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1. 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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2. 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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3. 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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4. 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

5. 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

6. 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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7. 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

12. Misclassification of females and males in cardiovascular magnetic resonance parametric mapping: the importance of sex-specific normal ranges for diagnosis of health vs. disease.

Author

Thomas, Katharine E, Lukaschuk, Elena, Shanmuganathan, Mayooran, Kitt, Jamie A, Popescu, Iulia A, Neubauer, Stefan, Piechnik, Stefan K, and Ferreira, Vanessa M

Subjects
MYOCARDIUM physiology, REFERENCE values, AGE distribution, MAGNETIC resonance imaging, SIMULATION methods in education, SEX distribution, HEART beat, DESCRIPTIVE statistics, RESEARCH funding, DIAGNOSTIC errors
Abstract

Aims Cardiovascular magnetic resonance parametric mapping enables non-invasive quantitative myocardial tissue characterization. Human myocardium has normal ranges of T1 and T2 values, deviation from which may indicate disease or change in physiology. Normal myocardial T1 and T2 values are affected by biological sex. Consequently, normal ranges created with insufficient numbers of each sex may result in sampling biases, misclassification of healthy values vs. disease, and even misdiagnoses. In this study, we investigated the impact of using male normal ranges for classifying female cases as normal or abnormal (and vice versa). Methods and results One hundred and forty-two healthy volunteers (male and female) were scanned on two Siemens 3T MR systems, providing averaged global myocardial T1 and T2 values on a per-subject basis. The Monte Carlo method was used to generate simulated normal ranges from these values to estimate the statistical accuracy of classifying healthy female or male cases correctly as 'normal' when using sex-specific vs. mixed-sex normal ranges. The normal male and female T1- and T2-mapping values were significantly different by sex, after adjusting for age and heart rate. Conclusion Using 15 healthy volunteers who are not sex specific to establish a normal range resulted in a typical misclassification of up to 36% of healthy females and 37% of healthy males as having abnormal T1 values and up to 16% of healthy females and 12% of healthy males as having abnormal T2 values. This paper highlights the potential adverse impact on diagnostic accuracy that can occur when local normal ranges contain insufficient numbers of both sexes. Sex-specific reference ranges should thus be routinely adopted in clinical practice. [ABSTRACT FROM AUTHOR]

Published
2024
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13. Toward Replacing Late Gadolinium Enhancement With Artificial Intelligence Virtual Native Enhancement for Gadolinium-Free Cardiovascular Magnetic Resonance Tissue Characterization in Hypertrophic Cardiomyopathy

Author

Zhang, Qiang, Burrage, Matthew K., Lukaschuk, Elena, Shanmuganathan, Mayooran, Popescu, Iulia A., Nikolaidou, Chrysovalantou, Mills, Rebecca, Werys, Konrad, Hann, Evan, Barutcu, Ahmet, Polat, Suleyman D., Salerno, Michael, Jerosch-Herold, Michael, Kwong, Raymond Y., Watkins, Hugh C., Kramer, Christopher M., Neubauer, Stefan, Ferreira, Vanessa M., and Piechnik, Stefan K.

Published
2021
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14. 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

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

Published
2023
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17. Kiosk 2R-FC-01 - Improving Sex-based Differences in ECG Diagnostic Performance for CMR Abnormalities Post Severe COVID-19 Infections

Author

Samat, Azlan Helmy Abd, Mahmod, Masliza, Lewandowski, Adam, McCracken, Celeste, Cassar, Mark P, Akhtar, Abid M, Ashkir, Zakariye, Moss, Alastair J, Manisty, Charlotte, Treibel, Thomas A, Lukaschuk, Elena, Piechnik, Stefan, Ferreira, Vanessa, Nikolaidou, Chrysovalantou, Miller, Christopher, Chiribiri, Amedeo, O'Regan, Declan, Steeds, Richard P, Weir-McCall, Jonathan R, Plein, Sven, McCann, Gerry, Evans, Rachael A, Brightling, Christopher E., Neubauer, Stefan, and Raman, Betty

