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4. Interaction of a priori Anatomic Knowledge with Self-Supervised Contrastive Learning in Cardiac Magnetic Resonance Imaging

6. Comparison of dual-bolus versus dual-sequence techniques for determining myocardial blood flow and myocardial perfusion reserve by cardiac magnetic resonance stress perfusion: From the Automated Quantitative analysis of myocardial perfusion cardiac Magnetic Resonance Consortium

7. Second-Line Myocardial Perfusion Imaging to Detect Obstructive Stenosis: Head-to-Head Comparison of CMR and PET

8. Abstract 16882: Comparison of Regadenoson and Adenosine in First Pass Quantitative Perfusion Cardiovascular Magnetic Resonance in Subjects With Suspected Coronary Microvascular Disease

9. Abstract 16817: Comparison of Diagnostic Performance of Quantitative Perfusion Stress Myocardial Perfusion Imaging With Cardiac Magnetic Resonance Between Different Vasodilators

13. Abstract 14447: Multi-Center Evaluation of Stress Myocardial Blood Flow by Cardiac Magnetic Resonance in Known and Suspected Ischemic Heart Disease: Preliminary Findings From the AQUA-MBF Initiative

20. Machine Learning Can Distinguish Hypertrophic Cardiomyopathy, Cardiac Light Chain and Transthyretin Amyloidosis Using Cardiac Magnetic Resonance Imaging Data and Standard Clinical Information

22. Diagnostic Accuracy of Global Stress Myocardial Blood Flow for the Detection of Obstructive Coronary Artery Disease: findings FBom the AQUA-MBF Study

28. Kiosk 9R-FB-03 - Diagnostic Accuracy of Global Stress Myocardial Blood Flow for the Detection of Obstructive Coronary Artery Disease: findings FBom the AQUA-MBF Study

34. CMR 3-102 - Machine Learning Can Distinguish Hypertrophic Cardiomyopathy, Cardiac Light Chain and Transthyretin Amyloidosis Using Cardiac Magnetic Resonance Imaging Data and Standard Clinical Information

35. DIFFERENCES IN QUALITATIVE AND FULLY-AUTOMATED QUANTITATIVE ASSESSMENT OF ADENOSINE STRESS PERFUSION CMR IN A REAL-WORLD POPULATION OF PATIENTS-POTENTIAL CLINICAL CONSEQUENCES

38. Second-Line Myocardial Perfusion Imaging to Detect Obstructive Stenosis:Head-to-Head Comparison of CMR and PET

39. Towards Emotional Interaction: Using Movies to Automatically Learn Users’ Emotional States

40. Did 'Minority Report' Get It Wrong? Superiority of the Mouse over 3D Input Devices in a 3D Placement Task

42. DEEP LEARNING ANALYSIS USING CARDIOVASCULAR MAGNETIC RESONANCE IMAGING FOR RISK PREDICTION IN CARDIAC AMYLOIDOSIS

45. Abstract 12691: Comparison of Deep Learning and Radiomic Features to Differentiate Non-Ischemic and Ischemic Cardiomyopathy Using Cardiac MRI Cine Imaging

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