yin, Zixi, Young, Rebecca, Youngs, Jonathan, Zhang, Ping, Zurke, Yasemin-Xiomara, Banning, Adrian, Antonopoulos, Alexios, Bajaj, Amrita, Kelion, Andrew, Deshpande, Aparna, Kardos, Attila, Hudson, Benjamin, Koo, Bon-Kwon, Shirodaria, Cheerag, Xie, Cheng, Kotanidis, Christos, Mahon, Ciara, Berry, Colin, Adlam, David, Newby, David, Connolly, Derek, Scaletta, Diane, Alexander, Donna, Nicol, Ed, McAlindon, Elisa, Oikonomou, Evangelos, Pugliese, Francesca, Pontone, Gianluca, Benedetti, Giulia, He, Guo-Wei, West, Henry, Kondo, Hidekazu, Benedek, Imre, Das, Intrajeet, Deanfield, John, Graby, John, Greenwood, John, Rodrigues, Jonathan, Ge, Junbo, Channon, Keith, Fabritz, Larissa, Fan, Li-Juan, Kingham, Lucy, Guglielmo, Marco, Lyasheva, Maria, Schmitt, Matthias, Beer, Meinrad, Anderson, Michelle, Desai, Milind, Marwan, Mohamed, Takahashi, Naohiko, Mehta, Nehal, Dai, Neng, Screaton, Nicholas, Sabharwal, Nikant, Maurovich-Horvat, Pál, Rao, Praveen, Kotronias, Rafail, Kharbanda, Rajesh, Preston, Rebecca, Wood, Richard, Blankstein, Ron, Rajani, Ronak, Mirsadraee, Saeed, Munir, Shahzad, Thomas, Sheena, Neubauer, Stefan, Klömpken, Steffen, Petersen, Steffen, Achenbach, Stephan, Anthony, Susan, Mak, Sze, Mittal, Tarun, Benedek, Theodora, Sharma, Vinoda, Lin, Wen-Hua, Kotanidis, Christos P, Rodrigues, Jonathan C L, O’Connor, Daniel, Siddique, Muhammad, Lockstone, Helen, Oikonomou, Evangelos K, Badi, Ileana, Lumley, Sheila F, Constantinides, Bede, Sanderson, Nicholas, Rodger, Gillian, Chau, Kevin K, Lodge, Archie, Tsakok, Maria, Gleeson, Fergus, Indrajeet, Das, Hudson, Benjamin J, Srivastava, Vivek, Farid, Shakil, Krasopoulos, George, Sayeed, Rana, Newby, David E, Channon, Keith M, and Antoniades, Charalambos

Published
2022
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33. Myocardial Involvement After Hospitalization for COVID-19 Complicated by Troponin Elevation: A Prospective, Multicenter, Observational Study

