223 results on '"Lukaschuk, Elena"'
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2. Clinical Significance of Myocardial Injury in Patients Hospitalized for COVID-19: A Prospective, Multicenter, Cohort Study
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Greenwood, J.P., McCann, G.P., Berry, C., Dweck, M., Miller, C.M., Chiribiri, A., Prasad, S., Ferreira, V.M., Bucciarelli-Ducci, C., Dawson, D., Moon, James C., Artico, Jessica, Shiwani, Hunain, Davies, Rhodri, Dweck, Marc, Berry, Colin, Roditi, Giles, Young, Robin, McConnachie, Alex, Kelly, Bernard, Macfarlane, Peter W., Miller, Christopher A., Levelt, Eylem, Goreka, Miroslawa, Somers, Kathryn, Byrom-Goulthorp, Roo J., Anderson, Michelle, Britton, Laura, Richards, Fiona, Jones, Laura M., Arnold, Ranjit, Moss, Alastair, Fisher, Jude, Wormleighton, Joanne, Parke, Kelly, Wright, Rachel, Yeo, Jian, Dawson, Dana, Falconer, Judith, Harries, Valerie, Henderson, Paula, Singh, Trisha, Newby, David, Piechnik, Stefan, Popescu, Iulia, Lukaschuk, Elena, Zhang, Qiang, Shanmuganathan, Mayooran, Neubauer, Stefan, Raman, Betty, Channon, Keith, Krasopoulos, Catherine, Nunes, Claudia, Da Silva Rodrigues, Liliana, Nixon, Harriet, Panopoulou, Athanasia, Fletcher, Alison, Manley, Peter, Mangion, Kenneth, Morrow, Andrew, 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, Treibel, Thomas, Shetye, Abhishek, Miller, Christopher M., Orsborne, Christopher, Woodville-Jones, William, Ferguson, Susan, Bratis, Konstantinos, Fairbairn, Timothy, Sionas, Michail, Widdows, Peris, Chew, Pei Gee, Marsden, Christian, Collins, Tom, George, Linsha, Kearney, Lisa, Flett, Andrew, Smith, Simon, Zhenge, Alice, Harvey, Jake, Inacio, Liliana, Hanam-Penfold, Tomas, Gruner, Lucy, Fontana, Marianna, 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, Chiribiri, Amedeo, Akerele, Ugochi, Cifra, Elna, Alskaf, Ebraham, Crawley, Richard, Villa, Adriana, Bucciarelli-Ducci, Chiara, 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, Prasad, Sanjay, 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, Manisty, Charlotte H., Hussain, Ifza, Gorecka, Miroslawa, McCann, Gerry P., Alzahir, Mohammed, Ramirez, Sara, Lin, Andrew, Swoboda, Peter P., McDiarmid, Adam K., Manisty, Charlotte, Treibel, Thomas A., Piechnik, Stefan K., Davies, Rhodri H., Ferreira, Vanessa M., Dweck, Marc R., and Greenwood, John P.
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- 2024
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3. Incidence of diabetes after SARS-CoV-2 infection in England and the implications of COVID-19 vaccination: a retrospective cohort study of 16 million people
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Al Arab, Marwa, Almaghrabi, Fatima, Andrews, Colm, Badrick, Ellena, Baz, Sarah, Beckford, Chelsea, Berman, Samantha, Bolton, Tom, Booth, Charlotte, Bowyer, Ruth, Boyd, Andy, Bridger-Staatz, Charis, Brophy, Sinead, Campbell, Archie, Campbell, Kirsteen C, Carnemolla, Alisia, Carpentieri, Jd, Cezard, Genevieve, Chaturvedi, Nishi, Cheetham, Nathan, Costello, Ruth, Cowling, Thomas, Crane, Matthew, Cuitun Coronado, Jose Ignacio, Curtis, Helen, Denaxas, Spiros, Denholm, Rachel, Di Gessa, Giorgio, Dobson, Richard, Douglas, Ian, Evans, Katharine M, Fang, Chao, Ferreira, Vanessa, Finnigan, Lucy, Fisher, Louis, Flaig, Robin, Folarin, Amos, Forbes, Harriet, Foster, Diane, Fox, Laura, Freydin, Maxim, Garcia, Paz, Gibson, Andy, Glen, Fiona, Goldacre, Ben, Goncalves Soares, Ana, Greaves, Felix, Green, Amelia, Green, Mark, Green, Michael, Griffith, Gareth, Hamill Howes, Lee, Hamilton, Olivia, Herbet, Annie, Herrett, Emily, Hopcroft, Lisa, Horne, Elsie, Hou, Bo, Hughes, Alun, Hulme, William, Huntley, Lizzie, Ip, Samantha, Jacques, Wels, Jezzard, Peter, Jones, Louise, Kanagaratnam, Arun, Karthikeyan Suseeladevi, Arun, Katikireddi, Vittal, Kellas, John, Kennedy, Jonathan I, Kibble, Milla, Knight, Rochelle, Knueppel, Anika, Kopasker, Daniel, Kromydas, Theocharis, Kwong, Alex, Langan, Sinead, Lemanska, Agnieszka, Lukaschuk, Elena, Mackenna, Brain, Macleod, John, Maddock, Jane, Mahalingasivam, Viyaasan, Mansfield, Kathryn, McArdle, Fintan, McCartney, Daniel, McEachan, Rosie, McElroy, Eoin, McLachlan, Stela, Mitchell, Ruth, Moltrecht, Bettina, Morley, Jess, Nab, Linda, Neubauer, Stefan, Nigrelli, Lidia, North, Teri, Northstone, Kate, Oakley, Jacqui, Palmer, Tom, Park, Chloe, Parker, Michael, Parsons, Sam, Patalay, Praveetha, Patel, Kishan, Perez-Reche, Francisco, Piechnik, Stefan, Piehlmaier, Dominik, Ploubidis, George, Rafeti, Elena, Raman, Betty, Ranjan, Yatharth, Rapala, Alicja, Rhead, Rebecca, Roberts, Amy, Sampri, Alexia, Sanders, Zeena-Britt, Santorelli, Gillian, Saunders, Laura C, Shah, Anoop, Shah, Syed Ahmar, Sharp, Steve, Shaw, Richard, Sheard, Laura, Sheikh, Aziz, Silverwood, Richard, Smeeth, Liam, Smith, Stephen, Stafford, Jean, Steptoe, Andrew, Sterne, Jonathan, Steves, Claire, Stewart, Callum, Taylor, Kurt, Tazare, John, Teece, Lucy, Thomas, Richard, Thompson, Ellen, Tilling, Kate, Timpson, Nicholas, Tomlinson, Laurie, Toms, Renin, Tunnicliffe, Elizabeth, Turner, Emma L, Walker, Alex, Walker, Venexia, Walter, Scott, Wang, Kevin, Wei, Yinghui, Whitehorn, Rebecca, Wielgoszewska, Bozena, Wild, James M, Willan, Kathryn, Willans, Robert, Williams, Dylan, Wong, Andrew, Wood, Angela, Woodward, Hannah, Wright, John, Yang, Tiffany, Zaninotto, Paola, Zheng, Bang, Zhu, Jingmin, Eastwood, Sophie, Horne, Elsie M F, Massey, Jon, Hopcroft, Lisa E M, Cuitun Coronado, Jose, Davy, Simon, Dillingham, Iain, Morton, Caroline, and Sterne, Jonathan A C
- Published
- 2024
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4. Fully Automated Myocardial Strain Estimation from CMR Tagged Images using a Deep Learning Framework in the UK Biobank