Published
2024
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19. Changes in Cardiac Morphology and Function in Individuals With Diabetes Mellitus: The UK Biobank Cardiovascular Magnetic Resonance Substudy

Author

Jensen, Magnus T., Fung, Kenneth, Aung, Nay, Sanghvi, Mihir M., Chadalavada, Sucharitha, Paiva, Jose M., Khanji, Mohammed Y., de Knegt, Martina C., Lukaschuk, Elena, Lee, Aaron M., Barutcu, Ahmet, Maclean, Edd, Carapella, Valentina, Cooper, Jackie, Young, Alistair, Piechnik, Stefan K., Neubauer, Stefan, and Petersen, Steffen E.

Published
2019
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20. Automated quality control in image segmentation: application to the UK Biobank cardiovascular magnetic resonance imaging study

Author

Robinson, Robert, Valindria, Vanya V., Bai, Wenjia, Oktay, Ozan, Kainz, Bernhard, Suzuki, Hideaki, Sanghvi, Mihir M., Aung, Nay, Paiva, José 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

Published
2019
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21. Left ventricular anatomy in obstructive hypertrophic cardiomyopathy: beyond basal septal hypertrophy

Author

Hermida, Uxio, primary, Stojanovski, David, additional, Raman, Betty, additional, Ariga, Rina, additional, Young, Alistair A, additional, Carapella, Valentina, additional, Carr-White, Gerry, additional, Lukaschuk, Elena, additional, Piechnik, Stefan K, additional, Kramer, Christopher M, additional, Desai, Milind Y, additional, Weintraub, William S, additional, Neubauer, Stefan, additional, Watkins, Hugh, additional, and Lamata, Pablo, additional

Published
2022
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22. Left ventricular anatomy in obstructive hypertrophic cardiomyopathy: beyond basal septal hypertrophy.

Author

Hermida, Uxio, Stojanovski, David, Raman, Betty, Ariga, Rina, Young, Alistair A, Carapella, Valentina, Carr-White, Gerry, Lukaschuk, Elena, Piechnik, Stefan K, Kramer, Christopher M, Desai, Milind Y, Weintraub, William S, Neubauer, Stefan, Watkins, Hugh, and Lamata, Pablo

Subjects
LEFT heart ventricle, EVALUATION of medical care, THREE-dimensional imaging, MYOCARDIUM, CARDIAC hypertrophy, LEFT ventricular hypertrophy, MAGNETIC resonance imaging, HUMAN anatomical models, MANN Whitney U Test, T-test (Statistics), VENTRICULAR outflow obstruction, GENOTYPES, RESEARCH funding, DESCRIPTIVE statistics, CHI-squared test, STATISTICAL models, PHENOTYPES, VASCULAR remodeling
Abstract

Aims Obstructive hypertrophic cardiomyopathy (oHCM) is characterized by dynamic obstruction of the left ventricular (LV) outflow tract (LVOT). Although this may be mediated by interplay between the hypertrophied septal wall, systolic anterior motion of the mitral valve, and papillary muscle abnormalities, the mechanistic role of LV shape is still not fully understood. This study sought to identify the LV end-diastolic morphology underpinning oHCM. Methods and results Cardiovascular magnetic resonance images from 2398 HCM individuals were obtained as part of the NHLBI HCM Registry. Three-dimensional LV models were constructed and used, together with a principal component analysis, to build a statistical shape model capturing shape variations. A set of linear discriminant axes were built to define and quantify (Z -scores) the characteristic LV morphology associated with LVOT obstruction (LVOTO) under different physiological conditions and the relationship between LV phenotype and genotype. The LV remodelling pattern in oHCM consisted not only of basal septal hypertrophy but a combination with LV lengthening, apical dilatation, and LVOT inward remodelling. Salient differences were observed between obstructive cases at rest and stress. Genotype negative cases showed a tendency towards more obstructive phenotypes both at rest and stress. Conclusions LV anatomy underpinning oHCM consists of basal septal hypertrophy, apical dilatation, LV lengthening, and LVOT inward remodelling. Differences between oHCM cases at rest and stress, as well as the relationship between LV phenotype and genotype, suggest different mechanisms for LVOTO. Proposed Z -scores render an opportunity of redefining management strategies based on the relationship between LV anatomy and LVOTO. [ABSTRACT FROM AUTHOR]

