Data Driven Methods for Image Reconstruction in PET

简介(申请人提供)：本计划的长远目标是 体层摄影术在放射诊断和核诊断中的应用 医学，特别是正电子发射断层扫描(PET)。该项目涉及 用于从原始探测器测量结果生成图像的计算机方法 PET扫描仪(即根据投影数据重建图像的算法)。 数据驱动的方法，以原始数据处理每个单独的测量 表单(即列表模式数据)可以从数据中提取更多信息， 与更常见的处理数据的方法相比， 测量空间中的直方图箱。 对于列表模式的数据，目前可用的重建算法包括 这些操作与入库数据的操作基本相同。然而， 这一探索性/开发性方案中的方法和可行性研究 涉及从列表模式数据重建概念上不同的视图 这导致了专门为列表模式数据量身定做的替代操作， 在迭代和非迭代算法中。这些操作是基于 关于测量空间中的光滑局部核(例如，高斯核)，其中 每个核都在线性和角度的有限区域内扩展 参数。这些操作涉及内核的反投影(或 经滤波的核)进入图像空间，以及相应的正向投影 从图像空间进入测量空间的核区域。 该项目旨在开发、实施、测试和评估两者的可行性 使用新的基于核的操作的迭代和非迭代算法 用于从列表模式数据重建图像。 肿瘤的PET成像，特别是全身扫描，受到低成本的限制 在发射和传输数据集中可获得的计数数。 提高定量精度和信噪比性能 临床实用的PET数据采集和处理将导致 改进了癌症的检测、诊断和治疗计划。

DESCRIPTION (Provided by Applicant): The long-term aim of this project is the improvement of tomographic imaging in diagnostic radiology and nuclear medicine, especially positron emission tomography (PET). The project involves computer methods for generating images from the raw detector measurements of PET scanners (i.e., algorithms for image reconstruction from projection data). Data-driven methods that process each individual measurement in its original form (i.e., list-mode data) can extract more information from the data, compared to the more common methods that process data that are accumulated in histogram bins in the measurement space. For list-mode data, the reconstruction algorithms available at present consist of operations that are basically ' the same as those for binned data. However, the methods and feasibility studies in this exploratory/developmental proposal involve a conceptually different view of reconstruction from list-mode data that leads to alternative operations, specifically tailored to list-mode data, within both iterative and non-iterative algorithms. These operations are based on smooth localized kernels (e.g., Gaussians) in the measurement space, where each kernel extends over a limited region in both the linear and angular parameters. The operations involve backprojection of the kernel (or of a filtered kernel) into the image space, and the corresponding forward projection from the image space into the kernel's region of the measurement space. The project aims to develop, implement, test, and evaluate for feasibility both iterative and non-iterative algorithms using the new kernel-based operations for image reconstruction from list-mode data. Cancer imaging with PET, especially whole-body scanning, is limited by the low numbers of counts obtainable in the emission and transmission data sets. Improving the quantitative accuracy and signal-to-noise performance of clinically practical data acquisition and processing in PET would lead to improved detection, diagnosis, and treatment planning of cancer.