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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
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- 2020
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5. Cardiac Remodeling After Hypertensive Pregnancy Following Physician-Optimized Blood Pressure Self-Management: The POP-HT Randomized Clinical Trial Imaging Substudy
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Kitt, Jamie, Krasner, Samuel, Barr, Logan, Frost, Annabelle, Tucker, Katherine, Bateman, Paul A., Suriano, Katie, Kenworthy, Yvonne, Lapidaire, Winok, Lacharie, Miriam, Mills, Rebecca, Roman, Cristian, Mackillop, Lucy, Cairns, Alexandra, Aye, Christina, Ferreira, Vanessa, Piechnik, Stefan, Lukaschuk, Elena, Thilaganathan, Basky, Chappell, Lucy C., Lewandowski, Adam J., McManus, Richard J., and Leeson, Paul
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- 2024
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6. Multiorgan MRI findings after hospitalisation with COVID-19 in the UK (C-MORE): a prospective, multicentre, observational cohort study
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Raman, Betty, McCracken, Celeste, Cassar, Mark P, Moss, Alastair J, Finnigan, Lucy, Samat, Azlan Helmy A, Ogbole, Godwin, Tunnicliffe, Elizabeth M, Alfaro-Almagro, Fidel, Menke, Ricarda, Xie, Cheng, Gleeson, Fergus, Lukaschuk, Elena, Lamlum, Hanan, McGlynn, Kevin, Popescu, Iulia A, Sanders, Zeena-Britt, Saunders, Laura C, Piechnik, Stefan K, Ferreira, Vanessa M, Nikolaidou, Chrysovalantou, Rahman, Najib M, Ho, Ling-Pei, Harris, Victoria C, Shikotra, Aarti, Singapuri, Amisha, Pfeffer, Paul, Manisty, Charlotte, Kon, Onn M, Beggs, Mark, O'Regan, Declan P, Fuld, Jonathan, Weir-McCall, Jonathan R, Parekh, Dhruv, Steeds, Rick, Poinasamy, Krisnah, Cuthbertson, Dan J, Kemp, Graham J, Semple, Malcolm G, Horsley, Alexander, Miller, Christopher A, O'Brien, Caitlin, Shah, Ajay M, Chiribiri, Amedeo, Leavy, Olivia C, Richardson, Matthew, Elneima, Omer, McAuley, Hamish J C, Sereno, Marco, Saunders, Ruth M, Houchen-Wolloff, Linzy, Greening, Neil J, Bolton, Charlotte E, Brown, Jeremy S, Choudhury, Gourab, Diar Bakerly, Nawar, Easom, Nicholas, Echevarria, Carlos, Marks, Michael, Hurst, John R, Jones, Mark G, Wootton, Daniel G, Chalder, Trudie, Davies, Melanie J, De Soyza, Anthony, Geddes, John R, Greenhalf, William, Howard, Luke S, Jacob, Joseph, Man, William D-C, Openshaw, Peter J M, Porter, Joanna C, Rowland, Matthew J, Scott, Janet T, Singh, Sally J, Thomas, David C, Toshner, Mark, Lewis, Keir E, Heaney, Liam G, Harrison, Ewen M, Kerr, Steven, Docherty, Annemarie B, Lone, Nazir I, Quint, Jennifer, Sheikh, Aziz, Zheng, Bang, Jenkins, R Gisli, Cox, Eleanor, Francis, Susan, Halling-Brown, Mark, Chalmers, James D, Greenwood, John P, Plein, Sven, Hughes, Paul J C, Thompson, A A Roger, Rowland-Jones, Sarah L, Wild, James M, Kelly, Matthew, Treibel, Thomas A, Bandula, Steven, Aul, Raminder, Miller, Karla, Jezzard, Peter, Smith, Stephen, Nichols, Thomas E, McCann, Gerry P, Evans, Rachael A, Wain, Louise V, Brightling, Christopher E, Neubauer, Stefan, Baillie, J K, Shaw, Alison, Hairsine, Brigid, Kurasz, Claire, Henson, Helen, Armstrong, Lisa, Shenton, Liz, Dobson, H, Dell, Amanda, Lucey, Alice, Price, Andrea, Storrie, Andrew, Pennington, Chris, Price, Claire, Mallison, Georgia, Willis, Gemma, Nassa, Heeah, Haworth, Jill, Hoare, Michaela, Hawkings, Nancy, Fairbairn, Sara, Young, Susan, Walker, S, Jarrold, I, Sanderson, Amy, David, C, Chong-James, K, Zongo, O, James, W Y, Martineau, A, King, Bernie, Armour, C, McAulay, D, Major, E, McGinness, Jade, McGarvey, L, Magee, N, Stone, Roisin, Drain, S, Craig, T, Bolger, A, Haggar, Ahmed, Lloyd, Arwel, Subbe, Christian, Menzies, Daniel, Southern, David, McIvor, Emma, Roberts, K, Manley, R, Whitehead, Victoria, Saxon, W, Bularga, A, Mills, N L, El-Taweel, Hosni, Dawson, Joy, Robinson, Leanne, Saralaya, Dinesh, Regan, Karen, Storton, Kim, Brear, Lucy, Amoils, S, Bermperi, Areti, Elmer, Anne, Ribeiro, Carla, Cruz, Isabel, Taylor, Jessica, Worsley, J, Dempsey, K, Watson, L, Jose, Sherly, Marciniak, S, Parkes, M, McQueen, Alison, Oliver, Catherine, Williams, Jenny, Paradowski, Kerry, Broad, Lauren, Knibbs, Lucy, Haynes, Matthew, Sabit, Ramsey, Milligan, L, Sampson, Claire, Hancock, Alyson, Evenden, Cerys, Lynch, Ceri, Hancock, Kia, Roche, Lisa, Rees, Meryl, Stroud, Natalie, Thomas-Woods, T, Heller, S, Robertson, E, Young, B, Wassall, Helen, Babores, M, Holland, Maureen, Keenan, Natalie, Shashaa, Sharlene, Price, Carly, Beranova, Eva, Ramos, Hazel, Weston, Heather, Deery, Joanne, Austin, Liam, Solly, Reanne, Turney, Sharon, Cosier, Tracey, Hazelton, Tracy, Ralser, M, Wilson, Ann, Pearce, Lorraine, Pugmire, S, Stoker, Wendy, McCormick, W, Dewar, A, Arbane, Gill, Kaltsakas, G, Kerslake, Helen, Rossdale, J, Bisnauthsing, Karen, Aguilar Jimenez, Laura A, Martinez, L M, Ostermann, Marlies, Magtoto, Murphy M, Hart, Nicholas, Marino, Philip, Betts, Sarah, Solano, Teresa S, Arias, Ava Maria, Prabhu, A, Reed, Annabel, Wrey Brown, Caroline, Griffin, Denise, Bevan, Emily, Martin, Jane, Owen, J, Alvarez Corral, Maria, Williams, Nick, Payne, Sheila, Storrar, Will, Layton, Alison, Lawson, Cathy, Mills, Clare, Featherstone, James, Stephenson, Lorraine, Burdett, Tracy, Ellis, Y, Richards, A, Wright, C, Sykes, D L, Brindle, K, Drury, Katie, Holdsworth, L, Crooks, M G, Atkin, Paul, Flockton, Rachel, Thackray-Nocera, Susannah, Mohamed, Abdelrahman, Taylor, Abigail, Perkins, Emma, Ross, Gavin, McGuinness, Heather, Tench, Helen, Phipps, Janet, Loosley, Ronda, Wolf-Roberts, Rebecca, Coetzee, S, Omar, Zohra, Ross, Alexandra, Card, Bethany, Carr, Caitlin, King, Clara, Wood, Chloe, Copeland, D, Calvelo, Ellen, Chilvers, Edwin R, Russell, Emily, Gordon, Hussain, Nunag, Jose