University of Mostaganem, Mostaganem, Algeria.
Comput Med Imaging Graph. 2012 Dec;36(8):620-6. doi: 10.1016/j.compmedimag.2012.07.002. Epub 2012 Aug 10.
Positron emission tomography (PET) imaging has the capability to produce regional or parametric images of physiological aspects in a tissue of interest. Apart from the acquired PET data, the concentration of the radiotracer supplied to the tissue through the vascularization has to be known as the input function (IF). IF can be obtained by manual or automatic blood sampling and cross calibrated with PET. These procedures are cumbersome, invasive and generate uncertainties. In the present work, we determine IF from internal artery in fluorodeoxyglucose (18F-FDG) brain images by means of Independent Component Analysis (ICA) based on Bayesian theory and Monte Carlo Markov Chain (MCMC) sampling method (BICA, Bayesian ICA). Dynamic brain images were decomposed with BICA into image sequences of blood and tissue components. A region of interest was drawn around the internal artery in the blood image to determine BICA-IF. BICA-IF was therefore corrected for spillover of radioactivity emission from tissue, then it was normalized with three plasma samples to correct for partial volume and blood to plasma radioactivity ratio. BICA-IF was found comparable to IF determined by blood sampling, and rCMRG values in several brain structures obtained with BICA-IF and sampled IF showed a bias of 6.4% which was attributed to the difference in the time sampling of 4s for sampled IF versus 15 s for BICA-MC-IF at early times. In conclusion, BICA is a strong approach in image decomposition to extract blood curves in a noninvasive way.
正电子发射断层扫描(PET)成像能够对感兴趣组织中的生理方面进行区域或参数成像。除了获取的 PET 数据外,还必须知道通过血管化供应给组织的示踪剂浓度作为输入函数(IF)。IF 可以通过手动或自动采血并与 PET 交叉校准来获得。这些过程繁琐、有创且会产生不确定性。在本工作中,我们通过基于贝叶斯理论和蒙特卡罗马尔可夫链(MCMC)采样方法的独立成分分析(ICA)(BICA,贝叶斯 ICA)从氟脱氧葡萄糖(18F-FDG)脑图像中的内部动脉确定 IF。动态脑图像通过 BICA 分解为血液和组织成分的图像序列。在血液图像中围绕内部动脉绘制感兴趣区域以确定 BICA-IF。然后,对 BICA-IF 进行放射性溢出校正,并用三个血浆样本进行归一化以校正部分容积和血液与血浆放射性比。发现 BICA-IF 与采血确定的 IF 相当,并且用 BICA-IF 和采样 IF 获得的几个脑结构中的 rCMRG 值显示出 6.4%的偏差,这归因于采样 IF 的 4s 时间采样与 BICA-MC-IF 的 15s 时间采样在早期的差异。总之,BICA 是一种强大的图像分解方法,可以以非侵入性的方式提取血液曲线。