Optimization of PET Image Reconstruction for Lesion Detection

用于病变检测的 PET 图像重建优化

基本信息

批准号：
10041119
负责人：
Kuang Gong
金额：
$ 9.43万
依托单位：
MASSACHUSETTS GENERAL HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-01 至 2022-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10041119
关键词：
Address Adult Algorithms Awareness Biological Models Clinic Clinical Computer Simulation Data Data Set Detection Diagnosis Distant Metastasis Dose Early Diagnosis Enhancing Lesion FOLH1 gene Gallium Goals Half-Life Image Incidence Injections Label Lead Lesion Location Malignant Neoplasms Malignant neoplasm of prostate Measures Methodology Methods Modeling Neuroendocrine Tumors Noise Oncology Outcome Output Patients Performance Phase Physics Play Positron Positron-Emission Tomography Prevalence Protocols documentation Radionuclide Imaging Reader Recovery Recurrence Resolution Role Savings Sensitivity and Specificity Staging Testing Time Tracer Training United States Validation Vendor X-Ray Computed Tomography base cancer type deep learning denoising effectiveness validation experience fluorodeoxyglucose image reconstruction imaging modality improved learning strategy molecular imaging neural network neuroendocrine differentiation novel outcome forecast pediatric patients pentetreotide radiologist radiotracer reconstruction routine imaging success treatment optimization treatment planning tumor

项目摘要

Optimization of PET Image Reconstruction for Lesion Detection Abstract PET is a molecular imaging modality widely used in oncology studies due to its high sensitivity and the potential of early diagnosis. For neuroendocrine tumors (NETs), 68Ga-DOTATATE PET has been recently used in clinical routine for imaging NETs in adult and pediatric patients since 2016. It plays an important role in the diagnosis and staging of NETs. However, compared to 18F-FDG PET, the image quality of 68Ga-DOTATATE PET is lower due to much larger positron range, shorter half-life, and lower dose administration limited by generator capacity. All of these compromises the lesion detectability of 68Ga-DOTATATE PET, especially for small lesions, and can potentially lead to inaccurate NET diagnosis. As 68Ga-DOTATATE PET is increasingly used in clinics, there is an urgent and unmet need to further optimize 68Ga-DOTATATE PET/CT imaging for NET detection. Recently, data-driven methods have been developed for PET image denoising, where the PET system model is not considered. As the tumor-to-background ratio of 68Ga-DOTATATE PET is greater than 18F-FDG PET, the lesion recovery of 68Ga-DOTATATE PET can be hugely influenced by the smoothing effects as well as potential mismatches between training and testing datasets. In this study, we propose a novel data- informed and lesion detection-driven image reconstruction framework. The PET system model, image denoising module, and lesion-detection module will all be included in this reconstruction framework. The two specific aims of this exploratory proposal are (1) to develop a lesion detection-driven PET image reconstruction framework and validate it based on comprehensive computer simulations, (2) to apply the proposed reconstruction framework to existing clinical 68Ga-DOTATATE PET/CT datasets and test it based on various figure-of-merits. We expect that the integrated outcome of the specific aims will be a novel and robust image reconstruction framework to better recover lesions in a 68Ga- DOTATATE PET scan, which is essential for NET managements.

用于病变检测的 PET 图像重建优化抽象的 PET 是一种广泛应用于肿瘤学研究的分子成像方式，因为它具有高灵敏度和早期诊断的潜力。对于神经内分泌肿瘤 (NET)，68Ga-DOTATATE PET 已被自 2016 年以来，最近在成人和儿童患者的 NET 成像临床常规中使用。在 NET 的诊断和分期中发挥着重要作用。然而，与 18F-FDG PET 相比，图像 68Ga-DOTATATE PET 的质量较低，因为正电子射程更大、半衰期更短，并且剂量管理受发生器容量限制。所有这些都会影响病变的可检测性 68Ga-DOTATATE PET，特别是对于小病变，可能会导致 NET 不准确诊断。随着68Ga-DOTATATE PET越来越多地应用于临床，存在着迫切且未满足的需求进一步优化 68Ga-DOTATATE PET/CT 成像以进行 NET 检测。最近，数据驱动 PET 图像去噪的方法已经开发出来，而 PET 系统模型还没有经过考虑的。由于 68Ga-DOTATATE PET 的肿瘤与背景比大于 18F-FDG PET， 68Ga-DOTATATE PET 的病变恢复也会受到平滑效果的巨大影响作为训练和测试数据集之间潜在的不匹配。在这项研究中，我们提出了一种新颖的数据—— 知情且病变检测驱动的图像重建框架。 PET系统模型、图像去噪模块和病变检测模块都将包含在这个重建框架中。该探索性提案的两个具体目标是 (1) 开发病变检测驱动的 PET 图像重建框架并基于全面的计算机模拟对其进行验证，（2）将提出的重建框架应用于现有的临床 68Ga-DOTATATE PET/CT 数据集并根据各种品质因数对其进行测试。我们期望具体目标的综合成果将是一个新颖且强大的图像重建框架，以更好地恢复 68Ga- DOTATATE PET 扫描，这对于 NET 管理至关重要。