◼ Interventional Systems cardiac disease through cardiovascular imaging. His areas of expertise include valvular heart disease, coronary artery disease and heart failure, emphasizing a patient-centered approach to medicine and general cardiology. Dr Christoph Gräni is Professor in Cardiology at the University of Bern, Switzerland, and Director of Cardiac Imaging, covering Echocardiography, Cardiac Magnetic Resonance, Cardiac Com- puted Tomography and Nuclear Cardiology at the University Hospital Bern. One of his main research focuses is on improving the diagnosis and risk stratification of different cardiomyopathies, myocarditis and cardiac amyloidosis, using multimodality cardi- ac imaging myocardial function analysis and fibrosis assessment. Additionally, his research includes non-invasive assessment of coronary artery disease, especially coronary artery anomalies. Dr Chiara Bucciarelli-Ducci is a cardiologist from Royal Bro- mpton and Harefield Hospital, UK and associate professor at King College London’s School of Biomedical Engineering & Imaging. She is an international opinion leader on the use of CMR in cardiovascular medicine and has over 20 years of clinical experience and expertise. Her career focuses on improving the care of patients by improving the identification of heart abnor- malities in a wide range of conditions and symptoms. Dr Marc Dweck is Professor of Clinical Cardiology and Con- sultant at the University of Edinburgh, UK. He is Vice President of EACVI with clinical interests in multi-modality imaging and cardiac device implantation. His research program is centred around the use of multi-modality imaging (echo, CT, CMR, PET) to improve our understanding of cardiovascular pathophysi- ology and ultimately to improve patient assessment, care and outcomes. He has published in many of the leading medical and cardiovascular journals and is the recipient of numerous nation- al and international awards. 1 Peck D, Rwebembera J, Nakagaayu D et al.: The Use of Artificial Intelligence Guid- ance for Rheumatic Heart Disease Screening by Novices; Journal of the American Society of Echocardiography 2023; https://doi.org/10.1016/j.echo.2023.03.001 2 Hirata Y, Nomura Y, Saijo Y, Sata M, Kusunose K: Reducing echocardiographic examination time through routine use of fully automated software: a comparative study of measurement and report creation time; Journal of Echocardiography 2024; https://doi.org/10.1007/s12574-023-00636-6 3 Slivnick JA, Hawkes W, oliveira J et al.: Cardiac amyloidosis detection from a sin- gle echocardiographic video clip: a novel artificial intelligence-based screening tool; European Heart Journal 2025; https://doi.org/10.1093/eurheartj/ehaf387 4 Valsaraj A, Kalmady SV, Sharma V et al.: Development and validation of echo- cardiography-based machine-learning models to predict mortality; eBioMedicine 2023; https://doi.org/10.1016/j.ebiom.2023.104479 5 Finck T, Hardenberg J, Will A et al.: 10-Year Follow-Up After Coronary Computed Tomography Angiography in Patients With Suspected Coronary Artery Disease; JACC Cardiovascular Imaging 2018; https://doi.org/10.1016/j.jcmg.2018.07.020 6 Sakai K, Shin D, Singh M et al.: Diagnostic Performance and Clinical Impact of Photon-Counting Detector Computed Tomography in Coronary Artery Disease; JACC 2024; https://doi.org/10.1016/j.jacc.2024.10.069 7 Feuchtner GM, Lacaita PG, Bax JJ et al.: AI-Quantitative CT Coronary Plaque Fea- tures Associate With a Higher Relative Risk in Women: CONFIRM2 Registry; Circulation: Cardiovascular Imaging 2025; https://doi.org/10.1161/CIRCIMAGING.125.018235 