Advanced planar image reconstruction for targeted alpha therapy

用于靶向 α 治疗的高级平面图像重建

• 批准号: 10593085
• 负责人: Charles Ross Schmidtlein
• 金额: $ 23.18万
• 依托单位: SLOAN-KETTERING INST CAN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10593085
• 关键词: Address; Alpha Particles; Biodistribution; Cell Cycle Stage; Cells; Clinical; DNA Double Strand Break; Data; Development; Diameter; Distributional Activity; Dose; Dose Rate; Environment; FOLH1 gene; Fractionation; Generations; Glioma; Goals; Happiness; Image; Knowledge; Linear Energy Transfer; Malignant Neoplasms; Malignant neoplasm of urinary bladder; Measures; Mediating; Metastatic Prostate Cancer; Methods; Mission; Modeling; Neuroendocrine Tumors; Noise; Normal tissue morphology; Organ; Outcome; Patient-Focused Outcomes; Patients; Performance; Public Health; Radiation Dose Unit; Radioisotopes; Radiopharmaceuticals; Research; Schedule; Surrogate Markers; System; Testing; Tissues; Toxic effect; United States National Institutes of Health; X-Ray Computed Tomography; absorption; cancer cell; castration resistant prostate cancer; cell killing; cytotoxicity; design; digital; dosimetry; image reconstruction; imaging modality; improved; leukemia; low dose computed tomography; melanoma; meter; novel; personalized medicine; personalized predictions; physical model; prevent; quantitative imaging; reconstruction; response; single photon emission computed tomography; standard of care; success; targeted treatment; therapy design; tumor

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.

项目摘要 标题：用于靶向α治疗的高级平面图像重建 基于α发射放射性药物（AER）的靶向α治疗（达特）最近已被用于治疗癌症。 成功地应用于许多用常规方法无法治愈的晚期癌症的治疗。 然而，目前对AER的生物分布成像的方法是次优的，这是由于非常低的给药浓度。 活动因此，AER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处方个性化治疗的能力是 暴露了AER生物分布的精确定量成像是纠正这种情况所必需的。 为了满足这一需求，我们的项目的长期目标是开发一种方法，用于生成准确的 使用低计数AER平面数据和共配准低剂量CT图像的AER投影图像（proj-AER） 在SPECT/CT摄像机上采集。所获得的proj-AER结合CT将允许准确的AER活性 将用作剂量测定模型的输入数据的估计，该模型将提供关于辐射的信息 器官/肿瘤剂量。这个建议的目的是开发一种新的稀疏促进重建 方法的基础上的物理模型的平面AER成像，并进行了验证的概念研究，其上级 定量准确性与标准治疗（SOC）。我们将使用低计数的225 Ac平面图像， 在SPECT/CT相机上采集的低剂量CT图像。将采用物理和数字幻影。 在强有力的初步数据的指导下，这一目标将通过追求两个具体目标来实现：1）发展和 验证用于AER的精确投影平面活性分布重建方法;以及2）比较 使用模拟和物理体模的225 Ac定量任务中，我们的方法在当前SOC下的性能 在SPECT/CT相机上采集的平面/CT数据。该方法将依赖于一个物理成像模型， 系统内核和正则化。为了控制噪声，我们将使用稀疏性促进正则化， L0范数的包络。固定点邻近算子方法将被用来解决由此产生的 非凸极小化问题将考虑噪声对器官/肿瘤活性估计值的影响 具有集合均方误差性能度量。偏差和方差的估计折衷将 被探索。模拟剂量测定任务将用于证明性能改进。成果： 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。 成功的衡量标准定义为在精密度和准确度方面的实质性（>50%）改善， AER区域活动浓度估算。这项研究之所以重要，是因为 预期AER的直接定量成像将允许达特的优化个性化设计，包括 活动/分级时间表。它也将使合理评估的效用成像AER替代品。 最终，这些知识将导致更高的可能性反应和治愈的患者与先进的 许多癌症的阶段。

项目成果

Multi‐scale cascaded networks for synthesis of mammogram to decrease intensity distortion and increase model‐based perceptual similarity

用于合成乳房X光照片的多尺度级联网络，以减少强度失真并增加基于模型的感知相似性

• DOI: 10.1002/mp.16007
• 发表时间: 2022
• 期刊: Medical Physics
• 影响因子: 3.8
• 作者: Jiang, Gongfa;He, Zilong;Zhou, Yuanpin;Wei, Jun;Xu, Yuesheng;Zeng, Hui;Wu, Jiefang;Qin, Genggeng;Chen, Weiguo;Lu, Yao
• 通讯作者: Lu, Yao