Gillen Rebecca, Erlandsson Kjell, Denis-Bacelar Ana M, Thielemans Kris, Hutton Brian F, McQuaid Sarah J
Institute of Nuclear Medicine, University College London, London, UK.
National Physical Laboratory, Teddington, UK.
EJNMMI Phys. 2022 Sep 5;9(1):59. doi: 10.1186/s40658-022-00489-5.
Currently, there is no consensus on the optimal partial volume correction (PVC) algorithm for oncology imaging. Several existing PVC methods require knowledge of the reconstructed resolution, usually as the point spread function (PSF)-often assumed to be spatially invariant. However, this is not the case for SPECT imaging. This work aimed to assess the accuracy of SPECT quantification when PVC is applied using a case-specific PSF.
Simulations of SPECT [Formula: see text]Tc imaging were performed for a range of activity distributions, including those replicating typical clinical oncology studies. Gaussian PSFs in reconstructed images were estimated using perturbation with a small point source. Estimates of the PSF were made in situations which could be encountered in a patient study, including; different positions in the field of view, different lesion shapes, sizes and contrasts, noise-free and noisy data. Ground truth images were convolved with the perturbation-estimated PSF, and with a PSF reflecting the resolution at the centre of the field of view. Both were compared with reconstructed images and the root-mean-square error calculated to assess the accuracy of the estimated PSF. PVC was applied using Single Target Correction, incorporating the perturbation-estimated PSF. Corrected regional mean values were assessed for quantitative accuracy.
Perturbation-estimated PSF values demonstrated dependence on the position in the Field of View and the number of OSEM iterations. A lower root mean squared error was observed when convolution of the ground truth image was performed with the perturbation-estimated PSF, compared with convolution using a different PSF. Regional mean values following PVC using the perturbation-estimated PSF were more accurate than uncorrected data, or data corrected with PVC using an unsuitable PSF. This was the case for both simple and anthropomorphic phantoms. For the simple phantom, regional mean values were within 0.7% of the ground truth values. Accuracy improved after 5 or more OSEM iterations (10 subsets). For the anthropomorphic phantoms, post-correction regional mean values were within 1.6% of the ground truth values for noise-free uniform lesions.
Perturbation using a simulated point source could potentially improve quantitative SPECT accuracy via the application of PVC, provided that sufficient reconstruction iterations are used.
目前,关于肿瘤成像的最佳部分容积校正(PVC)算法尚无共识。现有的几种PVC方法需要了解重建分辨率,通常将其作为点扩散函数(PSF)——通常假定其在空间上是不变的。然而,单光子发射计算机断层显像(SPECT)成像并非如此。这项工作旨在评估使用特定病例PSF进行PVC时SPECT定量的准确性。
针对一系列活度分布进行SPECT[公式:见正文]锝成像模拟,包括那些复制典型临床肿瘤学研究的分布。利用小的点源微扰估计重建图像中的高斯PSF。在患者研究中可能遇到的情况下进行PSF估计,包括:视野中的不同位置、不同的病变形状、大小和对比度、无噪声和有噪声的数据。将真实图像与微扰估计的PSF以及反映视野中心分辨率的PSF进行卷积。将两者与重建图像进行比较,并计算均方根误差以评估估计PSF的准确性。使用包含微扰估计PSF的单目标校正应用PVC。评估校正后的区域平均值的定量准确性。
微扰估计的PSF值显示出对视野位置和有序子集最大期望值最大化(OSEM)迭代次数的依赖性。与使用不同PSF进行卷积相比,如果使用微扰估计的PSF对真实图像进行卷积,则观察到更低的均方根误差。使用微扰估计的PSF进行PVC后的区域平均值比未校正的数据或使用不合适的PSF进行PVC校正的数据更准确。简单模型和拟人模型均是如此。对于简单模型,区域平均值在真实值的0.7%以内。在进行5次或更多次OSEM迭代(10个子集)后,准确性有所提高。对于拟人模型,对于无噪声均匀病变,校正后的区域平均值在真实值的1.6%以内。
只要使用足够的重建迭代次数,使用模拟点源进行微扰有可能通过应用PVC提高SPECT定量的准确性。