Lloyd, Schronce, J, March, Katherine, Samuel, Katherine, Burden, L, Evison, Lynsey, McLeavey, Laura, Orriss-Dib, Lorna, Tarusan, Lawrence, Mariveles, Myril, Roy, Maura, Mohamed, Noura, Simpson, Neil, Yasmin, Najira, Cullinan, P, Daly, Patrick, Haq, Sulaimaan, Moriera, Silvia, Fayzan, Tamanah, Munawar, Unber, Nwanguma, Uchechi, Lingford-Hughes, A, Altmann, Danny, Johnston, D, Mitchell, J, Valabhji, J, Price, L, Molyneaux, P L, Thwaites, Ryan S, Walsh, S, Frankel, A, Lightstone, L, Wilkins, M, Willicombe, M, McAdoo, S, Touyz, R, Guerdette, Anne-Marie, Warwick, Katie, Hewitt, Melanie, Reddy, R, White, Sonia, McMahon, A, Hoare, Amy, Knighton, Abigail, Ramos, Albert, Te, Amelie, Jolley, Caroline J, Speranza, Fabio, Assefa-Kebede, Hosanna, Peralta, Ida, Breeze, Jonathon, Shevket, K, Powell, Natassia, Adeyemi, Oluwaseun, Dulawan, Pearl, Adrego, Rita, Byrne, S, Patale, Sheetal, Hayday, A, Malim, M, Pariante, C, Sharpe, C, Whitney, J, Bramham, K, Ismail, K, Wessely, S, Nicholson, T, Ashworth, Andrew, Humphries, Amy, Tan, Ai Lyn, Whittam, Beverley, Coupland, C, Favager, Clair, Peckham, D, Wade, Elaine, Saalmink, Gwen, Clarke, Jude, Glossop, Jodie, Murira, Jennifer, Rangeley, Jade, Woods, Janet, Hall, Lucy, Dalton, Matthhew, Window, Nicola, Beirne, Paul, Hardy, Tim, Coakley, G, Turtle, Lance, Berridge, Anthony, Cross, Andy, Key, Angela L, Rowe, Anna, Allt, Ann Marie, Mears, Chloe, Malein, Flora, Madzamba, Gladys, Hardwick, H E, Earley, Joanne, Hawkes, Jenny, Pratt, James, Wyles, J, Tripp, K A, Hainey, Kera, Allerton, Lisa, Lavelle-Langham, L, Melling, Lucy, Wajero, Lilian O, Poll, L, Noonan, Matthew J, French, N, Lewis-Burke, N, Williams-Howard, S A, Cooper, Shirley, Kaprowska, Sabina, Dobson, S L, Marsh, Sophie, Highett, Victoria, Shaw, V, Beadsworth, M, Defres, S, Watson, Ekaterina, Tiongson, Gerlynn F, Papineni, Padmasayee, Gurram, Sambasivarao, Diwanji, Shalin N, Quaid, Sheena, Briggs, A, Hastie, Claire, Rogers, Natalie, Stensel, D, Bishop, L, McIvor, K, Rivera-Ortega, P, Al-Sheklly, B, Avram, Cristina, Faluyi, David, Blaikely, J, Piper Hanley, K, Radhakrishnan, K, Buch, M, Hanley, N A, Odell, Natasha, Osbourne, Rebecca, Stockdale, Sue, Felton, T, Gorsuch, T, Hussell, T, Kausar, Zunaira, Kabir, T, McAllister-Williams, H, Paddick, S, Burn, D, Ayoub, A, Greenhalgh, Alan, Sayer, A, Young, A, Price, D, Burns, G, MacGowan, G, Fisher, Helen, Tedd, H, Simpson, J, Jiwa, Kasim, Witham, M, Hogarth, Philip, West, Sophie, Wright, S, McMahon, Michael J, Neill, Paula, Dougherty, Andrew, Morrow, A, Anderson, David, Grieve, D, Bayes, Hannah, Fallon, K, Mangion, K, Gilmour, L, Basu, N, Sykes, R, Berry, C, McInnes, I B, Donaldson, A, Sage, E K, Barrett, Fiona, Welsh, B, Bell, Murdina, Quigley, Jackie, Leitch, Karen, Macliver, L, Patel, Manish, Hamil, R, Deans, Andrew, Furniss, J, Clohisey, S, Elliott, Anne, Solstice, A R, Deas, C, Tee, Caroline, Connell, David, Sutherland, Debbie, George, J, Mohammed, S, Bunker, Jenny, Holmes, Katie, Dipper, A, Morley, Anna, Arnold, David, Adamali, H, Welch, H, Morrison, Leigh, Stadon, Louise, Maskell, Nick, Barratt, Shaney, Dunn, Sarah, Waterson, Samuel, Jayaraman, Bhagy, Light, Tessa, Selby, N, Hosseini, A, Shaw, Karen, Almeida, Paula, Needham, Robert, Thomas, Andrew K, Matthews, Laura, Gupta, Ayushman, Nikolaidis, Athanasios, Dupont, Catherine, Bonnington, J, Chrystal, Melanie, Greenhaff, P L, Linford, S, Prosper, Sabrina, Jang, W, Alamoudi, Asma, Bloss, Angela, Megson, Clare, Nicoll, Debby, Fraser, Emily, Pacpaco, Edmund, Conneh, Florence, Ogg, G, McShane, H, Koychev, Ivan, Chen, Jin, Pimm, John, Ainsworth, Mark, Pavlides, M, Sharpe, M, Havinden-Williams, May, Petousi, Nayia, Talbot, Nick, Carter, Penny, Kurupati, Prathiba, Dong, T, Peng, Yanchun, Burns, A, Kanellakis, N, Korszun, A, Connolly, B, Busby, J, Peto, T, Patel, B, Nolan, C M, Cristiano, Daniele, Walsh, J A, Liyanage, Kamal, Gummadi, Mahitha, Dormand, N, Polgar, Oliver, George, P, Barker, R E, Patel, Suhani, Gibbons, M, Matila, Darwin, Jarvis, Hannah, Lim, Lai, Olaosebikan, Olaoluwa, Ahmad, Shanaz, Brill, Simon, Mandal, S, Laing, C, Michael, Alice, Reddy, A, Johnson, C, Baxendale, H, Parfrey, H, Mackie, J, Newman, J, Pack, Jamie, Parmar, J, Paques, K, Garner, Lucie, Harvey, Alice, Summersgill, C, Holgate, D, Hardy, E, Oxton, J, Pendlebury, Jessica, McMorrow, L, Mairs, N, Majeed, N, Dark, P, Ugwuoke, R, Knight, Sean, Whittaker, S, Strong-Sheldrake, Sophia, Matimba-Mupaya, Wadzanai, Chowienczyk, P, Pattenadk, Dibya, Hurditch, E, Chan, Flora, Carborn, H, Foot, H, Bagshaw, J, Hockridge, J, Sidebottom, J, Lee, Ju Hee, Birchall, K, Turner, Kim, Haslam, L, Holt, L, Milner, L, Begum, M, Marshall, M, Steele, N, Tinker, N, Ravencroft, Phillip, Butcher, Robyn, Misra, S, Coburn, Zach, Fairman, Alexandra, Ford, Amber, Holbourn, Ailsa, Howell, Alice, Lawrie, Allan, Lye, Alison, Mbuyisa, Angeline, Zawia, Amira, Holroyd-Hind, B, Thamu, B, Clark, Cameron, Jarman, Claire, Norman, C, Roddis, C, Foote, David, Lee, Elvina, Ilyas, F, Stephens, G, Newell, Helen, Turton, Helena, Macharia, Irene, Wilson, Imogen, Cole, Joby, McNeill, J, Meiring, J, Rodger, J, Watson, James, Chapman, Kerry, Harrington, Kate, Chetham, Luke, Hesselden, L, Nwafor, Lorenza, Dixon, Myles, Plowright, Megan, Wade, Phillip, Gregory, Rebecca, Lenagh, Rebecca, Stimpson, R, Megson, Sharon, Newman, Tom, Cheng, Yutung, Goodwin, Camelia, Heeley, Cheryl, Sissons, D, Sowter, D, Gregory, Heidi, Wynter, Inez, Hutchinson, John, Kirk, Jill, Bennett, Kaytie, Slack, Katie, Allsop, Lynne, Holloway, Leah, Flynn, Margaret, Gill, Mandy, Greatorex, M, Holmes, Megan, Buckley, Phil, Shelton, Sarah, Turner, Sarah, Sewell, Terri