Scientific Institute H San Raffaele, Via Olgettina 60, 20132 Milano, Italy.
Phys Med Biol. 2010 Jul 21;55(14):4131-51. doi: 10.1088/0031-9155/55/14/012. Epub 2010 Jul 5.
The interest in positron emission tomography (PET) and particularly in hybrid integrated PET/CT systems has significantly increased in the last few years due to the improved quality of the obtained images. Nevertheless, one of the most important limits of the PET imaging technique is still its poor spatial resolution due to several physical factors originating both at the emission (e.g. positron range, photon non-collinearity) and at detection levels (e.g. scatter inside the scintillating crystals, finite dimensions of the crystals and depth of interaction). To improve the spatial resolution of the images, a possible way consists of measuring the point spread function (PSF) of the system and then accounting for it inside the reconstruction algorithm. In this work, the system response of the GE Discovery STE operating in 3D mode has been characterized by acquiring (22)Na point sources in different positions of the scanner field of view. An image-based model of the PSF was then obtained by fitting asymmetric two-dimensional Gaussians on the (22)Na images reconstructed with small pixel sizes. The PSF was then incorporated, at the image level, in a three-dimensional ordered subset maximum likelihood expectation maximization (OS-MLEM) reconstruction algorithm. A qualitative and quantitative validation of the algorithm accounting for the PSF has been performed on phantom and clinical data, showing improved spatial resolution, higher contrast and lower noise compared with the corresponding images obtained using the standard OS-MLEM algorithm.
由于获得的图像质量得到了提高，正电子发射断层扫描（PET），特别是集成式正电子发射断层扫描/计算机断层扫描（PET/CT）系统，在过去几年中的兴趣显著增加。然而，由于发射（例如正电子射程、光子非共线性）和检测水平（例如闪烁晶体中的散射、晶体的有限尺寸和相互作用深度）都存在多种物理因素，PET 成像技术的一个最重要限制仍然是其空间分辨率较差。为了提高图像的空间分辨率，可以通过测量系统的点扩散函数（PSF），然后在重建算法中考虑它来实现。在这项工作中，通过在扫描仪视场的不同位置获取（22）Na 点源，对在 3D 模式下运行的 GE Discovery STE 系统的响应进行了特征描述。然后，通过对使用小像素尺寸重建的（22）Na 图像拟合不对称二维高斯函数，获得了基于图像的 PSF 模型。然后，将 PSF 作为图像级别的一部分，合并到三维有序子集最大似然期望最大化（OS-MLEM）重建算法中。对包含 PSF 的算法在体模和临床数据上进行了定性和定量验证，与使用标准 OS-MLEM 算法获得的相应图像相比，该算法显示出了更高的空间分辨率、更高的对比度和更低的噪声。
