Fast image reconstruction in PET from many short-duration frames of data

利用许多短时数据帧在 PET 中快速重建图像

• 批准号: 7701285
• 负责人: ROBERT M LEWITT
• 金额: $ 23.88万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7701285
• 关键词: Animal Model; Automatic Data Processing; Biochemical Pathway; Biological Models; Biomedical Research; Characteristics; Clinical; Clinical Medicine; Clinical Research; Computing Methodologies; Data; Data Set; Development; Diagnosis; Discipline of Nuclear Medicine; Disease; Drug Formulations; Event; Figs - dietary; Generations; Human; Image; Imaging Device; Individual; Information Systems; Kinetics; Lead; Medicine; Methods; Modeling; Monitor; Motion; Neighborhoods; One-Step dentin bonding system; Operative Surgical Procedures; Outcome; Patients; Phase; Photons; Positron-Emission Tomography; Procedures; Process; Property; Public Health; Recording of previous events; Research; Running; Simulate; Solutions; Staging; Stream; System; Testing; Three-Dimensional Image; Time; Tracer; Variant; Weight; Work; base; cancer diagnosis; cancer imaging; computerized data processing; data acquisition; density; design; human disease; human subject; image reconstruction; imaging modality; improved; interest; public health relevance; radiotracer; reconstruction; respiratory; response; tool; treatment planning; uptake

DESCRIPTION (provided by applicant): The overall objective of this project is to improve the quality of images obtained by positron emission tomography (PET) for human studies in clinical nuclear medicine and in biomedical research. This will be done by developing a fast computer method for generating images from basic photon-count data acquired by PET scanners. Two versions of the method will be developed - the first for conventional PET and the second for time-of-flight (TOF) PET. Data from TOF-PET systems contain additional information that permits better spatial localization of coincidence events, compared to conventional (non-TOF) scanners. In some clinical and research applications of PET, an individual study produces a large number of data sets in a time sequence, with each data set containing a relatively small number of counts collected over a short duration of time, and where each data set requires a separate run of an image reconstruction method to generate the corresponding volume image. This situation arises when (a) multiple images are used to determine the uptake of the radiotracer at each voxel as a function of time (time-activity curves), or (b) multiple images are used to do retrospective image-based gating of data acquired continuously in the presence of quasi-cyclic respiratory motion. Generation of a large number of volume images requires a large amount of time when conventional reconstruction methods are used. The proposed image reconstruction method is different from conventional methods in the following respects. (a) The proposed method performs non-iterative, linear operations on the data, whereas conventional methods are iterative, and non-linear. (b) The proposed method shifts the iterative phase of the computation from conventional operations on the data into a preprocessing step that is done once for each scanner-reconstruction geometry, i.e., no iterative computation for each data set. The proposed preprocessing step performs iterative computation to build a set of weight factors to be used for non- iterative operations on multiple data sets, whereas conventional methods perform iterative computation directly on each data set. (c) The proposed method does not aim to be an optimal statistical estimator for processing Poisson-distributed PET data. Instead, the method achieves the desirable property of linear processing of the data, which leads to quantitative unbiased estimates in low-count regions of the image, as required for tracer kinetic studies and for image-based gating using short-duration blocks of data. The proposed method, in common with standard statistical iterative methods, can do event-by-event processing of PET data acquired in list mode, and it can incorporate a system model that is spatially variant. The project is based on this general framework, and involves the development, implementation, and testing using simulated data, of two specific methods, one for conventional PET data and the other for TOF- PET data. PUBLIC HEALTH RELEVANCE: Positron emission tomography is now well established as a valuable imaging tool for biomedical research, for the diagnosis of cancer and other diseases, and for the planning and monitoring of treatment. The proposed work involves a new method for computer processing of data that is designed to improve the accuracy of PET images, especially in clinical and research applications requiring generation of a large number of images in a time sequence. The proposed work is relevant to public health, since an improvement in the accuracy of PET images would lead to more accurate diagnosis of cancer and other diseases, more accurate planning of treatment, and more accurate monitoring of the response to therapy, leading in turn to better patient outcomes.

描述（由申请人提供）：本项目的总体目标是提高正电子发射断层扫描（PET）获得的图像质量，用于临床核医学和生物医学研究中的人体研究。这将通过开发一种快速的计算机方法来实现，该方法用于从PET扫描仪采集的基本光子计数数据生成图像。将开发两种版本的方法-第一种用于常规PET，第二种用于飞行时间（TOF）PET。与传统（非TOF）扫描仪相比，来自TOF-PET系统的数据包含允许更好地空间定位符合事件的附加信息。在PET的一些临床和研究应用中，个体研究在时间序列中产生大量数据集，每个数据集包含在短持续时间内收集的相对少量的计数，并且其中每个数据集需要单独运行图像重建方法以生成对应的体积图像。当（a）使用多个图像来确定作为时间的函数的每个体素处的放射性示踪剂的摄取（时间-活性曲线），或者（B）使用多个图像来对在存在准周期性呼吸运动的情况下连续采集的数据进行基于回顾性图像的门控时，出现这种情况。当使用常规重建方法时，大量体积图像的生成需要大量时间。所提出的图像重建方法在以下方面不同于常规方法。(a)所提出的方法对数据执行非迭代、线性运算，而传统方法是迭代和非线性的。(b)所提出的方法将计算的迭代阶段从对数据的常规操作转移到预处理步骤，该预处理步骤针对每个扫描器重建几何结构进行一次，即，对每个数据集没有迭代计算。所提出的预处理步骤执行迭代计算以构建用于对多个数据集进行非迭代操作的权重因子集合，而常规方法直接对每个数据集执行迭代计算。(c)所提出的方法并不旨在成为一个最佳的统计估计处理泊松分布的PET数据。相反，该方法实现了数据的线性处理的理想特性，这导致在图像的低计数区域中的定量无偏估计，如示踪剂动力学研究和使用短持续时间数据块的基于图像的门控所需的。所提出的方法，在共同的标准统计迭代方法，可以做事件的PET数据采集列表模式下的事件处理，它可以纳入一个系统模型，是空间上的变化。该项目是基于这一总体框架，并涉及开发，实施和测试使用模拟数据，两种具体的方法，一种用于传统的PET数据，另一种用于TOF- PET数据。公共卫生关系：正电子发射断层扫描是一种非常有价值的成像工具，用于生物医学研究、癌症和其他疾病的诊断以及治疗的计划和监测。拟议的工作涉及一种新的计算机处理数据的方法，旨在提高PET图像的准确性，特别是在临床和研究应用中，需要在一个时间序列中生成大量的图像。拟议的工作与公共卫生有关，因为PET图像准确性的提高将导致更准确地诊断癌症和其他疾病，更准确地规划治疗，更准确地监测对治疗的反应，从而导致更好的患者结果。