Iterative Image reconstruction for high-resolution PET imaging

描述(由申请人提供)：已经开发了用于发射断层扫描的迭代重建算法，其显著改善了过滤反投影方法的图像质量。然而，目前的大多数重建算法都隐含地假设系统模型是精确的。在每一次正反投影中直接使用精确的系统模型所带来的艰巨的计算挑战往往导致人们采用不太准确的模型。这导致重建图像中的噪声增加和分辨率降低，因为在现有方法中无法校正建模误差的影响。这笔赠款的目标是开发一类新的迭代重建方法，以补偿建模误差的影响。这项工作是基于我们对从系统模型中的每个组件到重建图像的错误传播的彻底分析。新方法的创新之处在于，它不需要在每个正反投影中都有一个精确的系统模型。当直接使用精确的系统模型进行迭代重建不可行时，该方法可以获得高分辨率的图像，并在不牺牲图像质量的情况下，通过使用简化的快进快退投影仪来减少重建时间。我们将首先发展具有纠错能力的高分辨率迭代图像重建理论。然后，我们将重点介绍该理论在正电子发射断层扫描(PET)中的应用和验证。我们将在microPET扫描仪上实现新的重建算法，并将使用蒙特卡罗模拟、物理模型实验和真实动物数据来评估病变检测和量化性能。我们相信，新的算法将为理解小动物模型中的人类疾病提供高分辨率的图像和准确的定量信息。一旦成功，我们将把重建算法扩展到临床成像系统，并将该理论应用于其他成像方式，如X射线CT、SPECT、MRI和光学断层扫描。正电子发射断层扫描(PET)是一种广泛应用于临床和生物学研究的功能成像手段。该项目将开发一种新的PET图像重建方法，为理解和治疗人类疾病提供高分辨率图像和准确的定量信息。

DESCRIPTION (provided by applicant): Iterative reconstruction algorithms that significantly improve image quality over filtered backprojection methods have been developed for emission tomography. However, most current reconstruction algorithms implicitly assume that the system model is exact. The daunting computational challenge associated with the direct use of an exact system model in each forward and back projection has often led people to adopt less accurate models. This results in increased noise and reduced resolution in reconstructed images, because the effect of the modeling error cannot be corrected in the existing methods. The goal of this grant is to develop a new class of iterative reconstruction methods that can compensate the effect of modeling error. The work is based on our thorough analysis of error propagation from each component in the system model into reconstructed images. The innovation of the new method is that it does not require an exact system model in every forward and back projection. The method can obtain high-resolution images when direct use of an accurate system model in the iterative reconstruction is impractical, and it can also reduce reconstruction time by using simplified fast forward and back projectors without sacrificing image quality. We will first develop the theory of high-resolution iterative image reconstruction with error correction capability. Then we will focus on the application and validation of the theory in positron emission tomography (PET). We will implement new reconstruction algorithms on microPET scanners, and will evaluate the lesion detection and quantitation performance using Monte Carlo simulations, physical phantom experiments, and real animal data. We believe that the new algorithms will provide high-resolution images and accurate quantitative information for understanding human diseases in small animal models. Upon success, we will extend the reconstruction algorithm to clinical imaging systems and will also apply the theory to other imaging modalities, such as X-ray CT, SPECT, MRI, and optical tomography. Lay abstract: Positron emission tomography (PET) is a functional imaging modality that is widely used in clinical and biological studies. This project will develop a novel image reconstruction method for PET which will provide high-resolution images and accurate quantitative information for understanding and treating human diseases.