Development and evaluation of convergent and accelerated penalized SPECT image reconstruction methods for improved dose-volume histogram estimation in radiopharmaceutical therapy.
Development and evaluation of convergent and accelerated penalized SPECT image reconstruction methods for improved dose-volume histogram estimation in radiopharmaceutical therapy.
复制标题
开发和评估收敛和加速惩罚 SPECT 图像重建方法，以改进放射性药物治疗中的剂量体积直方图估计。
DOI：
10.1118/1.4897613
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发表时间：
2014
期刊：
影响因子：
3.8
通讯作者：
Frey,EricC
中科院分区：
文献类型：
--
作者：
Cheng,Lishui;Hobbs,RobertF;Sgouros,George;Frey,EricC
目的：三维（3D）剂量学有可能更好地预测正常组织和肿瘤的反应，它是基于从放射断层扫描中获得的患者活性分布的三维估计。剂量-体积直方图（DVHs）是一种重要的三维剂量测量方法，是放射治疗中广泛使用的治疗计划工具。精确的三维剂量测定需要精确估计空间和时间上的放射性分布。这项工作的目的是开发和证明惩罚SPECT图像重建方法的潜力，以改善从3D剂量学方法获得的dvh估计。方法：作者开发了惩罚图像重建方法，使用最大后验（MAP）形式主义，其本质上包含正则化以控制噪声，并且与线性滤波器不同，旨在保留锐利的边缘。研究了两种先验：一种是三维双曲先验，称为单时间MAP (STMAP)；另一种是四维双曲先验，称为跨时间MAP (CTMAP)，利用空间和时间信息来控制噪声。CTMAP方法假设估计的活动分布与来自不同时间点的投影数据集之间的完美配准。推导并实现了加速算法和收敛算法。在蒙特卡罗模拟研究中，采用改良的基于NURBS的心脏躯干模型和多室肾模型，以及来自临床研究的器官活动和参数来评估这些方法。计算累积剂量率体积直方图（CDRVHs）和累积DVHs (CDVHs)，并对使用惩罚算法和OS - EM重建的幻影和SPECT图像进行定性和定量比较。将STMAP方法应用于患者数据，并对STMAP和OS‐EM获得的CDRVHs进行定性比较。结果表明，与优化后滤波的OS - EM相比，惩罚算法显著提高了肝脏等大型器官的CDRVH和CDVH估计值。例如，CTMAP和STMAP获得的注射后5小时肝脏CDRVHs的均方误差（MSEs）分别约为最佳过滤OS‐EM获得的MSEs的15%和17%。对于CDVH估计，CTMAP和STMAP获得的mse分别约为OS‐EM的16%和19%。对于肾脏和肾皮质，所有算法都观察到较大的残差，可能是由于部分体积效应。STMAP方法在应用于患者数据时显示出有希望的定性结果。结论通过仿真研究，提出并评价了图像分块重建方法。研究表明，与优化后滤波的OS - EM重建相比，MAP算法大大提高了肝脏等大型器官的CDVH估计。对于具有精细结构细节的小器官，如肾脏，MAP算法和OS‐EM都观察到很大的残余误差。虽然CTMAP提供的mse略好于STMAP，但考虑到在算法中处理不同时间点图像的错配需要额外的努力，以及残余错配的潜在影响，3D正则化方法（如STMAP中使用的方法）似乎是更实用的选择。
PurposeThree‐dimensional (3D) dosimetry has the potential to provide better prediction of response of normal tissues and tumors and is based on 3D estimates of the activity distribution in the patient obtained from emission tomography. Dose–volume histograms (DVHs) are an important summary measure of 3D dosimetry and a widely used tool for treatment planning in radiation therapy. Accurate estimates of the radioactivity distribution in space and time are desirable for accurate 3D dosimetry. The purpose of this work was to develop and demonstrate the potential of penalized SPECT image reconstruction methods to improve DVHs estimates obtained from 3D dosimetry methods.MethodsThe authors developed penalized image reconstruction methods, using maximuma posteriori(MAP) formalism, which intrinsically incorporate regularization in order to control noise and, unlike linear filters, are designed to retain sharp edges. Two priors were studied: one is a 3D hyperbolic prior, termed single‐time MAP (STMAP), and the second is a 4D hyperbolic prior, termed cross‐time MAP (CTMAP), using both the spatial and temporal information to control noise. The CTMAP method assumed perfect registration between the estimated activity distributions and projection datasets from the different time points. Accelerated and convergent algorithms were derived and implemented. A modified NURBS‐based cardiac‐torso phantom with a multicompartment kidney model and organ activities and parameters derived from clinical studies were used in a Monte Carlo simulation study to evaluate the methods. Cumulative dose‐rate volume histograms (CDRVHs) and cumulative DVHs (CDVHs) obtained from the phantom and from SPECT images reconstructed with both the penalized algorithms and OS‐EM were calculated and compared both qualitatively and quantitatively. The STMAP method was applied to patient data and CDRVHs obtained with STMAP and OS‐EM were compared qualitatively.ResultsThe results showed that the penalized algorithms substantially improved the CDRVH and CDVH estimates for large organs such as the liver compared to optimally postfiltered OS‐EM. For example, the mean squared errors (MSEs) of the CDRVHs for the liver at 5 h postinjection obtained with CTMAP and STMAP were about 15% and 17%, respectively, of the MSEs obtained with optimally filtered OS‐EM. For the CDVH estimates, the MSEs obtained with CTMAP and STMAP were about 16% and 19%, respectively, of the MSEs from OS‐EM. For the kidneys and renal cortices, larger residual errors were observed for all algorithms, likely due to partial volume effects. The STMAP method showed promising qualitative results when applied to patient data.ConclusionsPenalized image reconstruction methods were developed and evaluated through a simulation study. The study showed that the MAP algorithms substantially improved CDVH estimates for large organs such as the liver compared to optimally postfiltered OS‐EM reconstructions. For small organs with fine structural detail such as the kidneys, a large residual error was observed for both MAP algorithms and OS‐EM. While CTMAP provided marginally better MSEs than STMAP, given the extra effort needed to handle misregistration of images at different time points in the algorithm and the potential impact of residual misregistration, 3D regularization methods, such as that used in STMAP, appear to be a more practical choice.