An Integrated Statistical Framework for Lesion Detection Using Dynamic PET

描述（申请人提供）：FDG正电子发射断层扫描（PET）已成为临床广泛接受和使用的疾病诊断、分期、治疗计划、管理和评估的分子成像工具。尽管传统的静态PET成像在肿瘤检测方面具有很高的灵敏度，但进一步的改进是很重要的，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变与背景对比度，静态图像的目视检查对小肿瘤可能是不准确的。计算机辅助检测（CAD）结合动态PET数据的使用有助于提高对这些小病变的敏感性和特异性。这项探索性生物工程研究拨款提案的目标是研究动态FDG PET的CAD方法，该方法在统计框架中集成了图像重建，病变检测和阈值处理。该方法将根据动态PET数据和成像系统的特性进行优化，并设计为使用标准的动态数据集，而无需测量血液输入功能。CAD系统将自动提供显示可能病变位置的体素统计图。通过使用结合空间和时间信息的统计检测算法，我们期望能够提高对在标准静态扫描中不清晰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。我们将把我们的最大后验（MAP）方法应用于PET图像重建，用于新一代临床扫描仪的数据，并根据扫描仪的特点优化建模和校准程序的性能。估计的动态示踪剂摄取的结果图像，以及它们的近似协方差，基于重建算法的理论分析计算，将被用作输入到匹配的子空间检测器。该检测器使用线性子空间结合广义似然比测试来表征典型肿瘤和正常组织动力学，以生成显示肿瘤存在或不存在可能性的体素统计图。典型的肿瘤和正常组织子空间将使用由放射科医生识别的具有肿瘤和正常组织感兴趣区域（roi）的多个受试者的训练数据集来获得。然后，统计检测图将被阈值化，以获得可能肿瘤位置的体素指示，同时控制多重比较的影响。我们将使用西门子Biograph TruePoint扫描仪对USC收集的动态数据进行CAD方法的实施、优化和初步评估。评估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临床试验的结直肠癌肝转移患者身上。系列影像学研究，随后的手术切除，通过病理和术中超声独立验证，将为评估我们的CAD检测方法的性能提供基础。

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.