Schmid Volker J, Gatehouse Peter D, Yang Guang-Zhong
Institute for Biomedical Engineering, Imperial College, South Kensington, London, United Kingdom.
Med Image Comput Comput Assist Interv. 2007;10(Pt 1):393-400. doi: 10.1007/978-3-540-75757-3_48.
Non-linear attenuation of the Arterial Input Function (AIF) is a major problem in first-pass MR perfusion imaging due to the high concentration of the contrast agent in the blood pool. This paper presents a technique to reconstruct the true AIF using signal intensities in the myocardium and the attenuated AIF based on a Hierarchical Bayesian Model (HBM). With the proposed method, both the AIF and the response function are modeled as smoothed functions by using Bayesian penalty splines (P-Splines). The derived AIF is then used to estimate the impulse response of the myocardium based on deconvolution analysis. The proposed technique is validated both with simulated data using the MMID4 model and ten in vivo data sets for estimating myocardial perfusion reserve rates. The results demonstrate the ability of the proposed technique in accurately reconstructing the desired AIF for myocardial perfusion quantification. The method does not involve any MRI pulse sequence modification, and thus is expected to have wider clinical impact.
由于血池中造影剂浓度较高，动脉输入函数（AIF）的非线性衰减是首次通过磁共振灌注成像中的一个主要问题。本文提出了一种基于分层贝叶斯模型（HBM），利用心肌中的信号强度和衰减的AIF来重建真实AIF的技术。在所提出的方法中，通过使用贝叶斯惩罚样条（P-Splines）将AIF和响应函数都建模为平滑函数。然后，基于去卷积分析，利用导出的AIF来估计心肌的脉冲响应。所提出的技术通过使用MMID4模型的模拟数据和十个用于估计心肌灌注储备率的体内数据集进行了验证。结果证明了所提出的技术能够准确重建用于心肌灌注定量的所需AIF。该方法不涉及任何MRI脉冲序列修改，因此有望产生更广泛的临床影响。
