Nuclear Medicine Department, Lapeyronie University Hospital, Montpellier, France.
Ann Nucl Med. 2013 Jan;27(1):84-95. doi: 10.1007/s12149-012-0657-5. Epub 2012 Oct 4.
We propose a statistical stopping criterion for iterative reconstruction in emission tomography based on a heuristic statistical description of the reconstruction process.
The method was assessed for MLEM reconstruction. Based on Monte-Carlo numerical simulations and using a perfectly modeled system matrix, our method was compared with classical iterative reconstruction followed by low-pass filtering in terms of Euclidian distance to the exact object, noise, and resolution. The stopping criterion was then evaluated with realistic PET data of a Hoffman brain phantom produced using the GATE platform for different count levels.
The numerical experiments showed that compared with the classical method, our technique yielded significant improvement of the noise-resolution tradeoff for a wide range of counting statistics compatible with routine clinical settings. When working with realistic data, the stopping rule allowed a qualitatively and quantitatively efficient determination of the optimal image.
Our method appears to give a reliable estimation of the optimal stopping point for iterative reconstruction. It should thus be of practical interest as it produces images with similar or better quality than classical post-filtered iterative reconstruction with a mastered computation time.
我们提出了一种基于启发式统计重建过程描述的发射断层成像迭代重建的统计停止准则。
该方法用于最大似然估计重建。基于蒙特卡罗数值模拟，并使用完全建模的系统矩阵，我们的方法与经典的迭代重建后进行低通滤波进行了比较，从与精确物体的欧几里得距离、噪声和分辨率方面进行了比较。然后，使用 GATE 平台生成的 Hoffman 脑幻影的真实 PET 数据，针对不同的计数水平，对停止准则进行了评估。
数值实验表明，与经典方法相比，我们的技术在与常规临床设置兼容的广泛计数统计范围内，对噪声分辨率折衷有显著改善。在使用真实数据时，该停止规则可以定性和定量地有效地确定最佳图像。
我们的方法似乎可以对迭代重建的最佳停止点进行可靠估计。因此，它具有实际意义，因为它可以产生与经典后滤波迭代重建相似或更好质量的图像，同时掌握计算时间。
