Design and evaluation of an abbreviated pixelwise dynamic contrast enhancement analysis protocol for early extracellular volume fraction estimation

Introduction: T1-based method is considered as the gold standard for extracellular volume fraction (ECV) mapping. This technique requires at least a 10 min delay after injection to acquire the post injection T1 map. Quantitative analysis of Dynamic Contrast Enhancement (DCE) images could lead to an earlier estimation of an ECV like parameter (2 min). The purpose of this study was to design a quantitative pixel-wise DCE analysis workflow to assess the feasibility of an early estimation of ECV. Methods: Fourteen patients with mitral valve prolapse were included in this study. The MR protocol, performed on a 3 T MR scanner, included MOLLI sequences for T1 maps acquisition and a standard SR-turboFlash sequence for dynamic acquisition. DCE data were acquired for at least 120 s. We implemented a full DCE analysis pipeline with a pre-processing step using an innovative motion correction algorithm (RC-REG algorithm) and a post-processing step using the extended Tofts Model (ECVETM). Estimated ECVETM maps were compared to standard T1-based ECV maps (ECVT1) with both a Pearson correlation analysis and a group-wise analysis. Results: Image and map quality assessment showed systematic improvements using the proposed workflow. Strong correlation was found between ECVETM, and ECVT1 values (r-square = 0.87). Conclusion: A DCE analysis workflow based on RC-REG algorithm and ETM analysis can provide good quality parametric maps. Therefore, it is possible to extract ECV values from a 2 min-long DCE acquisition that are strongly correlated with ECV values from the T1 based method.

Automated summary

The study aimed to assess the feasibility of early estimation of ECV through a quantitative pixel-wise DCE analysis workflow. Results showed strong correlation between ECV values obtained from a 2-minute DCE acquisition using RC-REG algorithm and ECV values from the T1-based method, indicating the potential for accurate ECV estimation in a shorter timeframe. The proposed workflow demonstrated systematic improvements in image and map quality assessment, making it a promising approach for ECV mapping.