8 Nieman K, García-García HM, Hideo-Kajita A et al.: Standards for quantitative assessments by coronary computed tomography angiography (CCTA); Journal of Car- diovascular Computed Tomography 2024; https://doi.org/10.1016/j.jcct.2024.05.232 9 Oikonomou EK, Marwan M, Desai MY et al.: Non-invasive detection of coronary inflammation using computed tomography and prediction of residual cardiovascu- lar risk (the CRISP CT study): a post-hoc analysis of prospective outcome data; Lancet 2018; https://doi.org/10.1016/S0140-6736(18)31114-0 10 Chan K, Wahome E, Tsiachristas A et al.: Inflammatory risk and cardiovascular events in patients without obstructive coronary artery disease: the ORFAN mul- ticentre, longitudinal cohort study; Lancet 2024; https://doi.org/10.1016/S0140- 6736(24)00596-8 11 Shiri I, Baj G, Mohammadi Kazaj P et al.: AI-based detection and classification of anomalous aortic origin of coronary arteries using coronary CT angiography images; Nature Communications 2025; https://doi.org/10.1038/s41467-025-58362-9 12 Illi J, Stark AW, Ilic M et al.: Hemodynamic Relevance Evaluation of Coronary Artery Anomaly During Stress Using FFR/IVUS in an Artificial Twin; JACC: Case Reports 2024; https://doi.org/10.1016/j.jaccas.2024.102729 13 Raman SV, Markl M, Patel AR et al.: 30-minute CMR for common clinical indica- tions: a Society for Cardiovascular Magnetic Resonance white paper; Journal of Cardi- ovascular Magnetic Resonance 2022; https://doi.org/10.1186/s12968-022-00844-6 14 Foley JRJ, Richmond C, Fent GJ et al.: Rapid Cardiovascular Magnetic Resonance for Ischemic Heart Disease Investigation (RAPID-IHD); JACC: Cardiovascular Imaging 2020; https://doi.org/10.1016/j.jcmg.2020.01.029 15 Bustin A, Stuber M, Sermesant M, Cochet H: Smart cardiac magnetic resonance delivering one-click and comprehensive assessment of cardiovascular disease; Euro- pean Heart Journal 2023; https://doi.org/10.1093/eurheartj/ehac814 16 Bhuva AN, Bai W, Lau C et al.: A Multicenter, Scan-Rescan, Human and Machine Learning CMR Study to Test Generalizability and Precision in Imaging Biomarker Analysis; Circulation: Cardiovascular Imaging 2019; https://doi.org/10.1161/circim- aging.119.009214 17 Liu Y, Hamilton J, Rajagopalan S et al.: Cardiac Magnetic Resonance Fingerprint- ing: Technical Overview and Initial Results; JACC: Cardiovascular Imaging 2018; https://doi.org/10.1016/j.jcmg.2018.08.028 18 Christidoulou AG, Shaw JL, Ngyuen C et al.: Magnetic resonance multitasking for motion-resolved quantitative cardiovascular imaging; Nature biomedical Engineer- ing 2018; https://doi.org/10.1038/s41551-018-0217-y 19 Kotecha T, Martinez-Naharro A, Boldrini M et al.: Automated Pixel-Wise Quan- titative Myocardial Perfusion Mapping by CMR to Detect Obstructive Coronary Artery Disease and Coronary Microvascular Dysfunction: Validation Against Invasive Coronary Physiology; JACC: Cardiovascular Imaging 2019; https://doi.org/10.1016/j. jcmg.2018.12.022 20 Hillier E, Friedrich MG: The Potential of Oxygenation-Sensitive CMR in Heart Fail- ure; Current Heart Failure Reports 2021; https://doi.org/10.1007/s11897-021-00525-y 21 Pizzi MN, Roque A, Cuéllar-Calabria H et al.: 18F-FDG-PET/CTA of Prosthetic Car- diac Valves and Valve-Tube Grafts: Infective Versus Inflammatory Patterns; JACC: Car- diovascular Imaging 2016; https://doi.org/10.1016/j.jcmg.2016.05.013 22 Rubeaux M, Joshi NV, Dweck MR et al.: Motion Correction of 18F-NaF PET for Imaging Coronary Atherosclerotic Plaques; Journal of Nuclear Medicine 2016; https://doi.org/10.2967/jnumed.115.162990 24 RADBook 2026