Quantitative T1 mapping in cardiomyopathy

Recent advancements in techniques of Cardiac Magnetic Resonance Imaging provide extended quantitative measurements of myocardial T1. Important tissue characteristics can be tracked noninvasively to allow practitioners to quantify important properties of regional and global myocardium function. Quantification of these T1 measures involves the compilation of multiple images to create a T1 recovery curve, providing a map that estimates the T1 value as an encoded pixel value. After contrast injection, the data is compared with native (no applied contrast agent) T1 to examine myocardial disease involving the interstitium as well as the extracellular volume fraction. Myocardial T1 mapping is an emerging biomarker for quantification of myocardial disease (since an important indicator of heart disease is the expansion of myocardial interstitial space, as is fibrosis). This paper explores the detection and quantification of cardiac involvement using delayed gadolinium enhancement combined with T1 mapping and myocardial extracellular volume fraction. It extends the research being conducted on Cardiac sarcoidosis, an important cardiomyopathy. Cardiac sarcoidosis is a multisystem granulomatous disease of unknown etiology. Cardiac MR is able to detect the active, inflammatory phase of the disease as well as the chronic phase where scarring and fibrosis are dominant. The use of gadolinium-based contrast agents improves the ability to discriminate ischemic from nonischemic etiologies, owing to different patterns among the various nonischemic cardiomyopathies. Since gadolinium shortens T1 relaxation time, the result is a brighter signal intensity in areas with increased interstitial space on inversion recovery T1-weighted sequences. The 1.5 Tesla Philips Achieva XR Scanner was used to collect the pre- and post- contrast images from five anonymous patients (subjects), following the MOLLI protocol. These images were stacked and run through MRMap, which creates parametric image maps of the MOLLI data. Data was graphed employing the Gado Partition Coefficient