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（母版） 选择性内放射治疗（SIRT）优先输送90 Y微球到靶病变， 在肝细胞癌（HCC）的治疗中显示出有希望的反应率和有限的毒性， 世界上最大的癌症死亡原因。然而，要实现更持久的对策， 改善/调整治疗，以确保所有病变和病变子区域均接受足够的辐射输送。 虽然体外立体定向放射治疗（SBRT）非常适合于较小的孤立性HCC，但其 对于较大或多病灶疾病的应用受到正常肝脏的辐射耐受性的挑战 薄壁组织一种剂量测定指导的综合方法，利用内部和外部的互补优势， 外部放射递送可以预期改善HCC的治疗。然而，为了实现这一转变， 需要建立安全性的前瞻性临床试验。此外，对于常规临床使用， 体内放射性核素治疗中的体素水平剂量估计，其落后于体外射束治疗剂量测定， 仍然需要。我们的长期目标是通过个体化剂量测定来提高放射治疗的疗效 指导治疗。我们在本申请中的目的是证明可以使用基于90 Y成像的 SIRT后的吸收剂量估计，以安全地将外部辐射输送到目标区域（体素）， 预测剂量不足，并开发基于深度学习的工具， 常规临床使用的实用估计。具体而言，在目标1中，我们将在HCC中进行1期临床试验 我们将采用新方法使用SIRT后90 Y PET/CT衍生吸收剂量图的患者 根据先前确定的剂量反应，将SBRT递送至预测剂量不足的肿瘤区域 模型该试验的主要目的是获得未来SIRT+SBRT联合治疗的安全性估计。 II期试验设计。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 用于90 Y PET/CT和SPECT/CT重建、关节重建-分割和散射的基于工具 在低计数率设置下的估计，通常用于90 Y。这些方法具有物理/数学 基础，其中卷积神经网络（CNN）被包括在迭代重建过程中， 而不是重建后去噪。在目标3中，我们将开发用于快速体素级剂量测定的CNN， 联合收割机与Aim 2的CNN相结合，开发具有统一剂量学任务的创新端到端框架 基础训练。在这项研究结束时，我们将准备在第二阶段试验中使用新的深度学习工具， 与单独使用SIRT相比，SIRT+SBRT的疗效增强，并朝着我们长期的目标迈进。 长期目标这将加速这些下一代工具在临床实践中的采用， 显著的积极影响，因为基于患者特定剂量测定的治疗将大大改善 疗效，与目前的标准实践SIRT。虽然我们专注于90 Y SIRT，但我们的工具将 一般适用于放射性核素治疗，是一种快速发展的治疗选择。