First-pass perfusion cardiac magnetic resonance (CMR) allows the quantitative assessment of myocardial blood flow (MBF). However, flow estimates are sensitive to the delay between the arterial and myocardial tissue tracer arrival time (tOnset) and the accurate estimation of MBF relies on the precise identification of tOnset. The aim of this study is to assess the sensitivity of the quantification process to tOnset at voxel level. Perfusion data were obtained from series of simulated data, a hardware perfusion phantom, and patients. Fermi deconvolution has been used for analysis. A novel algorithm, based on sequential deconvolution, which minimizes the error between myocardial curves and fitted curves obtained after deconvolution, has been used to identify the optimal tOnset for each region. Voxel-wise analysis showed to be more sensitive to tOnset compared to segmental analysis. The automated detection of the tOnset allowed a net improvement of the accuracy of MBF quantification and in patients the identification of perfusion abnormalities in territories that were missed when a constant user-selected tOnset was used. Our results indicate that high-resolution MBF quantification should be performed with optimized tOnset values at voxel level.
- Myocardial perfusion quantification
- tracer arrival time delay