Repeatability of radiomic features in myocardial T1 and T2 mapping.

Purpose: To investigate the test-retest repeatability of radiomic features in myocardial native T1 and T2 mapping.

Methods: In this prospective study, 50 healthy volunteers (29 women and 21 men, mean age 39.4 ± 13.

Color-map recommendation for MR relaxometry maps.

Purpose: To harmonize the use of color for MR relaxometry maps and therefore recommend the use of specific color-maps for representing , , and maps and their inverses.

Methods: Perceptually linearized color-maps were chosen to have similar color settings as those proposed by Griswold et al. in 2018.

Color Maps: Facilitating the Clinical Impact of Quantitative MRI.

Presenting quantitative data using non-standardized color maps potentially results in unrecognized misinterpretation of data. Clinically meaningful color maps should intuitively and inclusively represent data without misleading interpretation. Uniformity of the color gradient for color maps is critically important.

Perfect Match: Radiomics and Artificial Intelligence in Cardiac Imaging.

Cardiovascular diseases remain a significant health burden, with imaging modalities like echocardiography, cardiac computed tomography, and cardiac magnetic resonance imaging playing a crucial role in diagnosis and prognosis. However, the inherent heterogeneity of these diseases poses challenges, necessitating advanced analytical methods like radiomics and artificial intelligence. Radiomics extracts quantitative features from medical images, capturing intricate patterns and subtle variations that may elude visual inspection.