Ann, Whitworth, V, Lovegrove, Wayne, Tomlinson, Johanne, Warburton, Louise, Painter, Sharon, Vickers, Carinna, Redwood, Dawn, Tilley, Jo, Palmer, Sue, Wainwright, Tania, Breen, G, Hotopf, M, Dunleavy, A, Teixeira, J, Ali, Mariam, Mencias, Mark, Msimanga, N, Siddique, Sulman, Samakomva, T, Tavoukjian, Vera, Forton, D, Ahmed, R, Cook, Amanda, Thaivalappil, Favas, Connor, Lynda, Rees, Tabitha, McNarry, M, Williams, N, McCormick, Jacqueline, McIntosh, Jerome, Vere, Joanne, Coulding, Martina, Kilroy, Susan, Turner, Victoria, Butt, Al-Tahoor, Savill, Heather, Fraile, Eva, Ugoji, Jacinta, Landers, G, Lota, Harpreet, Portukhay, Sofiya, Nasseri, Mariam, Daniels, Alison, Hormis, Anil, Ingham, Julie, Zeidan, Lisa, Osborne, Lynn, Chablani, Manish, Banerjee, A, David, A, Pakzad, A, Rangelov, B, Williams, B, Denneny, E, Willoughby, J, Xu, M, Mehta, P, Batterham, R, Bell, R, Aslani, S, Lilaonitkul, W, Checkley, A, Bang, Dongchun, Basire, Donna, Lomas, D, Wall, E, Plant, Hannah, Roy, K, Heightman, M, Lipman, M, Merida Morillas, Marta, Ahwireng, Nyarko, Chambers, R C, Jastrub, Roman, Logan, S, Hillman, T, Botkai, A, Casey, A, Neal, A, Newton-Cox, A, Cooper, B, Atkin, C, McGee, C, Welch, C, Wilson, D, Sapey, E, Qureshi, H, Hazeldine, J, Lord, J M, Nyaboko, J, Short, J, Stockley, J, Dasgin, J, Draxlbauer, K, Isaacs, K, Mcgee, K, Yip, K P, Ratcliffe, L, Bates, M, Ventura, M, Ahmad Haider, N, Gautam, N, Baggott, R, Holden, S, Madathil, S, Walder, S, Yasmin, S, Hiwot, T, Jackson, T, Soulsby, T, Kamwa, V, Peterkin, Z, Suleiman, Z, Chaudhuri, N, Wheeler, H, Djukanovic, R, Samuel, R, Sass, T, Wallis, T, Marshall, B, Childs, C, Marouzet, E, Harvey, M, Fletcher, S, Dickens, C, Beckett, P, Nanda, U, Daynes, E, Charalambou, A, Yousuf, A J, Lea, A, Prickett, A, Gooptu, Bibek, Hargadon, Beverley, Bourne, Charlotte, Christie, C, Edwardson, C, Lee, D, Baldry, E, Stringer, E, Woodhead, F, Mills, G, Arnold, H, Aung, H, Qureshi, I N, Finch, J, Skeemer, J, Hadley, K, Khunti, Kamlesh, Carr, Liesel, Ingram, L, Aljaroof, M, Bakali, M, Bakau, M, Baldwin, M, Bourne, Michelle, Pareek, Manish, Soares, M, Tobin, Martin, Armstrong, Natalie, Brunskill, Nigel, Goodman, N, Cairns, P, Haldar, Pranab, McCourt, P, Dowling, R, Russell, Richard, Diver, Sarah, Edwards, Sarah, Glover, Sarah, Parker, S, Siddiqui, Salman, Ward, T J C, Mcnally, T, Thornton, T, Yates, Tom, Ibrahim, W, Monteiro, Will, Thickett, D, Wilkinson, D, Broome, M, McArdle, P, Upthegrove, R, Wraith, D, Langenberg, C, Summers, C, Bullmore, E, Heeney, J L, Schwaeble, W, Sudlow, C L, Adeloye, D, Newby, D E, Rudan, I, Shankar-Hari, M, Thorpe, M, Pius, R, Walmsley, S, McGovern, A, Ballard, C, Allan, L, Dennis, J, Cavanagh, J, Petrie, J, O'Donnell, K, Spears, M, Sattar, N, MacDonald, S, Guthrie, E, Henderson, M, Guillen Guio, Beatriz, Zhao, Bang, Lawson, C, Overton, Charlotte, Taylor, Chris, Tong, C, Mukaetova-Ladinska, Elizabeta, Turner, E, Pearl, John E, Sargant, J, Wormleighton, J, Bingham, Michelle, Sharma, M, Steiner, Mike, Samani, Nilesh, Novotny, Petr, Free, Rob, Allen, R J, Finney, Selina, Terry, Sarah, Brugha, Terry, Plekhanova, Tatiana, McArdle, A, Vinson, B, Spencer, L G, Reynolds, W, Ashworth, M, Deakin, B, Chinoy, H, Abel, K, Harvie, M, Stanel, S, Rostron, A, Coleman, C, Baguley, D, Hufton, E, Khan, F, Hall, I, Stewart, I, Fabbri, L, Wright, L, Kitterick, P, Morriss, R, Johnson, S, Bates, A, Antoniades, C, Clark, D, Bhui, K, Channon, K M, Motohashi, K, Sigfrid, L, Husain, M, Webster, M, Fu, X, Li, X, Kingham, L, Klenerman, P, Miiler, K, Carson, G, Simons, G, Huneke, N, Calder, P C, Baldwin, D, Bain, S, Lasserson, D, Daines, L, Bright, E, Stern, M, Crisp, P, Dharmagunawardena, R, Reddington, A, Wight, A, Bailey, L, Ashish, A, Robinson, E, Cooper, J, Broadley, A, Turnbull, A, Brookes, C, Sarginson, C, Ionita, D, Redfearn, H, Elliott, K, Barman, L, Griffiths, L, Guy, Z, Gill, Rhyan, Nathu, Rashmita, Harris, Edward, Moss, P, Finnigan, J, Saunders, Kathryn, Saunders, Peter, Kon, S, Kon, Samantha S, O'Brien, Linda, Shah, K, Shah, P, Richardson, Emma, Brown, V, Brown, M, Brown, Jo, Brown, J, Brown, Ammani, Brown, Angela, Choudhury, N, Jones, S, Jones, H, Jones, L, Jones, I, Jones, G, Jones, Heather, Jones, Don, Davies, Ffyon, Davies, Ellie, Davies, Kim, Davies, Gareth, Davies, Gwyneth A, Howard, K, Porter, Julie, Rowland, J, Rowland, A, Scott, Kathryn, Singh, Suver, Singh, Claire, Thomas, S, Thomas, Caradog, Lewis, Victoria, Lewis, J, Lewis, D, Harrison, P, Francis, C, Francis, R, Hughes, Rachel Ann, Hughes, Joan, Hughes, A D, Thompson, T, Kelly, S, Smith, D, Smith, Nikki, Smith, Andrew, Smith, Jacqui, Smith, Laurie, Smith, Susan, Evans, Teriann, Evans, Ranuromanana I, Evans, D, Evans, R, Evans, H, and Evans, J
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- 2023
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7. Improving the generalizability of convolutional neural network-based segmentation on CMR images
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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
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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
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- 2019
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8. 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
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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.
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- 2023
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9. Automated Quality Control in Image Segmentation: Application to the UK Biobank Cardiac MR Imaging Study
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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
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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
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- 2019