Author

Artico, Jessica, Shiwani, Hunain, Moon, James C., Gorecka, Miroslawa, McCann, Gerry P., Roditi, Giles, Morrow, Andrew, Mangion, Kenneth, Lukaschuk, Elena, Shanmuganathan, Mayooran, Miller, Christopher A., Chiribiri, Amedeo, Prasad, Sanjay K., Adam, Robert D., Singh, Trisha, Bucciarelli-Ducci, Chiara, Dawson, Dana, Knight, Daniel, Fontana, Marianna, Manisty, Charlotte, Treibel, Thomas A., Levelt, Eylem, Arnold, Ranjit, Macfarlane, Peter W., Young, Robin, McConnachie, Alex, Neubauer, Stefan, Piechnik, Stefan K., Davies, Rhodri H., Ferreira, Vanessa M., Dweck, Marc R., Berry, Colin, Greenwood, John P., Kelly, Bernard, Goreka, Miroslawa, Somers, Kathryn, Byrom-Goulthorp, Roo J., Anderson, Michelle, Britton, Laura, Richards, Fiona, Jones, Laura M., Moss, Alastair, Fisher, Jude, Wormleighton, Joanne, Parke, Kelly, Wright, Rachel, Yeo, Jian, Falconer, Judith, Harries, Valerie, Henderson, Paula, Newby, David, Popescu, Iulia, Zhang, Qiang, Raman, Betty, Channon, Keith, Krasopoulos, Catherine, Nunes, Claudia, Da Silva Rodrigues, Liliana, Nixon, Harriet, Panopoulou, Athanasia, Fletcher, Alison, Manley, Peter, Sykes, Robert, Fallon, Kirsty, Brown, Ammani, Kelly, Laura, McGinley, Christopher, Briscoe, Michael, Woodward, Rosemary, Hopkins, Tracey, McLennan, Evonne, Tynan, Nicola, Dymock, Laura, Swoboda, Peter, Wright, Judith, Exley, Donna, Steeds, Richard, Hutton, Kady, MacDonald, Sonia, Shetye, Abhishek, Orsborne, Christopher, Woodville-Jones, William, Ferguson, Susan, Bratis, Konstantinos, Fairbairn, Timothy, Sionas, Michail, Widdows, Peris, Gee Chew, Pei, Marsden, Christian, Collins, Tom, George, Linsha, Kearney, Lisa, Flett, Andrew, Smith, Simon, Zhenge, Alice, Harvey, Jake, Inacio, Liliana, Hanam-Penfold, Tomas, Gruner, Lucy, Razvi, Yousuf S.K., Crause, Jacolene, Davies, Nina M., Brown, James T., Chaco, Liza, Patel, Rishi, Kotecha, Tushar, Knight, Dan S., Green, Thomas, Ripley, David, Thompson, Maria, Cifra, Ugochi Akerele Elna, Alskaf, Ebraham, Crawley, Richard, Villa, Adriana, Nightingale, Angus K., Wright, Kim, Bonnick, Esther D., Hopkins, Emma, George, Jessy, Joseph, Linta, Cole, Graham, Vimalesvaran, Kavitha, Ali, Nadine, Carr, Caitlin R., Ross, Alexandra A.R., King, Clara, Farzad, Zohreh, Salmi, Sara A., Kirby, Kevin, McDiarmid, Adam, Stevenson, Hannah J., Matsvimbo, Pamela S., Joji, Lency, Fearby, Margaret, Brown, Benjamin, Bunce, Nicholas, Jennings, Robert, Sookhoo, Vennessa, Joshi, Shatabdi, Kanagala, Prathap, Fullalove, Sandra, Toohey, Catherine, Fenlon, Kate, Bellenger, Nicholas, He, Jingzhou, Statton, Sarah, Pamphilon, Nicola, Steele, Anna, Ball, Claire, McGahey, Ann, Balma, Silvia, Wilkes, Lynsey, Lewis, Katy, Walter, Michelle, Ionescu, Adrian, Ninan, Tishi, Richards, Suzanne, Williams, Marie, Alfakih, Khaled, Pilgrim, Samia, Joy, George, and Hussain, Ifza

Published
2023
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34. Automated cardiovascular magnetic resonance image analysis with fully convolutional networks

Author

Bai, Wenjia, Sinclair, Matthew, Tarroni, Giacomo, Oktay, Ozan, Rajchl, Martin, Vaillant, Ghislain, Lee, Aaron M., Aung, Nay, Lukaschuk, Elena, Sanghvi, Mihir M., Zemrak, Filip, Fung, Kenneth, Paiva, Jose Miguel, Carapella, Valentina, Kim, Young Jin, Suzuki, Hideaki, Kainz, Bernhard, Matthews, Paul M., Petersen, Steffen E., Piechnik, Stefan K., Neubauer, Stefan, Glocker, Ben, and Rueckert, Daniel

