Colilla S, Crow A, Petkun W, Singer DE, Simon T, Liu X. Estimates of current and future incidence and prevalence of atrial fibrillation in the U.S. adult population. Am J Cardiol. 2013;112(8):1142–7. https://doi.org/10.1016/j.amjcard.2013.05.063.

Article  PubMed  Google Scholar 

ElMaghawry M, Romeih S. DECAAF: emphasizing the importance of MRI in AF ablation. Glob Cardiol Sci Pract. 2015;2015:8. https://doi.org/10.5339/gcsp.2015.8.

Article  PubMed  PubMed Central  Google Scholar 

Marrouche NF, Wilber D, Hindricks G, Jais P, Akoum N, Marchlinski F, Kholmovski E, Burgon N, Hu N, Mont L, Deneke T, Duytschaever M, Neumann T, Mansour M, Mahnkopf C, Herweg B, Daoud E, Wissner E, Bansmann P, Brachmann J. Association of atrial tissue fibrosis identified by delayed enhancement MRI and atrial fibrillation catheter ablation: the DECAAF study. JAMA. 2014;311(5):498–506. https://doi.org/10.1001/jama.2014.3.

Article  CAS  PubMed  Google Scholar 

Burstein B, Nattel S. Atrial fibrosis: mechanisms and clinical relevance in atrial fibrillation. J Am Coll Cardiol. 2008;51(8):802–9. https://doi.org/10.1016/j.jacc.2007.09.064.

Article  CAS  PubMed  Google Scholar 

Verma A, Jiang C-Y, Betts TR, Chen J, Deisenhofer I, Mantovan R, Macle L, Morillo CA, Haverkamp W, Weerasooriya R, Albenque J-P, Nardi S, Menardi E, Novak P, Sanders P. Approaches to catheter ablation for persistent atrial fibrillation. N Engl J Med. 2015;372(19):1812–22. https://doi.org/10.1056/NEJMoa1408288. Publisher: Massachusetts Medical Society _eprint.

Oakes RS, Badger TJ, Kholmovski EG, Akoum N, Burgon NS, Fish EN, Blauer JJE, Rao SN, DiBella EVR, Segerson NM, Daccarett M, Windfelder J, McGann CJ, Parker D, MacLeod RS, Marrouche NF. Detection and quantification of left atrial structural remodeling with delayed-enhancement magnetic resonance imaging in patients with atrial fibrillation. Circulation. 2009;119(13):1758–67. https://doi.org/10.1161/CIRCULATIONAHA.108.811877.

Article  PubMed  PubMed Central  Google Scholar 

Caixal G, Alarcón F, Althoff TF, Nuñez-Garcia M, Benito EM, Borràs R, Perea RJ, Prat-González S, Garre P, Soto-Iglesias D, Gunturitz C, Cozzari J, Linhart M, Tolosana JM, Arbelo E, Roca-Luque I, Sitges M, Guasch E, Mont L. Accuracy of left atrial fibrosis detection with cardiac magnetic resonance: correlation of late gadolinium enhancement with endocardial voltage and conduction velocity. Europace. 2021;23(3):380–8. https://doi.org/10.1093/europace/euaa313.

Article  PubMed  Google Scholar 

Lange M, Kwan E, Dosdall DJ, MacLeod RS, Bunch TJ, Ranjan R. Case report: personalized computational model guided ablation for left atrial flutter. Front Cardiovasc Med. 2022;9:893752. https://doi.org/10.3389/fcvm.2022.893752.

Article  PubMed  PubMed Central  Google Scholar 

McDowell KS, Vadakkumpadan F, Blake R, Blauer J, Plank G, MacLeod RS, Trayanova NA. Methodology for patient-specific modeling of atrial fibrosis as a substrate for atrial fibrillation. J Electrocardiol. 2012;45(6):640–5. https://doi.org/10.1016/j.jelectrocard.2012.08.005.

Article  PubMed  PubMed Central  Google Scholar 

Chow LS, Paramesran R. Review of medical image quality assessment. Biomed Signal Process Control. 2016;27:145–54. https://doi.org/10.1016/j.bspc.2016.02.006.