10. Abstract 14282: Electrocardiography Rules out Myocardial Abnormalities on Cardiac Magnetic Resonance Imaging in Post-Hospitalized COVID-19 Patients: A Multicenter Observational Study
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Abd Samat, Azlan Helmy, Mahmod, Masliza, Lewandowski, Adam J, Cassar, Mark Philip, Akhtar, Mohammed, Moss, Alastair, Manisty, Charlotte, Treibel, Thomas, Ashkir, Zakariye, McCracken, Celeste, Lukaschuk, Elena, Piechnik, Stefan, Ferreira, Vanessa, Xie, Cheng, Cuthbertson, Dan, Kemp, Graham, Nikolaidou, Chrysovalantou, Miller, Christopher, Chiribiri, Amedeo, OʼRegan, Declan, Francis, Susan, Steeds, Richard P, Weir-McCall, Jonathan, Wild, Jim M, Plein, Sven, McCann, Gerry, Evans, Rachael, brightling, chris, Neubauer, Stefan, and Raman, Betty
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- 2023
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11. Real-time Prediction of Segmentation Quality
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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
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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
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- 2018
12. Myocardial Involvement After Hospitalization for COVID-19 Complicated by Troponin Elevation: A Prospective, Multicenter, Observational Study
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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
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- 2023
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13. Automated cardiovascular magnetic resonance image analysis with fully convolutional networks
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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
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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
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- 2017
14. Clinical Significance of Myocardial Injury in Patients Hospitalized for COVID-19: A Prospective, Multicenter, Cohort Study.
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Shiwani, Hunain, Artico, Jessica, Moon, James C., Gorecka, Miroslawa, McCann, Gerry P., Roditi, Giles, Morrow, Andrew, Mangion, Kenneth, Lukaschuk, Elena, Shanmuganathan, Mayooran, Miller, Christopher A., Chiribiri, Amedeo, Alzahir, Mohammed, Ramirez, Sara, Lin, Andrew, Swoboda, Peter P., McDiarmid, Adam K., Sykes, Robert, Singh, Trisha, and Bucciarelli-Ducci, Chiara
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Hospitalized COVID-19 patients with troponin elevation have a higher prevalence of cardiac abnormalities than control individuals. However, the progression and impact of myocardial injury on COVID-19 survivors remain unclear. This study sought to evaluate myocardial injury in COVID-19 survivors with troponin elevation with baseline and follow-up imaging and to assess medium-term outcomes. This was a prospective, longitudinal cohort study in 25 United Kingdom centers (June 2020 to March 2021). Hospitalized COVID-19 patients with myocardial injury underwent cardiac magnetic resonance (CMR) scans within 28 days and 6 months postdischarge. Outcomes were tracked for 12 months, with quality of life surveys (EuroQol-5 Dimension and 36-Item Short Form surveys) taken at discharge and 6 months. Of 342 participants (median age: 61.3 years; 71.1% male) with baseline CMR, 338 had a 12-month follow-up, 235 had a 6-month CMR, and 215 has baseline and follow-up quality of life surveys. Of 338 participants, within 12 months, 1.2% died; 1.8% had new myocardial infarction, acute coronary syndrome, or coronary revascularization; 0.8% had new myopericarditis; and 3.3% had other cardiovascular events requiring hospitalization. At 6 months, there was a minor improvement in left ventricular ejection fraction (1.8% ± 1.0%; P < 0.001), stable right ventricular ejection fraction (0.4% ± 0.8%; P = 0.50), no change in myocardial scar pattern or volume (P = 0.26), and no imaging evidence of continued myocardial inflammation. All pericardial effusions (26 of 26) resolved, and most pneumonitis resolved (95 of 101). EuroQol-5 Dimension scores indicated an overall improvement in quality of life (P < 0.001). Myocardial injury in severe hospitalized COVID-19 survivors is nonprogressive. Medium-term outcomes show a low incidence of major adverse cardiovascular events and improved quality of life. (COVID-19 Effects on the Heart; ISRCTN58667920) [Display omitted] [ABSTRACT FROM AUTHOR]
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- 2024
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15. The Role of Coronary Blood Flow and Myocardial Edema in the Pathophysiology of Takotsubo Syndrome
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Couch, Liam S., primary, Thomas, Katharine E., additional, Marin, Federico, additional, Terentes-Printzios, Dimitrios, additional, Kotronias, Rafail A., additional, Chai, Jason, additional, Lukaschuk, Elena, additional, Shanmuganathan, Mayooran, additional, Kellman, Peter, additional, Langrish, Jeremy P., additional, Channon, Keith M., additional, Neubauer, Stefan, additional, Piechnik, Stefan K., additional, Ferreira, Vanessa M., additional, de Maria, Giovanni Luigi, additional, and Banning, Adrian P., additional
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- 2024
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16. Standardization of T1-mapping in cardiovascular magnetic resonance using clustered structuring for benchmarking normal ranges
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Popescu, Iulia A., Werys, Konrad, Zhang, Qiang, Puchta, Henrike, Hann, Evan, Lukaschuk, Elena, Ferreira, Vanessa M., and Piechnik, Stefan K.
