Advancements in T1 Mapping for Cardiac MRI: Comprehensive Review of Techniques, Clinical Applications, Artificial...

The use of cardiovascular magnetic resonance (CMR) T1 mapping is becoming the dominant method for quantitatively characterizing diffuse tissue abnormalities in the myocardium that cannot be detected by traditional late gadolinium enhancement (LGE). This review provides a comprehensive overview of recent technical advances in T1 mapping, including a critical evaluation of the different acquisition techniques and their clinical applications. It also examines the role of artificial intelligence (AI) in the interpretation of T1 mapping data. It discusses its potential impact on clinical implementation and the translation of T1 mapping into routine practice.

A narrative review of the current literature related to T1 mapping was conducted using PubMed and Scopus. The search focused on T1 mapping techniques, including their development, normal ranges, clinical applications, and AI approaches to T1 mapping image analysis.

Differentiation among T1 mapping techniques (MOLLI, SHMOLLI, SASHA, SMART1Map) based on accuracy, robustness, and acquisition limitations can influence their use in clinical practice. The literature indicates considerable variability in the T1 normal range, which is attributed to sequence type, magnetic field strength, manufacturer, and patient characteristics, making it challenging to create a universal reference range. From a clinical perspective, T1 mapping helps evaluate cardiomyopathy, infiltrative and ischemic disease (including myocarditis), as it shows improved ability to detect diffuse myocardial involvement compared to LGE alone. AI algorithms may be useful for automating segmentation, reducing motion artefacts, and improving measurement consistency.

Despite its considerable clinical value, the broader implementation of T1 mapping remains challenged by variations in acquisition protocols and the lack of universally standardized reference values. Artificial intelligence may help mitigate some of these limitations by enhancing measurement consistency and facilitating automated image analysis. Nevertheless, further validation studies and greater harmonization across imaging platforms are still necessary before AI-driven approaches can be fully integrated into routine clinical practice.

T1 mapping represents a valuable non-invasive technique for myocardial tissue characterization. Although AI has the potential to enhance reproducibility, improve image quality, and streamline workflow, broader clinical adoption remains contingent upon further efforts. These include the standardization of acquisition and analysis methods, validation in large multicenter cohorts, and the attainment of regulatory approval.