Then we tested the accuracy of the tOnset detection algorithm and validated it against gold standard perfusion measurements using an MR-compatible perfusion phantom.
In both experiments, we evaluated the impact of CNR on the detection of the onset arrival time and, thus, on the accuracy of perfusion estimates. For this purpose, the original data were corrupted by adding Rician noise of variable amplitudes to both Caif (t) and Ctiss(t) [12 (link)], [13 (link)]. The range of noise amplitude was chosen so that CNR in the both Caif (t) and Ctiss(t) would be between 5 and 40. Equal noise amplitudes were added to both Caif (t) and Ctiss(t) at each CNR level.
Finally, we compared the results of voxel-wise and segmental analysis performed with optimized tOnset with the results obtained from considering tOnset a free global parameter in phantom and in vivo.
All the analyses described in this study were performed using house-made software programmed with MATLAB (Mathworks, Natick, MA, USA, version R2010b). All data (phantom and patient) were acquired on a Philips Achieva 3T (TX) system, equipped with a 32-channel cardiac-phased array receiver coil (Philips Healthcare, Best, The Netherlands).