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- 2021
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17. Quality Control-Driven Image Segmentation Towards Reliable Automatic Image Analysis in Large-Scale Cardiovascular Magnetic Resonance Aortic Cine Imaging
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Hann, Evan, Biasiolli, Luca, Zhang, Qiang, Popescu, Iulia A., Werys, Konrad, Lukaschuk, Elena, Carapella, Valentina, Paiva, Jose M., Aung, Nay, Rayner, Jennifer J., Fung, Kenneth, Puchta, Henrike, Sanghvi, Mihir M., Moon, Niall O., Thomas, Katharine E., Ferreira, Vanessa M., Petersen, Steffen E., Neubauer, Stefan, Piechnik, Stefan K., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Shen, Dinggang, editor, Liu, Tianming, editor, Peters, Terry M., editor, Staib, Lawrence H., editor, Essert, Caroline, editor, Zhou, Sean, editor, Yap, Pew-Thian, editor, and Khan, Ali, editor
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- 2019
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18. Deep learning with attention supervision for automated motion artefact detection in quality control of cardiac T1-mapping
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Zhang, Qiang, Hann, Evan, Werys, Konrad, Wu, Cody, Popescu, Iulia, Lukaschuk, Elena, Barutcu, Ahmet, Ferreira, Vanessa M., and Piechnik, Stefan K.
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- 2020
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19. Standardized image post-processing of cardiovascular magnetic resonance T1-mapping reduces variability and improves accuracy and consistency in myocardial tissue characterization
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Carapella, Valentina, Puchta, Henrike, Lukaschuk, Elena, Marini, Claudia, Werys, Konrad, Neubauer, Stefan, Ferreira, Vanessa M., and Piechnik, Stefan K.
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- 2020
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20. Automated CMR Index of Left Ventricular Diastolic Function (e’): A Validation Study Against Echocardiography in the Large-scale beta3-lvh Trial
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Gonzales, Ricardo, primary, Arvidsson, Per, additional, Lukaschuk, Elena, additional, Balligand, Jean-Luc, additional, Heiberg, Einar, additional, Peters, Dana, additional, Zhang, Qiang, additional, Ferreira, Vanessa, additional, and Piechnik, Stefan, additional
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- 2024
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21. Comparison of T1- and t2-mapping in the Detection of Acute Myocardial Oedema in Takotsubo Syndrome
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Thomas, Katharine, primary, Lukaschuk, Elena, additional, Channon, Keith, additional, Neubauer, Stefan, additional, Piechnik, Stefan, additional, and Ferreira, Vanessa, additional
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- 2024
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22. Quantitative CMR population imaging on 20,000 subjects of the UK Biobank imaging study: LV/RV quantification pipeline and its evaluation
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Attar, Rahman, Pereañez, Marco, Gooya, Ali, Albà, Xènia, Zhang, Le, de Vila, Milton Hoz, Lee, Aaron M., Aung, Nay, Lukaschuk, Elena, Sanghvi, Mihir M., Fung, Kenneth, Paiva, Jose Miguel, Piechnik, Stefan K., Neubauer, Stefan, Petersen, Steffen E., and Frangi, Alejandro F.
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- 2019
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23. Incidence of diabetes after SARS-CoV-2 infection in England and the implications of COVID-19 vaccination: a retrospective cohort study of 16 million people
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Taylor, Kurt, Eastwood, Sophie, Walker, Venexia, Cezard, Genevieve, Knight, Rochelle, Al Arab, Marwa, Wei, Yinghui, Horne, Elsie M F, Teece, Lucy, Forbes, Harriet, Walker, Alex, Fisher, Louis, Massey, Jon, Hopcroft, Lisa E M, Palmer, Tom, Cuitun Coronado, Jose, Ip, Samantha, Davy, Simon, Dillingham, Iain, Morton, Caroline, Greaves, Felix, Macleod, John, Goldacre, Ben, Wood, Angela, Chaturvedi, Nishi, Sterne, Jonathan A C, Denholm, Rachel, Al Arab, Marwa, Almaghrabi, Fatima, Andrews, Colm, Badrick, Ellena, Baz, Sarah, Beckford, Chelsea, Berman, Samantha, Bolton, Tom, Booth, Charlotte, Bowyer, Ruth, Boyd, Andy, Bridger-Staatz, Charis, Brophy, Sinead, Campbell, Archie, Campbell, Kirsteen C, Carnemolla, Alisia, Carpentieri, Jd, Cezard, Genevieve, Chaturvedi, Nishi, Cheetham, Nathan, Costello, Ruth, Cowling, Thomas, Crane, Matthew, Cuitun Coronado, Jose Ignacio, Curtis, Helen, Denaxas, Spiros, Denholm, Rachel, Di Gessa, Giorgio, Dobson, Richard, Douglas, Ian, Evans, Katharine M, Fang, Chao, Ferreira, Vanessa, Finnigan, Lucy, Fisher, Louis, Flaig, Robin, Folarin, Amos, Forbes, Harriet, Foster, Diane, Fox, Laura, Freydin, Maxim, Garcia, Paz, Gibson, Andy, Glen, Fiona, Goldacre, Ben, Goncalves Soares, Ana, Greaves, Felix, Green, Amelia, Green, Mark, Green, Michael, Griffith, Gareth, Hamill Howes, Lee, Hamilton, Olivia, Herbet, Annie, Herrett, Emily, Hopcroft, Lisa, Horne, Elsie, Hou, Bo, Hughes, Alun, Hulme, William, Huntley, Lizzie, Ip, Samantha, Jacques, Wels, Jezzard, Peter, Jones, Louise, Kanagaratnam, Arun, Karthikeyan Suseeladevi, Arun, Katikireddi, Vittal, Kellas, John, Kennedy, Jonathan I, Kibble, Milla, Knight, Rochelle, Knueppel, Anika, Kopasker, Daniel, Kromydas, Theocharis, Kwong, Alex, Langan, Sinead, Lemanska, Agnieszka, Lukaschuk, Elena, Mackenna, Brain, Macleod, John, Maddock, Jane, Mahalingasivam, Viyaasan, Mansfield, Kathryn, McArdle, Fintan, McCartney, Daniel, McEachan, Rosie, McElroy, Eoin, McLachlan, Stela, Mitchell, Ruth, Moltrecht, Bettina, Morley, Jess, Nab, Linda, Neubauer, Stefan, Nigrelli, Lidia, North, Teri, Northstone, Kate, Oakley, Jacqui, Palmer, Tom, Park, Chloe, Parker, Michael, Parsons, Sam, Patalay, Praveetha, Patel, Kishan, Perez-Reche, Francisco, Piechnik, Stefan, Piehlmaier, Dominik, Ploubidis, George, Rafeti, Elena, Raman, Betty, Ranjan, Yatharth, Rapala, Alicja, Rhead, Rebecca, Roberts, Amy, Sampri, Alexia, Sanders, Zeena-Britt, Santorelli, Gillian, Saunders, Laura C, Shah, Anoop, Shah, Syed Ahmar, Sharp, Steve, Shaw, Richard, Sheard, Laura, Sheikh, Aziz, Silverwood, Richard, Smeeth, Liam, Smith, Stephen, Stafford, Jean, Steptoe, Andrew, Sterne, Jonathan, Steves, Claire, Stewart, Callum, Taylor, Kurt, Tazare, John, Teece, Lucy, Thomas, Richard, Thompson, Ellen, Tilling, Kate, Timpson, Nicholas, Tomlinson, Laurie, Toms, Renin, Tunnicliffe, Elizabeth, Turner, Emma L, Walker, Alex, Walker, Venexia, Walter, Scott, Wang, Kevin, Wei, Yinghui, Whitehorn, Rebecca, Wielgoszewska, Bozena, Wild, James M, Willan, Kathryn, Willans, Robert, Williams, Dylan, Wong, Andrew, Wood, Angela, Woodward, Hannah, Wright, John, Yang, Tiffany, Zaninotto, Paola, Zheng, Bang, and Zhu, Jingmin
