Reader Andrew J, Sureau Florent C, Comtat Claude, Trébossen Régine, Buvat Irène
School of Chemical Engineering and Analytical Science, The University of Manchester, PO Box 88, Manchester M60 1QD, UK.
Phys Med Biol. 2006 Nov 7;51(21):5455-74. doi: 10.1088/0031-9155/51/21/005. Epub 2006 Oct 6.
A fully 4D joint-estimation approach to reconstruction of temporal sequences of 3D positron emission tomography (PET) images is proposed. The method estimates both a set of temporal basis functions and the corresponding coefficient for each basis function at each spatial location within the image. The joint estimation is performed through a fully 4D version of the maximum likelihood expectation maximization (ML-EM) algorithm in conjunction with two different models of the mean of the Poisson measured data. The first model regards the coefficients of the temporal basis functions as the unknown parameters to be estimated and the second model regards the temporal basis functions themselves as the unknown parameters. The fully 4D methodology is compared to the conventional frame-by-frame independent reconstruction approach (3D ML-EM) for varying levels of both spatial and temporal post-reconstruction smoothing. It is found that using a set of temporally extensive basis functions (estimated from the data by 4D ML-EM) significantly reduces the spatial noise when compared to the independent method for a given level of image resolution. In addition to spatial image quality advantages, for smaller regions of interest (where statistical quality is often limited) the reconstructed time-activity curves show a lower level of bias and a lower level of noise compared to the independent reconstruction approach. Finally, the method is demonstrated on clinical 4D PET data.
提出了一种用于三维正电子发射断层扫描（PET）图像时间序列重建的全四维联合估计方法。该方法在图像内的每个空间位置估计一组时间基函数以及每个基函数对应的系数。联合估计通过全四维版本的最大似然期望最大化（ML-EM）算法结合泊松测量数据均值的两种不同模型来执行。第一种模型将时间基函数的系数视为待估计的未知参数，第二种模型将时间基函数本身视为未知参数。对于不同程度的空间和时间重建后平滑处理，将全四维方法与传统的逐帧独立重建方法（三维ML-EM）进行了比较。结果发现，与给定图像分辨率水平下的独立方法相比，使用一组时间上广泛的基函数（通过四维ML-EM从数据中估计）可显著降低空间噪声。除了空间图像质量优势外，对于较小的感兴趣区域（统计质量通常有限），与独立重建方法相比，重建的时间-活性曲线显示出较低的偏差水平和较低的噪声水平。最后，在临床四维PET数据上展示了该方法。