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Cardiovascular magnetic resonance (CMR) imaging is a standard imaging modality for assessing cardiovascular diseases (CVDs), the leading cause of death globally. CMR enables accurate quantification of the cardiac chamber volume, ejection fraction and myocardial mass, providing information for diagnosis and monitoring of CVDs. However, for years, clinicians have been relying on manual approaches for CMR image analysis, which is time consuming and prone to subjective errors. It is a major clinical challenge to automatically derive quantitative and clinically relevant information from CMR images. Deep neural networks have shown a great potential in image pattern recognition and segmentation for a variety of tasks. Here we demonstrate an automated analysis method for CMR images, which is based on a fully convolutional network (FCN). The network is trained and evaluated on a large-scale dataset from the UK Biobank, consisting of 4,875 subjects with 93,500 pixelwise annotated images. The performance of the method has been evaluated using a number of technical metrics, including the Dice metric, mean contour distance and Hausdorff distance, as well as clinically relevant measures, including left ventricle (LV) end-diastolic volume (LVEDV) and end-systolic volume (LVESV), LV mass (LVM); right ventricle (RV) end-diastolic volume (RVEDV) and end-systolic volume (RVESV). By combining FCN with a large-scale annotated dataset, the proposed automated method achieves a high performance on par with human experts in segmenting the LV and RV on short-axis CMR images and the left atrium (LA) and right atrium (RA) on long-axis CMR images., Comment: Accepted for publication by Journal of Cardiovascular Magnetic Resonance

Published
2017

35. Assessment of Cardiac Energy Metabolism, Function, and Physiology in Patients With Heart Failure Taking Empagliflozin: The Randomized, Controlled EMPA-VISION Trial

Author

Hundertmark, Moritz J., Adler, Amanda, Antoniades, Charalambos, Coleman, Ruth, Griffin, Julian L., Holman, Rury R., Lamlum, Hanan, Lee, Jisoo, Massey, Daniel, Miller, Jack J.J.J., Milton, Joanne E., Monga, Shveta, Mózes, Ferenc E., Nazeer, Areesha, Raman, Betty, Rider, Oliver, Rodgers, Christopher T., Valkovič, Ladislav, Wicks, Eleanor, Mahmod, Masliza, and Neubauer, Stefan

Published
2023
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37. Society for Cardiovascular Magnetic Resonance (SCMR) guidelines for reporting cardiovascular magnetic resonance examinations

Author

Hundley, W. Gregory, Bluemke, David A., Bogaert, Jan, Flamm, Scott D., Fontana, Marianna, Friedrich, Matthias G., Grosse-Wortmann, Lars, Karamitsos, Theodoros D., Kramer, Christopher M., Kwong, Raymond Y., McConnell, Michael, Nagel, Eike, Neubauer, Stefan, Nijveldt, Robin, Pennell, Dudley J., Petersen, Steffen E., Raman, Subha V., and van Rossum, Albert

Published
2022
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40. Increased serum miR-193a-5p during non-alcoholic fatty liver disease progression: Diagnostic and mechanistic relevance

