Cardiac involvement in sarcoidosis is currently under-diagnosed despite being the leading cause of death amongst sarcoidosis patients. Therefore, accurate and early diagnosis of subclinical but active cardiac sarcoidosis (ACS) is an important clinical goal. The recent advent of hybrid PET/MRI has enabled the multi-parametric and non-invasive evaluation of ACS in the myocardium at a significantly reduced radiation exposure. Although cardiac MRI with late gadolinium enhancement (LGE) may visualize the anatomical pattern of myocardial injury due to ACS, it cannot differentiate between active disease and old chronic scarring. On the other hand, 18FFDG PET standard uptake values (SUVs), which are used to identify regions of increased myocardial inflammation in patients with ACS, may not discriminate ACS-related inflammatory FDG signal, from high non-specific physiological FDG uptake, often leading to false positive ACS interpretation. In this study, we introduce a clinically feasible dynamic 18F-FDG PET/MR cardiac imaging protocol enabling the 4D analysis of the 18FFDG myocardial uptake pattern, coregistered with the LGE MR images. In addition, we employ advanced direct 4D Patlak parametric PET image reconstructions to deliver, beside standard semi-quantitative SUVs, highly quantitative images of the physiological parameters of FDG uptake rate (Ki), obtained directly from the complete 4D PET acquisition data, for enhanced robustness to statistical noise and Ki quantification. We believe that our proposed framework of multi-parametric 4D 18F-FDG PET/MR cardiac imaging may substantially improve ACS diagnosis by i) allowing a more accurate identification of positive ACS patterns in matched myocardium regions across LGE, SUV as well as Ki images and ii) enabling the quantitative Ki-driven differentiation between true- and false-positive ACS indications for enhanced specificity.
|2017 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2017 - Conference Proceedings
|2017 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2017
|21/10/17 → 28/10/17