Published
2023
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23. 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

Published
2018
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25. Artificial Intelligence for Contrast-Free MRI: Scar Assessment in Myocardial Infarction Using Deep Learning-Based Virtual Native Enhancement.

Author

Zhang, Qiang, Burrage, Matthew K., Shanmuganathan, Mayooran, Gonzales, Ricardo A., Lukaschuk, Elena, Thomas, Katharine E., Mills, Rebecca, Leal Pelado, Joana, Nikolaidou, Chrysovalantou, Popescu, Iulia A., Lee, Yung P., Zhang, Xinheng, Dharmakumar, Rohan, Myerson, Saul G., Rider, Oliver, Channon, Keith M., Neubauer, Stefan, Piechnik, Stefan K., Ferreira, Vanessa M., and Oxford Acute Myocardial Infarction (OxAMI) Study

Published
2022
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27. Subclinical Changes in Cardiac Functional Parameters as Determined by Cardiovascular Magnetic Resonance (CMR) Imaging in Sleep Apnea and Snoring: Findings from UK Biobank

Author

Curta, Adrian, primary, Hetterich, Holger, additional, Schinner, Regina, additional, Lee, Aaron M., additional, Sommer, Wieland, additional, Aung, Nay, additional, Sanghvi, Mihir M., additional, Fung, Kenneth, additional, Lukaschuk, Elena, additional, Cooper, Jackie A., additional, Paiva, José Miguel, additional, Carapella, Valentina, additional, Neubauer, Stefan, additional, Piechnik, Stefan K., additional, and Petersen, Steffen E., additional

Published
2021
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28. Sex-specific associations between alcohol consumption, cardiac morphology, and function as assessed by magnetic resonance imaging: insights form the UK Biobank Population Study

Author

Simon, Judit, primary, Fung, Kenneth, additional, Kolossváry, Márton, additional, Sanghvi, Mihir M., additional, Aung, Nay, additional, Paiva, Jose Miguel, additional, Lukaschuk, Elena, additional, Carapella, Valentina, additional, Merkely, Béla, additional, Bittencourt, Marcio S., additional, Karády, Júlia, additional, Lee, Aaron M., additional, Piechnik, Stefan K., additional, Neubauer, Stefan, additional, Maurovich-Horvat, Pál, additional, and Petersen, Steffen E., additional

Published
2020
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33. Improving the Generalizability of Convolutional Neural Network-Based Segmentation on CMR Images

Author

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

Published
2020
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34. Association Between Recreational Cannabis Use and Cardiac Structure and Function

Author

Khanji, Mohammed Y., primary, Jensen, Magnus T., additional, Kenawy, Asmaa A., additional, Raisi-Estabragh, Zahra, additional, Paiva, Jose M., additional, Aung, Nay, additional, Fung, Kenneth, additional, Lukaschuk, Elena, additional, Zemrak, Filip, additional, Lee, Aaron M., additional, Barutcu, Ahmet, additional, Maclean, Edd, additional, Cooper, Jackie, additional, Piechnik, Stefan K., additional, Neubauer, Stefan, additional, and Petersen, Steffen E., additional

Published
2020
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35. Fully Automated Myocardial Strain Estimation from Cardiovascular MRI–tagged Images Using a Deep Learning Framework in the UK Biobank

Author

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

Published
2020
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36. 3.2 First Genome-Wide Association Study of Cardiovascular Magnetic Resonance Derived Aortic Distensibility Reveals 7 Loci