Author

Clark, James, Cordell, Heather J., Darlay, Rebecca, Day, Christopher P., Hardy, Tim, Liu, Yang-Lin, Oakley, Fiona, Palmer, Jeremy, Queen, Rachel, Wonders, Kristy, Bossuyt, Patrick M., Holleboom, Adriaan G., Zafarmand, Hadi, Vali, Yasaman, Lee, Jenny, Clement, Karine, Pais, Raluca, Schuppan, Detlef, Allison, Michael, Cuenca, Sergio Rodriguez, Pellegrinelli, Vanessa, Vacca, Michele, Vidal-Puig, Antonio, Hyötyläinen, Tuulia, McGlinchey, Aidan, Orešič, Matej, Sen, Partho, Mato, Jose, Millet, Óscar, Dufour, Jean-Francois, Harrison, Stephen, Neubauer, Stefan, Pavlides, Michael, Mozes, Ferenc, Akhtar, Salma, Banerjee, Rajarshi, Kelly, Matt, Shumbayawonda, Elizabeth, Dennis, Andrea, Erpicum, Charlotte, Romero-Gomez, Manuel, Gallego-Durán, Rocío, Fernández, Isabel, Karsdal, Morten, Leeming, Diana, Fisker, Mette Juul, Erhardtsen, Elisabeth, Rasmussen, Daniel, Qvist, Per, Sinisi, Antonia, Sandt, Estelle, Tonini, Maria Manuela, Parola, Maurizio, Rosso, Chiara, Marra, Fabio, Gastaldelli, Amalia, Francque, Sven, Kechagias, Stergios, Yki-Järvinen, Hannele, Porthan, Kimmo, van Mil, Saskia, Papatheodoridis, George, Cortez-Pinto, Helena, Valenti, Luca, Petta, Salvatore, Miele, Luca, Geier, Andreas, Trautwein, Christian, Hockings, Paul, Newsome, Phil, Wenn, David, Pereira Rodrigues, Cecília Maria, Hanf, Rémy, Chaumat, Pierre, Rosenquist, Christian, Trylesinski, Aldo, Ortiz, Pablo, Duffin, Kevin, Yunis, Carla, Miller, Melissa, Tuthill, Theresa, Ertle, Judith, Younes, Ramy, Alexander, Leigh, Ostroff, Rachel, Kjær, Mette Skalshøi, Mikkelsen, Lars Friis, Brass, Clifford, Jennings, Lori, Balp, Maria-Magdalena, Martic, Miljen, Hanauer, Guido, Shankar, Sudha, Torstenson, Richard, Fournier, Céline, Ehman, Richard, Kalutkiewicz, Michael, Pepin, Kay, Myers, Joel, Shevell, Diane, Ho, Gideon, Landgren, Henrik, Myers, Rob, Doward, Lynda, Whalley, Diane, Twiss, James, Johnson, Katherine, Leary, Peter J., Govaere, Olivier, Barter, Matthew J., Charlton, Sarah H., Cockell, Simon J., Tiniakos, Dina, Zatorska, Michalina, Bedossa, Pierre, Brosnan, M. Julia, Cobbold, Jeremy F., Ekstedt, Mattias, Aithal, Guruprasad P., Clément, Karine, Schattenberg, Jörn M., Boursier, Jerome, Ratziu, Vlad, Bugianesi, Elisabetta, Anstee, Quentin M., and Daly, Ann K.

Published
2022
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41. Symptom Persistence Despite Improvement in Cardiopulmonary Health – Insights from longitudinal CMR, CPET and lung function testing post-COVID-19

Author

Cassar, Mark Philip, Tunnicliffe, Elizabeth M., Petousi, Nayia, Lewandowski, Adam J., Xie, Cheng, Mahmod, Masliza, Samat, Azlan Helmy Abd, Evans, Rachael A., Brightling, Christopher E., Ho, Ling-Pei, Piechnik, Stefan K., Talbot, Nick P., Holdsworth, David, Ferreira, Vanessa M., Neubauer, Stefan, and Raman, Betty

Published
2021
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42. Predictors of Major Atrial Fibrillation Endpoints in the National Heart, Lung, and Blood Institute HCMR

Author

Kramer, Christopher M., DiMarco, John P., Kolm, Paul, Ho, Carolyn Y., Desai, Milind Y., Kwong, Raymond Y., Dolman, Sarahfaye F., Desvigne-Nickens, Patrice, Geller, Nancy, Kim, Dong-Yun, Maron, Martin S., Appelbaum, Evan, Jerosch-Herold, Michael, Friedrich, Matthias G., Schulz-Menger, Jeanette, Piechnik, Stefan K., Mahmod, Masliza, Jacoby, Daniel, White, James, Chiribiri, Amedeo, Helms, Adam, Choudhury, Lubna, Michels, Michelle, Bradlow, William, Salerno, Michael, Dawson, Dana K., Weinsaft, Jonathan W., Berry, Colin, Nagueh, Sherif F., Buccarelli-Ducci, Chiara, Owens, Anjali, Casadei, Barbara, Watkins, Hugh, Weintraub, William S., and Neubauer, Stefan

