Quantitative SPECT for Targeted Radionuclide Therapy

描述（由申请人提供）：靶向放射性核素治疗（TRT）在包括甲状腺癌和非霍奇金淋巴瘤在内的许多癌症的治疗中发挥着越来越重要的作用。其他癌症的TRT制剂正处于不同的发展阶段。例如，使用Y-90标记微球局部治疗不可切除的肝脏肿瘤似乎很有希望。对于这些疗法，剂量测定是其开发、批准和验证的重要组成部分。剂量测定可以帮助减少试验中的不良反应，并提供对治疗药物在个体和群体中失败或成功的原因的见解。它还在患者特异性治疗计划中发挥重要作用。TRT的剂量估计是基于定量平面或SPECT成像研究的结果。定量平面成像虽然应用广泛，但涉及到各种图像退化效应的补偿组合，导致精度和精度不稳定。商业SPECT系统上可用的图像重建方法通常是为涉及视觉解释的诊断程序设计和优化的，而不是用于量化。在此资助的前一个资助期，我们开发了定量SPECT重建方法，并在全器官剂量学的背景下验证了它们。然而，对于肿瘤、局部治疗或放射性标记肽，3D剂量学是必不可少的，这需要在亚器官水平上估计器官中的3D活性分布。活动分布估计的准确性受到图像退化因素、噪声和部分体积效应的限制。不发射伽马射线的TRT试剂的轫致辐射成像能力可能有许多重要的应用，但由于主要光子的连续能谱和由此产生的高水平光子散射而变得复杂。在这一竞争更新中，我们建议开发和优化用于TRT剂量学应用的定量SPECT采集和重建方法。这些将包括SPECT重建方法，该方法模拟伽玛射线和轫致辐射发射器的图像退化效应。他们将使用3D和4D最大后验（MAP）方法来提供降噪，并结合解剖信息来减少部分体积效应。4D方法还将通过优化时间维度的平滑来进一步降低噪声，并将配准纳入重建算法。我们建议利用物理模拟、现实模拟和动物研究相结合的方法来优化和验证这些新的定量SPECT方法，并将其应用于几种TRT药物的临床试验。最后，我们将严格评估这些方法的准确性和精度，并与传统方法在模拟幽灵种群中进行比较。本研究的结果将是一套经过验证的定量SPECT重建方法，具有良好的准确度和精密度。这些方法将在三维剂量估计方面提供实质性的改进，从而能够预测和理解TRT的生物反应并优化治疗剂量。

DESCRIPTION (provided by applicant): Targeted radionuclide therapy (TRT) plays an increasingly important role in treatment of a number of cancers including thyroid cancer and non-Hodgkins lymphoma. TRT agents for other cancers are at various stages of development. For example, locoregional therapy using Y-90 labeled microspheres for non-resectable liver tumors appears promising. For these therapies, dosimetry is an essential part of their development, approval, and validation. Dosimetry can help reduce adverse reactions in trials and provides insight into the reasons for failure or success of the therapeutic agents both in individuals and in populations. It also plays an important role in patient-specific treatment planning. Dose estimates in TRT are based on results of quantitative planar or SPECT imaging studies. Quantitative planar imaging, though widely used, involves ad hoc combinations of compensations for various image degrading effects, resulting in variable accuracy and precision. Image reconstruction methods available on commercial SPECT systems are typically designed and optimized for diagnostic procedures involving visual interpretation and not for quantification. In the previous funding period of this grant we developed quantitative SPECT reconstruction methods and validated them in the context of whole organ dosimetry. However, for tumors, locoregional therapy or radiolabeled peptides, 3D dosimetry is essential, which requires estimates of the 3D activity distribution in organs at the sub-organ level. The accuracy of activity distribution estimates is limited by image degrading factors, noise, and partial volume effects. The ability to image bremsstrahlung radiation from TRT agents that do not emit gamma rays could have a number of important applications, but is complicated by the continuous energy spectrum of primary photons and the resulting high levels of photon scatter. In this competing renewal, we propose to develop and optimize quantitative SPECT acquisition and reconstruction methods for TRT dosimetry applications. These will include SPECT reconstruction methods that model the image degrading effects for both gamma ray and bremsstrahlung radiation emitters. They will use 3D and 4D maximum a posteriori (MAP) methods to provide noise reduction and incorporate anatomical information to reduce partial volume effects. The 4D methods will also provide further noise reduction through optimized smoothing in the time dimension and incorporate registration into the reconstruction algorithm. We propose to optimize and validate these new quantitative SPECT methods using a combination of physical phantom, realistic simulation and animal studies and to apply them in clinical trials of several TRT agents. Finally, we will rigorously evaluate the accuracy and precision of these methods in comparison with conventional methods in simulated populations of phantoms. The result of this research will be a set of well-validated quantitative SPECT reconstruction methods with well-characterized accuracies and precisions. These methods would provide substantial improvements in 3D dose estimates, and thus in the ability to predict and understand biological response and optimize therapeutic doses for TRT.