- Abstract
Some studies have shown that the incidence of type 2 diabetes increases after a diagnosis of COVID-19, although the evidence is not conclusive. However, the effects of the COVID-19 vaccine on this association, or the effect on other diabetes subtypes, are not clear. We aimed to investigate the association between COVID-19 and incidence of type 2, type 1, gestational and non-specific diabetes, and the effect of COVID- 19 vaccination, up to 52 weeks after diagnosis.
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- 2024
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24. Cardiac Remodeling After Hypertensive Pregnancy Following Physician-Optimized Blood Pressure Self-Management: The POP-HT Randomized Clinical Trial Imaging Sub-study
- Author
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Kitt, Jamie, primary, Krasner, Samuel, additional, Barr, Logan, additional, Frost, Annabelle, additional, Tucker, Katherine, additional, Bateman, Paul A., additional, Suriano, Katie, additional, Kenworthy, Yvonne, additional, Lapidaire, Winok, additional, Lacharie, Miriam, additional, Mills, Rebecca, additional, Roman, Cristian, additional, Mackillop, Lucy, additional, Cairns, Alexandra, additional, Aye, Christina, additional, Ferreira, Vanessa, additional, Piechnik, Stefan, additional, Lukaschuk, Elena, additional, Thilaganathan, Basky, additional, Chappell, Lucy C., additional, Lewandowski, Adam J., additional, McManus, Richard J., additional, and Leeson, Paul, additional
- Published
- 2023
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- View/download PDF
25. 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
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Thomas, Katharine E, primary, Lukaschuk, Elena, additional, Shanmuganathan, Mayooran, additional, Kitt, Jamie A, additional, Popescu, Iulia A, additional, Neubauer, Stefan, additional, Piechnik, Stefan K, additional, and Ferreira, Vanessa M, additional
- Published
- 2023
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- View/download PDF
26. Toward Replacing Late Gadolinium Enhancement With Artificial Intelligence Virtual Native Enhancement for Gadolinium-Free Cardiovascular Magnetic Resonance Tissue Characterization in Hypertrophic Cardiomyopathy
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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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27. 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
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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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28. Towards the Semantic Enrichment of Free-Text Annotation of Image Quality Assessment for UK Biobank Cardiac Cine MRI Scans
- Author
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Carapella, Valentina, Jiménez-Ruiz, Ernesto, Lukaschuk, Elena, Aung, Nay, Fung, Kenneth, Paiva, Jose, Sanghvi, Mihir, Neubauer, Stefan, Petersen, Steffen, Horrocks, Ian, Piechnik, Stefan, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Carneiro, Gustavo, editor, Mateus, Diana, editor, Peter, Loïc, editor, Bradley, Andrew, editor, Tavares, João Manuel R. S., editor, Belagiannis, Vasileios, editor, Papa, João Paulo, editor, Nascimento, Jacinto C., editor, Loog, Marco, editor, Lu, Zhi, editor, Cardoso, Jaime S., editor, and Cornebise, Julien, editor
- Published
- 2016
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29. 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
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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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30. Kiosk 2R-FC-01 - Improving Sex-based Differences in ECG Diagnostic Performance for CMR Abnormalities Post Severe COVID-19 Infections
- Author
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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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31. Kiosk 9R-TB-09 - Comparison of T1- and t2-mapping in the Detection of Acute Myocardial Oedema in Takotsubo Syndrome
- Author
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Thomas, Katharine, Lukaschuk, Elena, Channon, Keith, Neubauer, Stefan, Piechnik, Stefan, and Ferreira, Vanessa
- Published
- 2024
- Full Text
- View/download PDF
32. Kiosk 9R-TA-01 - Automated CMR Index of Left Ventricular Diastolic Function (e’): A Validation Study Against Echocardiography in the Large-scale beta3-lvh Trial
- Author
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Gonzales, Ricardo, Arvidsson, Per, Lukaschuk, Elena, Balligand, Jean-Luc, Heiberg, Einar, Peters, Dana, Zhang, Qiang, Ferreira, Vanessa, and Piechnik, Stefan
- Published
- 2024
- Full Text
- View/download PDF
33. Fully-automated left ventricular mass and volume MRI analysis in the UK Biobank population cohort: evaluation of initial results
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Suinesiaputra, Avan, Sanghvi, Mihir M., Aung, Nay, Paiva, Jose Miguel, Zemrak, Filip, Fung, Kenneth, Lukaschuk, Elena, Lee, Aaron M., Carapella, Valentina, Kim, Young Jin, Francis, Jane, Piechnik, Stefan K., Neubauer, Stefan, Greiser, Andreas, Jolly, Marie-Pierre, Hayes, Carmel, Young, Alistair A., and Petersen, Steffen E.