Article  Google Scholar 

Kaufman L, Kramer DM, Crooks LE, Ortendahl DA. Measuring signal-to-noise ratios in MR imaging. Radiology. 1989;173(1):265–7. https://doi.org/10.1148/radiology.173.1.2781018. Publisher: Radiological Society of North America. Accessed 2023-05-11.

Henkelman RM. Measurement of signal intensities in the presence of noise in MR images. Med Phys. 1985;12(2):232–3. https://doi.org/10.1118/1.595711.

Article  CAS  PubMed  Google Scholar 

Geissler A, Gartus A, Foki T, Tahamtan AR, Beisteiner R, Barth M. Contrast-to-noise ratio (CNR) as a quality parameter in fMRI. J Magn Reson Imaging. 2007;25(6):1263–70. https://doi.org/10.1002/jmri.20935. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/jmri.20935.

Salem KA, Lewin JS, Aschoff AJ, Duerk JL, Wilson DL. Validation of a human vision model for image quality evaluation of fast interventional magnetic resonance imaging. J Electron Imaging. 2002;11(2):224–35. https://doi.org/10.1117/1.1453412.

Article  Google Scholar 

Cavaro-Menard C, Zhang L, Le Callet P. Diagnostic quality assessment of medical images: challenges and trends. In: 2010 2nd European Workshop on Visual Information Processing (EUVIP), pp. 277–284. 2010. https://doi.org/10.1109/EUVIP.2010.5699147.

Zhang L, Gooya A, Dong B, Hua R, Petersen SE, Medrano-Gracia P, Frangi AF. Automated quality assessment of cardiac MR images using convolutional neural networks. In: Tsaftaris SA, Gooya A, Frangi AF, Prince JL, editors. Simulation and Synthesis in Medical Imaging. Lecture Notes in Computer Science, Springer, Cham; 2016. pp. 138–145. https://doi.org/10.1007/978-3-319-46630-9_14.

Pizarro RA, Cheng X, Barnett A, Lemaitre H, Verchinski BA, Goldman AL, Xiao E, Luo Q, Berman KF, Callicott JH, Weinberger DR, Mattay VS. Automated quality assessment of structural magnetic resonance brain images based on a supervised machine learning algorithm. Front Neuroinf. 2016;10:.

Nabavi, S., Simchi, H., Moghaddam, M.E., Abin, A.A., Frangi, A.F.: A generalised deep meta-learning model for automated quality control of cardiovascular magnetic resonance images. [eess]; 2023.https://doi.org/10.48550/arXiv.2303.13324. arXiv:2303.13324. Accessed 09 May 2023.

Siebermair J, Kholmovski EG, Marrouche N. Assessment of left atrial fibrosis by late gadolinium enhancement magnetic resonance imaging. JACC: Clin Electrophysiol. 2017;3(8):791–802. https://doi.org/10.1016/j.jacep.2017.07.004. Accessed 29 Nov 2023.

Streiner DL, Norman GR, Cairney J. Health measurement scales: a practical guide to their development and use. Oxford University Press; 2015. Google-Books-ID: JH3OBAAAQBAJ.

Fleiss JL, Levin B, Paik MC. Statistical methods for rates and proportions, 1st edn. Wiley Series in Probability and Statistics. Wiley; 2003. https://doi.org/10.1002/0471445428. https://onlinelibrary.wiley.com/doi/book/10.1002/0471445428. Accessed 10 Aug 2023.

Cicchetti DV, Sparrow SA. Developing criteria for establishing interrater reliability of specific items: applications to assessment of adaptive behavior. Am J Ment Defic. 1981;86(2):127–37.

He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, pp. 770–778. 2016.

Loshchilov I, Hutter F. SGDR: stochastic gradient descent with warm restarts. 2016. arXiv:1608.03983

Draelos RL, Carin L. Use hirescam instead of grad-cam for faithful explanations of convolutional neural networks. 2020. arXiv:2011.08891.

Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision, pp. 618–626. 2017.