Signal intensity variationin cardiac magnetic resonance sequences in the context of hypertrophic cardiomyopathy and myocarditis - Beco, Patrícia Andreia Brandão (2024)
ABSTRACT: Cardiovascular diseases are a leading cause of morbidity and mortality globally. Cardiac magnetic resonance is a non-invasive, ionizing radiation-free imaging method essential for the diagnosis and monitoring of cardiovascular diseases. Techniques like Late Gadolinium Enhancement, T1 Mapping, and CINE imaging are valuable for evaluating heart structure, function, and pathology. Late Gadolinium Enhancement, while effective, requires contrast agents, which may accumulate in the body and cause complications in patients with chronic kidney disease. This study investigates the possibility of using T1 Mapping and/or CINE images as the first triage to detect possible candidates for Late Gadolinium Enhancement images. Methods: A total of 36 individuals (18 LGE+ and 18 LGE-) undergoing CMR for myocarditis or Hypertrophic Cardiomyopathy were analyzed. Demographic data were collected, and mean values were recorded. Qualitative and quantitative analyses were performed on T1 Mapping and CINE images to assess their sensitivity, specificity, accuracy, and precision in detecting myocardial enhancement. Results: T1 Mapping has shown potential in identifying changes in the myocardium, but with variations in sensitivity and less consistency in detecting pathological patterns when compared to CINE Imaging, which has shown greater consistency, with 72% sensitivity in detecting affected areas. Blind test analysis showed that 73% of patients had no lesions identified and were negative, avoiding the use of contrast. The physician was correct in 87.5% of positive cases, identifying at least one positive image modality of hypersignal, ensuring that these patients received contrast correctly and only 12.5% would not need it. Discussion/Conclusions: The results show that T1 mapping and CINE images are promising as initial triage tools for detecting Myocarditis and HCM. This would exclude many healthy patients from contrast-enhanced examinations, reducing risks and costs. This would increase efficiency, allowing for more scans in less time, crucial during gadolinium shortages. The analysis also highlights the importance of machine learning (ML) software to better detect the signal intensity variations.