Artificial Intelligence (AI) for Myocardial Tissue Characterization in Cardiac Magnetic Resonance (CMR) represents a burgeoning field with significant potential to revolutionize non-invasive cardiac diagnostics. Traditionally, CMR relies on gadolinium-based contrast agents (GBCA) to assess late gadolinium enhancement (LGE) for myocardial viability and structure. However, the use of GBCA poses risks, including nephrogenic systemic fibrosis and gadolinium deposition in tissues. Parametric native T1 and T2 mapping have emerged as promising alternatives, offering quantitative pixel-wise myocardial tissue phenotyping. Despite their potential, the lack of standardized mapping sequences and thresholds for distinguishing between healthy and diseased myocardium has hindered their routine clinical application. Recent advancements in AI have opened new avenues for myocardial disease pattern recognition, leveraging existing myocardial signal features in non-contrast steady-state free precession cine imaging and T1 and T2 mapping. These AI-based solutions promise to mitigate the limitations of current methods, reduce reliance on contrast agents, and enhance diagnostic accuracy, particularly in resource-limited settings.

This research topic aims to explore and develop AI strategies for myocardial tissue characterization in CMR, with the goal of advancing non-invasive diagnostic techniques. Specifically, the research seeks to address questions such as: How can AI improve the accuracy and reliability of myocardial tissue phenotyping? What are the most effective AI models for identifying myocardial disease patterns without contrast agents? How can these AI solutions be standardized and integrated into clinical practice to benefit a broader patient population?

To gather further insights in the application of AI for myocardial tissue characterization, we welcome articles addressing, but not limited to, the following themes:
- Development and validation of AI models for myocardial tissue phenotyping
- Comparative studies of AI-based and traditional CMR techniques
- Standardization of AI algorithms for clinical use
- Impact of AI on reducing gadolinium use and scan times
- AI applications in low-resource settings for cardiac diagnostics
- Integration of AI with existing CMR workflows
- Ethical considerations and regulatory challenges in AI-based CMR

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Frontiers in Cardiovascular Medicine

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