Author

Fung, Kenneth, primary, Biasiolli, Luca, primary, Hann, Evan, primary, Ramirez, Julia, primary, Lukaschuk, Elena, primary, Aung, Nay, primary, Paiva, Jose, primary, Werys, Konrad, primary, Sanghvi, Mihir, primary, Thomson, Ross, primary, Rayner, Jennifer, primary, Puchta, Henrike, primary, Moon, Niall, primary, Thomas, Katharine, primary, Lee, Aaron, primary, Piechnik, Stefan, primary, Neubauer, Stefan, primary, Petersen, Steffen, primary, and Munroe, Patricia, primary

Published
2020
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37. Does self-reported pregnancy loss identify women at risk of an adverse cardiovascular phenotype in later life? Insights from UK Biobank

Author

Elmahi, Einas, primary, Sanghvi, Mihir M., additional, Jones, Alexander, additional, Aye, Christina Y. L., additional, Lewandowski, Adam J., additional, Aung, Nay, additional, Cooper, Jackie A., additional, Paiva, José Miguel, additional, Lukaschuk, Elena, additional, Piechnik, Stefan K., additional, Neubauer, Stefan, additional, Petersen, Steffen E., additional, and Leeson, Paul, additional

Published
2019
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38. Association Between Ambient Air Pollution and Cardiac Morpho-Functional Phenotypes: Insights From the UK Biobank Population Imaging Study

Author

Aung, Nay, Sanghvi, Mihir M., Zemrak, Filip, Lee, Aaron M., Cooper, Jackie A., Paiva, Jose M., Thomson, Ross J., Fung, Kenneth, Khanji, Mohammed Y., Lukaschuk, Elena, Carapella, Valentina, Kim, Young Jin, Munroe, Patricia B., Piechnik, Stefan K., Neubauer, Stefan, and Petersen, Steffen E.

Subjects
Male, Databases, Factual, air pollution, Magnetic Resonance Imaging, Cine, Ventricular Function, Left, Original Research Articles, magnetic resonance imaging, Humans, Aged, Biological Specimen Banks, Air Pollutants, Ventricular Remodeling, phenotypes, Environmental Exposure, Middle Aged, United Kingdom, Cross-Sectional Studies, Phenotype, Cardiovascular Diseases, ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, Female, Nitrogen Oxides, Particulate Matter
Abstract

Supplemental Digital Content is available in the text., Background: Exposure to ambient air pollution is strongly associated with increased cardiovascular morbidity and mortality. Little is known about the influence of air pollutants on cardiac structure and function. We aim to investigate the relationship between chronic past exposure to traffic-related pollutants and the cardiac chamber volume, ejection fraction, and left ventricular remodeling patterns after accounting for potential confounders. Methods: Exposure to ambient air pollutants including particulate matter and nitrogen dioxide was estimated from the Land Use Regression models for the years between 2005 and 2010. Cardiac parameters were measured from cardiovascular magnetic resonance imaging studies of 3920 individuals free from pre-existing cardiovascular disease in the UK Biobank population study. The median (interquartile range) duration between the year of exposure estimate and the imaging visit was 5.2 (0.6) years. We fitted multivariable linear regression models to investigate the relationship between cardiac parameters and traffic-related pollutants after adjusting for various confounders. Results: The studied cohort was 62±7 years old, and 46% were men. In fully adjusted models, particulate matter with an aerodynamic diameter

Published
2019

39. Prospective association between handgrip strength and cardiac structure and function in UK adults

Author

Beyer, Sebastian E, Sanghvi, Mihir M, Aung, Nay, Hosking, Alice, Cooper, Jackie A, Paiva, José Miguel, Lee, Aaron M, Fung, Kenneth, Lukaschuk, Elena, Carapella, Valentina, Mittleman, Murray A, Brage, Soren, Piechnik, Stefan K, Neubauer, Stefan, Petersen, Steffen E, Abete, P, Brage, Soren [0000-0002-1265-7355], and Apollo - University of Cambridge Repository

