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

描述(申请人提供)：该项目的总体目标是改善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，该方法可以从PET扫描仪获取的基本光子计数数据生成图像。该方法将开发两个版本-第一个用于常规PET，第二个用于飞行时间(TOF)PET。与传统(非TOF)扫描仪相比，TOF-PET系统的数据包含更多信息，可以更好地对符合事件进行空间定位。在PET的一些临床和研究应用中，单个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较少数量的计数，并且其中每个数据集需要单独运行图像重建方法以生成相应的体积图像。当(A)使用多个图像来确定作为时间(时间-活度曲线)的函数的每个体素处的放射性示踪剂的摄取时，或者(B)使用多个图像来对在存在准循环呼吸运动的情况下连续采集的数据进行基于回溯图像的选通时，就会出现这种情况。当使用传统的重建方法时，生成大量的体图像需要大量的时间。本文提出的图像重建方法与传统的图像重建方法在以下几个方面有所不同。(A)所提出的方法对数据执行非迭代的线性运算，而传统方法是迭代的和非线性的。(B)该方法将计算的迭代阶段从对数据的常规操作转移到对每个扫描器重建几何图形只做一次的预处理步骤，即不对每个数据集进行迭代计算。所提出的预处理步骤执行迭代计算以构建用于多个数据集上的非迭代运算的权重因子集合，而传统方法直接在每个数据集上执行迭代计算。(C)建议的方法并不旨在成为处理泊松分布正电子发射计算机断层扫描数据的最优统计估计器。取而代之的是，该方法实现了数据的线性处理的理想特性，这导致了在图像的低计数区域中的定量无偏估计，这是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选通所需的。与标准的统计迭代方法一样，该方法可以对以列表模式采集的PET数据进行逐个事件的处理，并且可以结合空间变化的系统模型。该项目以这一总体框架为基础，涉及使用模拟数据开发、实施和测试两种具体方法，一种用于常规PET数据，另一种用于TOF-PET数据。与公众健康相关：正电子发射断层扫描现已成为生物医学研究、癌症和其他疾病的诊断以及治疗计划和监测的宝贵成像工具。这项工作涉及一种新的计算机处理数据的方法，旨在提高PET图像的精度，特别是在需要按时间序列生成大量图像的临床和研究应用中。这项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对癌症和其他疾病的更准确诊断，更准确的治疗计划，以及更准确的治疗反应监测，进而导致更好的患者结果。

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.