Published
2021
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43. Maximal Wall Thickness Measurement in Hypertrophic Cardiomyopathy: Biomarker Variability and its Impact on Clinical Care

Author

Captur, Gabriella, Manisty, Charlotte H., Raman, Betty, Marchi, Alberto, Wong, Timothy C., Ariga, Rina, Bhuva, Anish, Ormondroyd, Elizabeth, Lobascio, Ilaria, Camaioni, Claudia, Loizos, Savvas, Bonsu-Ofori, Jenade, Turer, Aslan, Zaha, Vlad G., Augutsto, João B., Davies, Rhodri H., Taylor, Andrew J., Nasis, Arthur, Al-Mallah, Mouaz H., Valentin, Sinitsyn, Perez de Arenaza, Diego, Patel, Vimal, Westwood, Mark, Petersen, Steffen E., Li, Chunming, Tang, Lijun, Nakamori, Shiro, Nezafat, Reza, Kwong, Raymond Y., Ho, Carolyn Y., Fraser, Alan G., Watkins, Hugh, Elliott, Perry M., Neubauer, Stefan, Lloyd, Guy, Olivotto, Iacopo, Nihoyannopoulos, Petros, and Moon, James C.

Published
2021
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46. Association of Mortality, Hospitalization, and Impaired Recovery with Adverse Cardiac Magnetic Resonance Phenotypes in Hypertrophic Cardiomyopathy Patients post-covid-19: An HCMR Analysis

Author

Raman, Betty, primary, Kolm, Paul, additional, Case, Brian, additional, Wallace, Ryan, additional, Dolman, Sarahfaye, additional, Weintraub, William, additional, Desai, Milind, additional, Marco, John Di, additional, Desvigne-Nickens, Patrice, additional, Geller, Nancy, additional, Ho, Carolyn, additional, Kim, Dong-Yun, additional, Kramer, Christopher, additional, and Neubauer, Stefan, additional

Published
2024
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47. Investigating the Role of the Right Ventricle in Hfpef Using Exercise CMR

Author

Xavier, Roshan, primary, Ng, Sher May, additional, Pan, Jiliu, additional, Miller, Jack, additional, Mózes, Ferenc, additional, Valkovič, Ladislav, additional, Rayner, Jenny, additional, Rigolli, Marzia, additional, Fronheiser, Matthew, additional, Neubauer, Stefan, additional, Lewis, Andrew, additional, and Rider, Oliver, additional

Published
2024
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49. Characterization of Pericardial Effusion Using t1-mapping in Patients with Atrial Fibrillation Undergoing Catheter Ablation

Author

Stracquadanio, Antonio, primary, Ende, Adam Von, additional, Gajendragadkar, Parag, additional, Bashir, Yaver, additional, Betts, Tim, additional, Ginks, Matthew, additional, Rajappan, Kim, additional, Pedersen, Michala, additional, Spartera, Marco, additional, Piechnik, Stefan, additional, Neubauer, Stefan, additional, Ferreira, Vanessa, additional, Casadei, Barbara, additional, and Wijesurendra, Rohan, additional

Published
2024
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50. Dynamic Cardiac Magnetic Resonance Measures of Left Atrial Mechanics Across Spectrum of Heart Failure with Preserved Ejection FBaction

Author

Ng, Sher May, primary, Xavier, Roshan, additional, Pan, Jiliu, additional, Miller, Jack, additional, Mózes, Ferenc, additional, Valkovič, Ladislav, additional, Rayner, Jenny, additional, Rigolli, Marzia, additional, Fronheiser, Matthew, additional, Neubauer, Stefan, additional, Rider, Oliver, additional, and Lewis, Andrew, additional

Published
2024
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