Zarinabad Niloufar, Hautvast Gilion L T F, Sammut Eva, Arujuna Aruna, Breeuwer Marcel, Nagel Eike, Chiribiri Amedeo
Division of Imaging Sciences and Biomedical Engineering, The Rayne Institute, St. Thomas¿ Hospital, London, U.K.
Philips Group Innovation¿Healthcare Incubators, Philips Research High Tech Campus, Eindhoven, AE, The Netherlands.
IEEE Trans Biomed Eng. 2014 Sep;61(9):2499-2506. doi: 10.1109/TBME.2014.2322937.
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.
首过灌注心脏磁共振成像(CMR)可对心肌血流(MBF)进行定量评估。然而,血流估计对动脉和心肌组织示踪剂到达时间(tOnset)之间的延迟很敏感,准确估计MBF依赖于对tOnset的精确识别。本研究的目的是在体素水平评估量化过程对tOnset的敏感性。灌注数据来自一系列模拟数据、硬件灌注模型和患者。采用费米反卷积进行分析。一种基于顺序反卷积的新算法已被用于识别每个区域的最佳tOnset,该算法可使心肌曲线与反卷积后获得的拟合曲线之间的误差最小化。体素级分析显示,与节段分析相比,其对tOnset更敏感。tOnset的自动检测使MBF量化的准确性得到了显著提高,并且在患者中能够识别出在使用固定的用户选择tOnset时遗漏区域的灌注异常。我们的结果表明,高分辨率MBF量化应在体素水平使用优化的tOnset值进行。