Advanced planar image reconstruction for targeted alpha therapy

项目摘要 标题：用于靶向阿尔法治疗的先进平面图像重建 基于阿尔法放射药物(AER)的靶向阿尔法治疗(TAT)是近年来出现的一种治疗方法 成功地用于治疗许多无法用传统方法治愈的晚期癌症。 然而，目前成像AER生物分布的方法是次优的，因为给药非常少。 活动。因此，AER的生物分布是未知的，因此AER的吸收剂量分布是 未知。因此，我们预测正常组织毒性和开出个性化治疗的能力是 妥协了。对AER的生物分布进行准确的定量成像对于纠正这种情况是必要的。 为了满足这一需求，我们项目的长期目标是开发一种方法来生成准确的 低计数AER平面数据与低剂量CT联合配准的AER投影图像(Proj-AER) 通过SPECT/CT相机获取。所获得的Proj-AER结合CT将允许准确的AER活动 将用作剂量测量模型的输入数据的估计，该模型将提供有关辐射的信息 器官/肿瘤的剂量。该方案的目标是开发一种新的稀疏性来促进重建 基于物理模型的平面AER成像方法及其优越性的概念验证研究 定量准确性与护理标准(SOC)。我们将使用低计数的225Ac平面图像并共同配准 SPECT/CT相机采集的低剂量CT图像。将使用物理和数字幻影。 在强劲的初步数据指导下，这一目标将通过追求两个具体目标来实现：1)发展和 验证AER的准确投影平面活度分布重建方法；以及2)比较 我们的方法在225Ac量化任务中使用模拟体模和物理体模的性能 SPECT/CT相机采集的平面/CT数据。该方法将依赖于包含以下内容的物理成像模型 制度内核与正规化。为了控制噪声，我们将使用稀疏性促进正则化 10范数的包络。采用定点逼近算子的方法来求解所得到的 非凸极小化问题。噪声对器官/肿瘤活动估计的影响将被考虑在内。 具有系综均方误差性能度量。偏差和方差的估计权衡将 被探索。模拟的剂量学任务将被用来展示性能的改进。结果： 我们将确定新的平面重建方法已准备好在临床环境中进行测试。 成功的标准被定义为在精确度和精确度方面有实质性(50%)的改进 AER区域活动集中度的估算。这项拟议的研究具有重要意义，因为它 预计AER的直接定量成像将允许TAT的优化个性化设计，包括 活动/分馏时间表。它还将使成像AER替代物的效用得到合理的评估。 最终，这些知识将导致晚期糖尿病患者更高的反应和治愈可能性。 许多癌症的分期。

Project Summary Title: Advanced Planar Image Reconstruction for Targeted Alpha Therapy Targeted Alpha Therapy (TAT) based on the alpha-emitting radiopharmaceuticals (AER) has been recently successfully applied as a treatment for many advanced-stage cancers incurable with conventional methods. However, current methods of imaging the biodistribution of AER are sub-optimal, due to very low administered activity. As a result, AER biodistributions are unknown and hence, absorbed dose distributions for AER are unknown. Thus, our ability to anticipate normal tissue toxicity and prescribe personalized treatment is compromised. Accurate quantitative imaging of the AER biodistribution is necessary to remedy this situation. To address this need, our project’s long-term goal is development of a method for the generation of accurate projection images of AER (proj-AER) using low-count AER planar data and co-registered low-dose CT images acquired on SPECT/CT cameras. The obtained proj-AER combined with CT will permit accurate AER activity estimation that will be used as input data for a dosimetry model, which will provide information on the radiation dose to organs/tumor. The objective of this proposal is to develop a novel sparsity promoting reconstruction method based on a physical model for planar AER imaging and perform a proof-of-concept study of its superior quantitative accuracy vs. standard-of-care (SOC). We will use low-count 225Ac planar images and co-registered low-dose CT images acquired on SPECT/CT cameras. Physical and digital phantoms will be employed. Guided by strong preliminary data, this objective will be attained by pursuing two specific aims: 1) Develop and validate an accurate projected planar activity distribution reconstruction method for AER; and 2) Compare performance of our method with current SOC in 225Ac quantitation tasks using simulated and physical-phantom planar/CT data acquired on SPECT/CT camera. The approach will rely on a physical imaging model containing the system kernels and regularization. To control noise, we will use sparsity promoting regularization with envelope of the l0-norm. The fixed-point proximity-operator approach will be used to solve the resulting nonconvex minimization problem. The effects of noise on organ/tumor activity estimates will be accounted for with an ensemble mean squared error performance metric. The estimation tradeoffs of bias and variance will be explored. Simulated dosimetry tasks will be used to demonstrate performance improvements. Outcomes: We will establish that the novel planar reconstruction method is ready for testing in the clinical environment. The measure of success is defined as substantial (>50%) improvement in precision and accuracy in the estimation of regional activity concentration of AER. The proposed research is significant because it is expected that the direct quantitative imaging of AER will allow optimized personalized design of TAT including activity/fractionation schedule. It will also enable rational assessment of the utility of imaging AER surrogates. Ultimately, such knowledge will lead to higher likelihood of response and cure for patients with advanced stages of many cancers.