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An Integrated Statistical Framework for Lesion Detection Using Dynamic PET

使用动态 PET 进行病变检测的综合统计框架

基本信息

批准号：
8421579
负责人：
Quanzheng Li
金额：
$ 17.09万
依托单位：
MASSACHUSETTS GENERAL HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-03-01 至 2014-02-28
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8421579
关键词：
Integrated Statistical Framework Lesion Detection

项目摘要

DESCRIPTION (provided by applicant): Positron emission tomography (PET) with FDG has become a widely accepted and used clinical molecular imaging tool for disease diagnosis, staging, treatment planning, management and evaluation. Although conventional static PET imaging provides high sensitivity in tumor detection, further improvement is important since even a small percentage of false negatives can have a major impact on treatment, cost and outcome. Visual inspection of static images is potentially inaccurate for small tumors due to limited spatial resolution and low lesion-to-background contrast. Computer aided detection (CAD) combined with use of dynamic PET data could assist in improving sensitivity and specificity for these small lesions. The goal of this exploratory Bioengineering Research Grant proposal is to investigate such a CAD method for dynamic FDG PET that integrates image reconstruction, lesion detection and thresholding in a statistical framework. The method will be optimized based on the properties of the dynamic PET data and the imaging system, and is designed to use standard dynamic data sets without the need for a measured blood input function. The CAD system will automatically provide a voxel-wise statistical map indicating probable lesion locations. By using a statistical detection algorithm that combines spatial and temporal information, we expect to be able to improve detection of small lesions that are not clearly visible in standard static scans and thereby provide improved diagnostic information to the radiologist. We will apply our maximum a posteriori (MAP) approach to PET image reconstruction to data from the new generation of clinical scanners, and optimize performance in terms of modeling and calibration procedures based on the characteristics of the scanner. The resulting images of estimated dynamic tracer uptake, as well as their approximate covariance, computed based on a theoretical analysis of the reconstruction algorithm, will be used as input to a matched subspace detector. This detector characterizes typical tumor and normal tissue dynamics using linear subspaces in combination with a generalized likelihood ratio test, to generate a voxel-wise statistical map indicating the likelihood of tumor presence or absence. Typical tumor and normal tissue subspaces will be obtained using a training dataset from multiple subjects with tumor and normal tissue regions of interest (ROIs) identified by a radiologist. The statistical detection map will then be thresholded to obtain a voxel-wise indication of likely tumor locations, while controlling for the effects of multiple comparisons. We will implement, optimize and perform preliminary evaluation of this CAD approach for dynamic data collected at USC using the Siemens Biograph TruePoint scanner. Evaluation will use Monte Carlo simulation and retrospective human studies. Human studies will focus on patients with liver metastases from colorectal cancer who are enrolled in an ongoing clinical trial. Serial imaging studies, with subsequent surgical resection and independent verification through pathology and intraoperative ultrasound, will provide a basis to evaluate the performance of our CAD detection approach. PUBLIC HEALTH RELEVANCE: Positron Emission Tomography (PET) has been widely used in cancer diagnosis, staging, treatment planning, management and evaluation. One of the main functions of PET is to detect tumors and metastatic lesions, which is conventionally done by visual inspection of a static volumetric image by a radiologist. This project is focused on using multiple images of the patient collected in a single session, in combination with a novel computer aided detection (CAD) method, to assist radiologists in detecting small tumors that may not be clearly visible using standard imaging protocols. Success of this project may lead to improved detection, staging and monitoring of metastatic disease.

描述（由申请人提供）：FDG正电子发射断层扫描（PET）已成为一种广泛接受和使用的临床分子成像工具，用于疾病诊断、分期、治疗计划、管理和评价。尽管传统的静态PET成像在肿瘤检测中提供了高灵敏度，但进一步改进是重要的，因为即使是很小比例的假阴性也会对治疗、成本和结果产生重大影响。由于有限的空间分辨率和低的病灶与背景对比度，静态图像的目视检查对于小肿瘤可能不准确。计算机辅助检测（CAD）结合使用动态PET数据可以帮助提高这些小病变的灵敏度和特异性。这项探索性生物工程研究资助提案的目标是研究动态FDG PET的CAD方法，该方法将图像重建、病变检测和阈值处理集成在统计框架中。该方法将根据动态PET数据和成像系统的特性进行优化，并设计为使用标准动态数据集，而无需测量的血液输入函数。CAD系统将自动提供指示可能的病变位置的逐体素统计图。通过使用结合了空间和时间信息的统计检测算法，我们期望能够改进在标准静态扫描中不清楚可见的小病变的检测，从而为放射科医生提供改进的诊断信息。我们将应用我们的最大后验概率（MAP）的方法，PET图像重建的数据，从新一代的临床扫描仪，并优化性能的建模和校准程序的基础上的扫描仪的特点。基于重建算法的理论分析计算的估计动态示踪剂摄取的所得图像以及它们的近似协方差将用作匹配子空间检测器的输入。该检测器使用线性子空间结合广义似然比检验来表征典型的肿瘤和正常组织动态，以生成指示肿瘤存在或不存在的可能性的逐体素统计图。将使用来自多个受试者的训练数据集获得典型的肿瘤和正常组织子空间，这些受试者具有由放射科医师识别的肿瘤和正常组织感兴趣区域（ROI）。然后将对统计检测图进行阈值处理，以获得可能的肿瘤位置的体素指示，同时控制多重比较的影响。我们将使用Siemens Biograph TruePoint扫描仪对在南加州大学收集的动态数据实施、优化和初步评估这种CAD方法。评价将使用蒙特卡罗模拟和回顾性人体研究。人体研究将集中在正在进行的临床试验中招募的结直肠癌肝转移患者。系列成像研究，随后的手术切除和独立的验证，通过病理和术中超声，将提供一个基础，以评估我们的CAD检测方法的性能。公共卫生相关性：正电子发射断层扫描（PET）已广泛应用于癌症的诊断、分期、治疗计划、管理和评估。PET的主要功能之一是检测肿瘤和转移性病变，这通常通过放射科医师对静态体积图像的视觉检查来完成。该项目的重点是使用在一次会议中收集的患者的多个图像，结合一种新的计算机辅助检测（CAD）方法，以帮助放射科医生检测使用标准成像协议可能无法清晰可见的小肿瘤。该项目的成功可能会改善转移性疾病的检测、分期和监测。