Division of Imaging Sciences and Biomedical Engineering, Wellcome Trust and EPSRC Medical Engineering Centre at Guy's and St. Thomas' NHS Foundation Trust, The Rayne Institute, St. Thomas' Hospital, London, United Kingdom.
Magn Reson Med. 2012 Dec;68(6):1994-2004. doi: 10.1002/mrm.24195. Epub 2012 Feb 21.
The purpose of this study is to enable high spatial resolution voxel-wise quantitative analysis of myocardial perfusion in dynamic contrast-enhanced cardiovascular MR, in particular by finding the most favorable quantification algorithm in this context. Four deconvolution algorithms--Fermi function modeling, deconvolution using B-spline basis, deconvolution using exponential basis, and autoregressive moving average modeling--were tested to calculate voxel-wise perfusion estimates. The algorithms were developed on synthetic data and validated against a true gold-standard using a hardware perfusion phantom. The accuracy of each method was assessed for different levels of spatial averaging and perfusion rate. Finally, voxel-wise analysis was used to generate high resolution perfusion maps on real data acquired from five patients with suspected coronary artery disease and two healthy volunteers. On both synthetic and perfusion phantom data, the B-spline method had the highest error in estimation of myocardial blood flow. The autoregressive moving average modeling and exponential methods gave accurate estimates of myocardial blood flow. The Fermi model was the most robust method to noise. Both simulations and maps in the patients and hardware phantom showed that voxel-wise quantification of myocardium perfusion is feasible and can be used to detect abnormal regions.
本研究旨在实现动态对比增强心血管磁共振中高空间分辨率的心肌灌注容积分析，特别是找到最适合的定量算法。我们测试了四种解卷积算法——费米函数建模、使用 B 样条基的解卷积、使用指数基的解卷积和自回归移动平均建模——来计算像素级的灌注估计值。这些算法是在合成数据上开发的，并通过硬件灌注体模与真实的金标准进行了验证。评估了每种方法在不同空间平均水平和灌注率下的准确性。最后，使用从五名疑似冠心病患者和两名健康志愿者获得的真实数据进行了像素级分析，生成了高分辨率的灌注图。在合成数据和灌注体模数据上，B 样条方法在估计心肌血流量方面的误差最高。自回归移动平均建模和指数方法可以准确估计心肌血流量。费米模型对噪声的鲁棒性最高。患者和硬件体模中的模拟和图谱都表明，心肌灌注的像素级定量是可行的，可用于检测异常区域。