Subjects
Adult, Male, Endocrine Disorders, Science, Cerebrovascular Diseases, Cardiology, Blood Pressure, Cardiovascular Medicine, Vascular Medicine, Ventricular Function, Left, Endocrinology, Medicine and Health Sciences, Diabetes Mellitus, Humans, Biomechanics, Public and Occupational Health, Prospective Studies, Aged, Hand Strength, Biology and Life Sciences, Stroke Volume, Physical Activity, Middle Aged, Stroke, Neurology, Cardiovascular Diseases, Metabolic Disorders, Hypertension, cardiovascular system, Medicine, Female, human activities, Research Article, Ejection Fraction
Abstract

Background Handgrip strength, a measure of muscular fitness, is associated with cardiovascular (CV) events and CV mortality but its association with cardiac structure and function is unknown. The goal of this study was to determine if handgrip strength is associated with changes in cardiac structure and function in UK adults. Methods and results Left ventricular (LV) ejection fraction (EF), end-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), mass (M), and mass-to-volume ratio (MVR) were measured in a sample of 4,654 participants of the UK Biobank Study 6.3 ± 1 years after baseline using cardiovascular magnetic resonance (CMR). Handgrip strength was measured at baseline and at the imaging follow-up examination. We determined the association between handgrip strength at baseline as well as its change over time and each of the cardiac outcome parameters. After adjustment, higher level of handgrip strength at baseline was associated with higher LVEDV (difference per SD increase in handgrip strength: 1.3ml, 95% CI 0.1±2.4; p = 0.034), higher LVSV (1.0ml, 0.3±1.8; p = 0.006), lower LVM (-1.0g, -1.8 ±-0.3; p = 0.007), and lower LVMVR (-0.013g/ml, -0.018 ±-0.007; p Conclusions Better handgrip strength was associated with cardiac structure and function in a pattern indicative of less cardiac hypertrophy and remodeling. These characteristics are known to be associated with a lower risk of cardiovascular events.

Published
2018

40. Sex-specific associations between alcohol consumption, cardiac morphology, and function as assessed by magnetic resonance imaging: insights form the UK Biobank Population Study.

Author

Simon, Judit, Fung, Kenneth, Kolossváry, Márton, Sanghvi, Mihir M., Aung, Nay, Paiva, Jose Miguel, Lukaschuk, Elena, Carapella, Valentina, Merkely, Béla, Bittencourt, Marcio S., Karády, Júlia, Lee, Aaron M., Piechnik, Stefan K., Neubauer, Stefan, Maurovich-Horvat, Pál, and Petersen, Steffen E.

Subjects
HEART anatomy, HEART radiography, HEART physiology, CONFIDENCE intervals, MULTIVARIATE analysis, MAGNETIC resonance imaging, REGRESSION analysis, ALCOHOL drinking, DESCRIPTIVE statistics, DATA analysis software, LONGITUDINAL method
Abstract

Aims Data regarding the effects of regular alcohol consumption on cardiac anatomy and function are scarce. Therefore, we sought to determine the relationship between regular alcohol intake and cardiac structure and function as evaluated with cardiac magnetic resonance imaging. Methods and results Participants of the UK Biobank who underwent cardiac magnetic resonance were enrolled in our analysis. Data regarding regular alcohol consumption were obtained from questionnaires filled in by the study participants. Exclusion criteria were poor image quality, missing, or incongruent data regarding alcohol drinking habits, prior drinking, presence of heart failure or angina, and prior myocardial infarction or stroke. Overall, 4335 participants (61.5 ± 7.5 years, 47.6% male) were analysed. We used multivariate linear regression models adjusted for age, ethnicity, body mass index, smoking, hypertension, diabetes mellitus, physical activity, cholesterol level, and Townsend deprivation index to examine the relationship between regular alcohol intake and cardiac structure and function. In men, alcohol intake was independently associated with marginally increased left ventricular end-diastolic volume [ β  = 0.14; 95% confidence interval (CI) = 0.05–0.24; P  = 0.004], left ventricular stroke volume (β  = 0.08; 95% CI = 0.03–0.14; P  = 0.005), and right ventricular stroke volume (β  = 0.08; 95% CI = 0.02–0.13; P  = 0.006). In women, alcohol consumption was associated with increased left atrium volume (β  = 0.14; 95% CI = 0.04–0.23; P  = 0.006). Conclusion Alcohol consumption is independently associated with a marginal increase in left and right ventricular volumes in men, but not in women, whereas alcohol intake showed an association with increased left atrium volume in women. Our results suggest that there is only minimal relationship between regular alcohol consumption and cardiac morphology and function in an asymptomatic middle-aged population. [ABSTRACT FROM AUTHOR]

