Enhancing low count PET and SPECT imaging with deep learning methods

利用深度学习方法增强低计数 PET 和 SPECT 成像

• 批准号: 10403701
• 负责人: YUNI K DEWARAJA
• 金额: $ 8.28万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-15 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10403701
• 关键词: 90Y; Adoption; Cancer Etiology; Cessation of life; Clinic; Clinical Trials; Disease; Dose; Ensure; External Beam Radiation Therapy; Foundations; Future; Goals; Image; Joint repair; Lesion; Liver parenchyma; Maps; Mathematics; Methods; Microspheres; Modeling; PET/CT scan; Parents; Patient-Focused Outcomes; Patients; Phase I Clinical Trials; Physics; Positron-Emission Tomography; Primary carcinoma of the liver cells; Process; Public Health; Radiation Tolerance; Radiation therapy; Radioembolization; Radionuclide therapy; Safety; Testing; Toxic effect; Training; base; clinical practice; convolutional neural network; deep learning; denoising; dosimetry; improved; innovation; internal radiation; learning strategy; next generation; novel; novel strategies; phase II trial; prospective; radiation delivery; reconstruction; response; single photon emission computed tomography; standard of care; tool; trial design; tumor

Abstract (Parent) Selective internal radiation therapy (SIRT) with preferential delivery of 90Y microspheres to target lesions has shown promising response rates with limited toxicity in the treatment of hepatocellular (HCC), the second leading cause of cancer death in the world. However, to achieve more durable responses, there is much room to improve/adapt the treatment to ensure that all lesions and lesion sub-regions receive adequate radiation delivery. While externally delivered stereotactic body radiation therapy (SBRT) is well suited for smaller solitary HCC, its application for larger or multifocal disease is challenged by the radiation tolerance of the normal liver parenchyma. A dosimetry guided combined approach that exploits complementary advantages of internal and external radiation delivery can be expected to improve treatment of HCC. To make this transition, however, prospective clinical trials establishing safety are needed. Furthermore, for routine clinic use, accurate and fast voxel-level dose estimation in internal radionuclide therapy, that lags behind external beam therapy dosimetry, is still needed. Our long-term goal is to improve the efficacy of radiation therapy with personalized dosimetry guided treatment. Our objective in this application is to demonstrate that it is possible to use 90Y imaging based absorbed dose estimates after SIRT to safely deliver external radiation to target regions (voxels) that are predicted to be underdosed and to develop deep learning based tools to make voxel-level internal dose estimation practical for routine clinic use. Specifically, in Aim 1, we will perform a Phase 1 clinical trial in HCC patients where we will take the novel approach of using the 90Y PET/CT derived absorbed dose map after SIRT to deliver SBRT to tumor regions predicted to be underdosed based on previously established dose-response models. The primary objective of the trial is to obtain estimates of safety of combined SIRT+SBRT for future Phase II trial design. In parallel, in Aim 2, building on promising initial results we will develop novel deep learning based tools for 90Y PET/CT and SPECT/CT reconstruction, joint reconstruction-segmentation and scatter estimation under the low count-rate setting, typical for 90Y. These methods have a physics/mathematics foundation, where convolutional neural networks (CNNs) are included within the iterative reconstruction process, instead of post-reconstruction denoising. In Aim 3, we will develop a CNN for fast voxel-level dosimetry and combine with the CNNs of Aim 2 to develop an innovative end-to-end framework with unified dosimetry-task based training. At the end of this study, we will be ready to use the new deep learning tools in a Phase II trial to demonstrate enhanced efficacy with SIRT+SBRT compared with SIRT alone and advance towards our long- term goal. This will accelerate adoption of these next-generation tools in clinical practice and will have a significant positive impact because treatment based on patient specific dosimetry will substantially improve efficacy, compared with current standard practice in SIRT. Although we focus on 90Y SIRT, our tools will be applicable in radionuclide therapy in general, a rapidly advancing treatment option.

摘要(父级) 优先向靶区输送90Y微球的选择性内放射治疗(SIRT) 显示有希望的应答率和有限的毒性治疗肝细胞癌(肝癌)，第二大 是世界上癌症死亡的主要原因。然而，为了实现更持久的回应，还有很大的空间可以 改进/调整治疗，以确保所有病变和病变亚区域都能得到足够的辐射。 虽然体外立体定向全身放射治疗(SBRT)非常适合较小的孤立性肝癌，但其 正常肝脏的辐射耐受性对较大或多灶性疾病的应用构成了挑战 薄壁组织。一种剂量学引导的组合方法，利用内部和外部的优势互补 体外放射治疗有望改善肝细胞癌的治疗。然而，为了实现这一转变， 确定安全性的前瞻性临床试验是必要的。此外，对于常规临床使用，准确和快速 体内放射性核素治疗中的体素水平剂量估计，它落后于外部射线治疗剂量学， 仍然是需要的。我们的长期目标是通过个人化剂量测量提高放射治疗的疗效。 引导治疗。我们在本应用程序中的目标是演示使用基于90Y成像的 SIRT后安全地将外部辐射传递到目标区域(体素)的吸收剂量估计 预测剂量不足，并开发基于深度学习的工具来制作体素级别的内剂量 对临床常规应用有实用价值。具体地说，在目标1中，我们将对肝细胞癌进行一期临床试验 我们将在SIRT后采用新的方法使用90YPET/CT得出的吸收剂量图的患者 根据先前建立的剂量-反应关系，将SBRT输送到预计剂量不足的肿瘤区域 模特们。试验的主要目标是获得未来SIRT+SBRT联合治疗的安全性评估 第二阶段试验设计。同时，在目标2中，我们将在有希望的初步结果的基础上开发新的深度学习 基于工具的90年PET/CT和SPECT/CT重建、关节重建-分割和散布 在低计数率设置下的估计，通常为90年。这些方法有物理[数学]学 基金会，其中卷积神经网络(CNN)被包括在迭代重建过程中， 而不是重建后的去噪。在目标3中，我们将开发用于快速体素级剂量测量的CNN和 与AIM 2的CNN相结合，开发具有统一剂量测量任务的创新端到端框架 以培训为基础。在本研究结束时，我们将准备在第二阶段试验中使用新的深度学习工具 证明与单独使用SIRT相比，SIRT+SBRT具有更强的疗效，并朝着我们的长期 学期目标。这将加速这些下一代工具在临床实践中的采用，并将 显著的积极影响，因为基于患者特定剂量学的治疗将显著改善 疗效，与目前SIRT的标准实践相比较。虽然我们专注于90年的SIRT，但我们的工具将是 一般适用于放射性核素治疗，这是一种快速发展的治疗选择。