- Published
- 2018
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34. 178 Cardiac manifestations of igg4-related disease
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Henry, John Aaron, primary, Thomas, Katharine, additional, Xavier, Roshan, additional, Lukaschuk, Elena, additional, Neubauer, Stefan, additional, Ferreira, Vanessa, additional, Rider, Oliver, additional, Burrage, Matthew, additional, Culver, Emma, additional, Piechnik, Stefan, additional, Lewis, Andrew, additional, and Selvaraj, Emmanuel, additional
- Published
- 2023
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35. Reference ranges for cardiac structure and function using cardiovascular magnetic resonance (CMR) in Caucasians from the UK Biobank population cohort
- Author
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Petersen, Steffen E., Aung, Nay, Sanghvi, Mihir M., Zemrak, Filip, Fung, Kenneth, Paiva, Jose Miguel, Francis, Jane M., Khanji, Mohammed Y., Lukaschuk, Elena, Lee, Aaron M., Carapella, Valentina, Kim, Young Jin, Leeson, Paul, Piechnik, Stefan K., and Neubauer, Stefan
- Published
- 2016
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36. Changes in Cardiac Morphology and Function in Individuals With Diabetes Mellitus: The UK Biobank Cardiovascular Magnetic Resonance Substudy
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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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37. Real-Time Prediction of Segmentation Quality
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Robinson, Robert, primary, Oktay, Ozan, additional, Bai, Wenjia, additional, Valindria, Vanya V., additional, Sanghvi, Mihir M., additional, Aung, Nay, additional, Paiva, José M., additional, Zemrak, Filip, additional, Fung, Kenneth, additional, Lukaschuk, Elena, additional, Lee, Aaron M., additional, Carapella, Valentina, additional, Kim, Young Jin, additional, Kainz, Bernhard, additional, Piechnik, Stefan K., additional, Neubauer, Stefan, additional, Petersen, Steffen E., additional, Page, Chris, additional, Rueckert, Daniel, additional, and Glocker, Ben, additional
- Published
- 2018
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38. Automated quality control in image segmentation: application to the UK Biobank cardiovascular magnetic resonance imaging study
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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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39. Left ventricular anatomy in obstructive hypertrophic cardiomyopathy: beyond basal septal hypertrophy
- Author
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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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- View/download PDF
40. 3.2 First Genome-Wide Association Study of Cardiovascular Magnetic Resonance Derived Aortic Distensibility Reveals 7 Loci
- Author
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Fung, Kenneth, Biasiolli, Luca, Hann, Evan, Ramirez, Julia, Lukaschuk, Elena, Aung, Nay, Paiva, Jose, Werys, Konrad, Sanghvi, Mihir, Thomson, Ross, Rayner, Jennifer, Puchta, Henrike, Moon, Niall, Thomas, Katharine, Lee, Aaron, Piechnik, Stefan, Neubauer, Stefan, Petersen, Steffen, and Munroe, Patricia
- Published
- 2019
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41. Automated cardiovascular magnetic resonance image analysis with fully convolutional networks
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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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42. Left ventricular anatomy in obstructive hypertrophic cardiomyopathy: beyond basal septal hypertrophy.
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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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43. The relationship of QRS morphology with cardiac structure and function in patients with heart failure
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Pellicori, Pierpaolo, Joseph, Anil C., Zhang, Jufen, Lukaschuk, Elena, Sherwi, Nasser, Bourantas, Christos V., Loh, Huan, Clark, Andrew L., and Cleland, John GF
- Published
- 2015
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44. Towards the Semantic Enrichment of Free-Text Annotation of Image Quality Assessment for UK Biobank Cardiac Cine MRI Scans
- Author
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Carapella, Valentina, primary, Jiménez-Ruiz, Ernesto, additional, Lukaschuk, Elena, additional, Aung, Nay, additional, Fung, Kenneth, additional, Paiva, Jose, additional, Sanghvi, Mihir, additional, Neubauer, Stefan, additional, Petersen, Steffen, additional, Horrocks, Ian, additional, and Piechnik, Stefan, additional
- Published
- 2016
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45. MOCOnet: Robust Motion Correction of Cardiovascular Magnetic Resonance T1 Mapping Using Convolutional Neural Networks
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Gonzales, Ricardo A., primary, Zhang, Qiang, additional, Papież, Bartłomiej W., additional, Werys, Konrad, additional, Lukaschuk, Elena, additional, Popescu, Iulia A., additional, Burrage, Matthew K., additional, Shanmuganathan, Mayooran, additional, Ferreira, Vanessa M., additional, and Piechnik, Stefan K., additional
- Published
- 2021
- Full Text
- View/download PDF
46. 9 Identification of thirty novel loci for cardiovascular magnetic resonance derived aortic distensibility in the UK Biobank
- Author
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Fung, Kenneth, primary, Biasiolli, Luca, additional, Hann, Evan, additional, Lukaschuk, Elena, additional, Ramírez, Julia, additional, Duijvenboden, Stefan van, additional, Aung, Nay, additional, Paiva, Jose M, additional, Sanghvi, Mihir M, additional, Thomson, Ross J, additional, Lee, Aaron M, additional, Piechnik, Stefan K, additional, Neubauer, Stefan, additional, Petersen, Steffen E, additional, and Munroe, Patricia B, additional
- Published
- 2021
- Full Text
- View/download PDF
47. Left atrial function measured by cardiac magnetic resonance imaging in patients with heart failure: clinical associations and prognostic value
- Author
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Pellicori, Pierpaolo, Zhang, Jufen, Lukaschuk, Elena, Joseph, Anil C., Bourantas, Christos V., Loh, Huan, Bragadeesh, Thanjavur, Clark, Andrew L., and Cleland, John G.F.
- Published
- 2015
- Full Text
- View/download PDF
48. Artificial Intelligence for Contrast-Free MRI: Scar Assessment in Myocardial Infarction Using Deep Learning-Based Virtual Native Enhancement.
- Author
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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
- Full Text
- View/download PDF
49. Atherosclerotic disease of the abdominal aorta and its branches: prognostic implications in patients with heart failure
- Author
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Bourantas, Christos V., Loh, Huan P., Sherwi, Nasser, Tweddel, Ann C., de Silva, Ramesh, Lukaschuk, Elena I., Nicholson, Antony, Rigby, Alan S., Thackray, Simon D., Ettles, Duncan F., Nikitin, Nikolay P., Clark, Andrew L., and Cleland, John G. F.
- Published
- 2012
- Full Text
- View/download PDF
50. Association Between Recreational Cannabis Use and Cardiac Structure and Function
- Author
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Khanji, Mohammed Y., Jensen, Magnus T., Kenawy, Asmaa A., Raisi-Estabragh, Zahra, Paiva, Jose M., Aung, Nay, Fung, Kenneth, Lukaschuk, Elena, Zemrak, Filip, Lee, Aaron M., Barutcu, Ahmet, Maclean, Edd, Cooper, Jackie, Piechnik, Stefan K., Neubauer, Stefan, and Petersen, Steffen E.
- Published
- 2020
- Full Text
- View/download PDF
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