Published
2021
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41. Improving Sex-based Differences in ECG Diagnostic Performance for CMR Abnormalities Post Severe COVID-19 Infections

Author

Samat, Azlan Helmy Abd, Mahmod, Masliza, Lewandowski, Adam, McCracken, Celeste, Cassar, Mark P, Akhtar, Abid M, Ashkir, Zakariye, Moss, Alastair J, Manisty, Charlotte, Treibel, Thomas A, Lukaschuk, Elena, Piechnik, Stefan, Ferreira, Vanessa, Nikolaidou, Chrysovalantou, Miller, Christopher, Chiribiri, Amedeo, O'Regan, Declan, Steeds, Richard P, Weir-McCall, Jonathan R, Plein, Sven, McCann, Gerry, Evans, Rachael A, Brightling, Christopher E., Neubauer, Stefan, and Raman, Betty

Published
2024
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42. Additional file 1 of Automated cardiovascular magnetic resonance image analysis with fully convolutional networks

Author

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

Subjects
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Abstract

Image demonstrating visual assessment and comparison between automated segmentation and manual segmentation. (PDF 210 kb)

Published
2018
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43. Automated localization and quality control of the aorta in cine CMR can significantly accelerate processing of the UK Biobank population data

Author

Biasiolli, Luca, primary, Hann, Evan, additional, Lukaschuk, Elena, additional, Carapella, Valentina, additional, Paiva, Jose M., additional, Aung, Nay, additional, Rayner, Jennifer J., additional, Werys, Konrad, additional, Fung, Kenneth, additional, Puchta, Henrike, additional, Sanghvi, Mihir M., additional, Moon, Niall O., additional, Thomson, Ross J., additional, Thomas, Katharine E., additional, Robson, Matthew D., additional, Grau, Vicente, additional, Petersen, Steffen E., additional, Neubauer, Stefan, additional, and Piechnik, Stefan K., additional

Published
2019
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44. Prevalence of scarred and dysfunctional myocardium in patients with heart failure of ischaemic origin: A cardiovascular magnetic resonance study

Author

Wong Kenneth, Alamgir Mohamed F, Tweddel Ann C, de Silva Ramesh, Sherwi Nassar, Lukaschuk Elena I, Loh Huan P, Nikitin Nikolay P, Bourantas Christos V, Gupta Sanjay, Clark Andrew L, and Cleland John GF

Subjects
Heart failure, Myocardial infarction, Hibernation, Cardiovascular magnetic resonance imaging, Late gadolinium enhancement, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract

Abstract Background Cardiovascular magnetic resonance (CMR) with late gadolinium enhancement (LGE) can provide unique data on the transmural extent of scar/viability. We assessed the prevalence of dysfunctional myocardium, including partial thickness scar, which could contribute to left ventricular contractile dysfunction in patients with heart failure and ischaemic heart disease who denied angina symptoms. Methods We invited patients with ischaemic heart disease and a left ventricular ejection fraction < 50% by echocardiography to have LGE CMR. Myocardial contractility and transmural extent of scar were assessed using a 17-segment model. Results The median age of the 193 patients enrolled was 70 (interquartile range: 63-76) years and 167 (87%) were men. Of 3281 myocardial segments assessed, 1759 (54%) were dysfunctional, of which 581 (33%) showed no scar, 623 (35%) had scar affecting ≤50% of wall thickness and 555 (32%) had scar affecting > 50% of wall thickness. Of 1522 segments with normal contractile function, only 98 (6%) had evidence of scar on CMR. Overall, 182 (94%) patients had ≥1 and 107 (55%) patients had ≥5 segments with contractile dysfunction that had no scar or ≤50% transmural scar suggesting viability. Conclusions In this cohort of patients with left ventricular systolic dysfunction and ischaemic heart disease, about half of all segments had contractile dysfunction but only one third of these had > 50% of the wall thickness affected by scar, suggesting that most dysfunctional segments could improve in response to an appropriate intervention.

