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Updated: Jul 31, 2025

Cardiac Magnetic Resonance for the Evaluation of Suspected Cardiac Thrombus: Conventional and Emerging Techniques
Published on: June 11, 2019
Cardiac magnetic resonance in ischemic cardiomyopathy: present role and future directions
Alessandra Scatteia1, Santo Dellegrottaglie1,2
1Advanced Cardiovascular Imaging Unit, Ospedale Accreditato Villa dei Fiori, Corso Italia 157, 80011 Acerra, Naples, Italy.
Insights
Cardiac magnetic resonance (CMR) offers comprehensive evaluation for ischemic cardiomyopathy, aiding diagnosis and prognosis. Advanced CMR techniques, including AI, show promise for improving patient care and understanding of this condition.
Area of Science:
- Cardiology
- Medical Imaging
- Diagnostic Techniques
Background:
- Ischemic cardiomyopathy presents significant mortality and morbidity, necessitating precise diagnostic and prognostic tools.
- Cardiac magnetic resonance (CMR) provides comprehensive evaluation of myocardial function, perfusion, and tissue composition.
- CMR offers advantages over other non-invasive modalities like stress echocardiography and nuclear techniques, including no ionizing radiation and superior imaging quality.
Purpose of the Study:
- To review the clinical applications of CMR in diagnosing and managing ischemic cardiomyopathy.
- To highlight the utility of standard and advanced CMR sequences in patient assessment.
- To discuss future directions, including AI-driven implementations for CMR analysis.
Main Methods:
- Review of current literature on CMR in ischemic cardiomyopathy.
- Description of standard and advanced CMR sequences.
- Discussion of emerging technologies and artificial intelligence in CMR analysis.
Main Results:
- Stress perfusion CMR is accurate, safe, cost-effective, and clinically valuable for detecting myocardial ischemia severity and distribution.
- CMR provides unique tissue characterization information crucial for ischemic cardiomyopathy.
- Despite its benefits, CMR availability, cost, and processing time currently limit routine clinical use.
Conclusions:
- CMR is a consolidated technique for evaluating ischemic cardiomyopathy, offering comprehensive functional, perfusion, and tissue characterization.
- Ongoing developments, particularly AI, are expected to enhance CMR efficiency and expand its diagnostic and prognostic capabilities.
- Further integration of CMR into clinical practice is crucial for optimizing patient management in ischemic cardiomyopathy.
Abstract:
Ischemic cardiomyopathy is a significant cause of mortality and morbidity, with peculiar needs for accurate diagnostic and prognostic characterization. Cardiac magnetic resonance (CMR) can help to satisfy these requirements by allowing a comprehensive evaluation of myocardial function, perfusion and tissue composition, with a demonstrated utility in guiding clinical management of patients with known or suspected ischemic cardiomyopathy. When compared with alternative non-invasive imaging modalities, such as stress echocardiography and nuclear techniques, CMR is able to provide accurate (function and perfusion) or peculiar (tissue characterization) information on cardiac pathophysiology, while avoiding exposition to ionizing radiations and overcoming limitations related to the quality of the imaging window. In particular, stress perfusion CMR showed to be accurate, safe, cost-effective, and clinically valuable as a non-invasive test for detecting severity and distribution of myocardial ischemia. In many circumstances, however, local availability of the technique, together with procedural costs, and scanning and post-processing time duration still limit the use of CMR in clinical routine. In the current review, we focused on clinical applications of CMR in ischemic cardiomyopathy. The consolidated role of the technique is described by illustrating both standard and advanced sequences that constitute the current body of a dedicated CMR examination. Ongoing developments and potential future diagnostic and prognostic applications of CMR when assessing ischemic cardiomyopathy are also discussed, with a focus on artificial intelligence-based implementations proposed for refining the efficiency of CMR analysis and reporting.
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