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首页> 外文期刊>Physics in medicine and biology. >Joint estimation of dynamic PET images and temporal basis functions using fully 4D ML-EM
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Joint estimation of dynamic PET images and temporal basis functions using fully 4D ML-EM

机译：使用全4D ML-EM联合估算动态PET图像和时基函数

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摘要

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.
机译：提出了一种用于重建3D正电子发射断层扫描（PET）图像时间序列的全4D联合估计方法。该方法估计图像中每个空间位置处的时间基础函数集和每个基础函数的对应系数。联合估计是通过最大似然期望最大化（ML-EM）算法的完整4D版本，结合两个不同的泊松测量数据平均值模型来执行的。第一个模型将时基函数的系数视为要估计的未知参数，第二个模型将时基函数本身视为未知参数。完整的4D方法与传统的逐帧独立重建方法（3D ML-EM）进行了比较，以实现不同水平的空间和时间重建后平滑。发现对于给定水平的图像分辨率，与独立方法相比，使用一组时间范围广泛的基函数（通过4D ML-EM从数据中估算）可显着降低空间噪声。除了空间图像质量优势外，与独立的重建方法相比，对于较小的关注区域（经常限制统计质量），重建的时间活动曲线还显示出较低的偏差水平和较低的噪声水平。最后，该方法在临床4D PET数据上得到了证明。

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