Published
2011
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45. Association Between Ambient Air Pollution and Cardiac Morpho-Functional Phenotypes

Author

Aung, Nay, primary, Sanghvi, Mihir M., additional, Zemrak, Filip, additional, Lee, Aaron M., additional, Cooper, Jackie A., additional, Paiva, Jose M., additional, Thomson, Ross J., additional, Fung, Kenneth, additional, Khanji, Mohammed Y., additional, Lukaschuk, Elena, additional, Carapella, Valentina, additional, Kim, Young Jin, additional, Munroe, Patricia B., additional, Piechnik, Stefan K., additional, Neubauer, Stefan, additional, and Petersen, Steffen E., additional

Published
2018
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46. Additional file 1: of Reference ranges for cardiac structure and function using cardiovascular magnetic resonance (CMR) in Caucasians from the UK Biobank population cohort

Author

Petersen, Steffen, Aung, Nay, Mihir Sanghvi, Zemrak, Filip, Fung, Kenneth, Paiva, Jose, Francis, Jane, Khanji, Mohammed, Lukaschuk, Elena, Lee, Aaron, Carapella, Valentina, Kim, Young, Leeson, Paul, Piechnik, Stefan, and Neubauer, Stefan

Abstract

Supplementary materials. (PDF 475 kb)

Published
2017
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47. Variation in lung function and alterations in cardiac structure and function—Analysis of the UK Biobank cardiovascular magnetic resonance imaging substudy

Author

Thomson, Ross J., primary, Aung, Nay, additional, Sanghvi, Mihir M., additional, Paiva, Jose Miguel, additional, Lee, Aaron M., additional, Zemrak, Filip, additional, Fung, Kenneth, additional, Pfeffer, Paul E., additional, Mackay, Alexander J., additional, McKeever, Tricia M., additional, Lukaschuk, Elena, additional, Carapella, Valentina, additional, Kim, Young Jin, additional, Bolton, Charlotte E., additional, Piechnik, Stefan K., additional, Neubauer, Stefan, additional, and Petersen, Steffen E., additional

Published
2018
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48. Prospective association between handgrip strength and cardiac structure and function in UK adults

Author

Beyer, Sebastian E., primary, Sanghvi, Mihir M., additional, Aung, Nay, additional, Hosking, Alice, additional, Cooper, Jackie A., additional, Paiva, José Miguel, additional, Lee, Aaron M., additional, Fung, Kenneth, additional, Lukaschuk, Elena, additional, Carapella, Valentina, additional, Mittleman, Murray A., additional, Brage, Soren, additional, Piechnik, Stefan K., additional, Neubauer, Stefan, additional, and Petersen, Steffen E., additional

Published
2018
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49. The impact of menopausal hormone therapy (MHT) on cardiac structure and function: Insights from the UK Biobank imaging enhancement study

Author

Sanghvi, Mihir M., primary, Aung, Nay, additional, Cooper, Jackie A., additional, Paiva, José Miguel, additional, Lee, Aaron M., additional, Zemrak, Filip, additional, Fung, Kenneth, additional, Thomson, Ross J., additional, Lukaschuk, Elena, additional, Carapella, Valentina, additional, Kim, Young Jin, additional, Harvey, Nicholas C., additional, Piechnik, Stefan K., additional, Neubauer, Stefan, additional, and Petersen, Steffen E., additional

Published
2018
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