Data Driven Methods for Image Reconstruction in PET

PET 图像重建的数据驱动方法

• 批准号: 6620725
• 负责人: ROBERT M LEWITT
• 金额: $ 19.81万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-03-01 至 2004-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6620725
• 关键词: Data; Driven; Methods; Image; Reconstruction

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.

描述（申请人提供）：本项目的长期目标是 放射诊断学和核医学中断层成像改进 医学，尤其是正电子发射断层扫描（PET）。该项目涉及 用于从原始检测器测量产生图像的计算机方法 PET扫描仪（即，用于从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结果 形式（即，列表模式数据）可以从数据中提取更多的信息， 与更常见的处理数据的方法相比， 测量空间中的直方图箱。 对于列表模式数据，目前可用的重建算法包括 这些操作基本上与装箱数据的操作相同。然而，在这方面， 本探索性/开发性提案中的方法和可行性研究 涉及从列表模式数据重构概念上不同的观点 这导致了替代操作，特别是针对列表模式数据， 在迭代和非迭代算法中。这些行动是基于 在平滑局部化核上（例如，在测量空间中，其中 每个核在线性和角向的有限区域上延伸 参数这些操作涉及内核（或 滤波核）到图像空间中，以及相应的前向投影 从图像空间到测量空间的核区域。 该项目旨在开发，实施，测试和评估可行性， 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。 PET的癌症成像，特别是全身扫描，受到低剂量的限制。 在发射和透射数据集中可获得的计数的数量。 提高定量准确度和信噪比性能 PET中的临床实用数据采集和处理将导致 改善癌症的检测、诊断和治疗计划。