Expert System for Personalized Reconstruction of PET Acquisitions

这项提议的目的是检验关于正电子发射成像特性的两个假设。 断层扫描(PET)：(1)可在重建量化方面获得实质性改进 正电子发射断层扫描(PET)系统，利用一个自动化的专家系统来确定 对特定患者的特定病变进行量化的最佳算法和重建参数；以及 (2)确定当地的PSF将允许更准确和灵活地使用该系统。重建 是PET成像的重要组成部分。已经开发了许多算法，但给出的算法 最可靠的SUV测量以一种复杂的方式取决于收购的许多情况， 包括-但不限于-计数水平、病变大小、病变形状、病变位置、背景水平和 结构(例如，靠近膀胱和肝脏的病变)，以及患者的大小。此外，数量上的 重建的响应取决于该方法的参数，例如迭代次数、点- 扩散函数(PSF)模型参数、滤波和特定病变。我们将综合嵌入 将已知大小、形状、位置和活动浓度的病变添加到现有数据集中。这将使我们能够 了解真相，提取重建的回应。然后我们可以对这种反应进行补偿。在……里面 此外，我们可以处理不同的算法和重建参数，以确定最佳组合 对于每个患者的每一处病变。我们的第二种方法将点源数据综合地嵌入到非常接近 病变，而不是植入类似大小的病变。这将给我们提供两个数据集：带和不带 点源。然后我们将重建这两个集合，并取其差值来估计本地PSF 重建空间。该局部PSF可以在重建空间中与估计的病变形状卷积 以计算ROI的估计偏差和噪声。第二种方法的优点是修正 可以为任意形状的ROI确定方差，但缺点是更多的处理-和 可能需要错误传播。这项建议的具体目标包括：(1)发展和 整合最初的专家系统工具，以允许图形用户输入和执行 使用不同的重建算法和适当的范围进行病变的集合 重建参数；(Ii)开发一种新的方法，使用嵌入的点源来估计 PSF在每个患者的重建中作为重建算法及其相关参数的函数 估计SUV测量中偏差和方差的另一种方法；(Iii)用模体测试系统 具有不同大小、形状和SUV值的病变；以及(Iv)通过临床植入来测试该系统 已知大小、形状和位置的相关病变，根据我们的医生的建议，进入存档患者 数据。

The objective of this proposal is to test two hypotheses of the imaging characteristics of positron emission tomography (PET): (1) that substantial improvements in reconstruction quantification can be obtained for positron emission tomography (PET) systems by utilizing an automated, expert system that determines the best algorithm and reconstruction parameters for quantification for a particular lesion in a particular patient; and (2) that determination of the local PSF will allow more accurate and flexible use of the system. Reconstruction is an essential component of PET imaging. Many algorithms have been developed, but the algorithm that gives the most reliable SUV measurement depends in a complicated way on many circumstances of the acquisition, including – but not limited to – count level, lesion size, lesion shape, lesion location, background level and structure (e.g., a lesion near the bladder versus the liver), and patient size. In addition, the quantitative response of that reconstruction depends on parameters of that approach, such as iteration number, point- spread function (PSF) model parameters, filtering, and the particular lesion. We will synthetically embed lesions of known size, shape, location, and activity concentration into an existing data set. This will allow us to know the truth and extract the response of the reconstruction. We can then compensate for this response. In addition, we can process different algorithms and reconstruction parameters to determine the best combination for each lesion in each patient. Our second approach synthetically embeds point-source data very near the lesion, as opposed to embedding a lesion of similar size. This will give us two data sets: with and without the point source. We will then reconstruct both sets and take the difference to estimate the local PSF in reconstruction space. This local PSF can be convolved in reconstruction space with the estimated lesion shape to calculate the estimated bias and noise for an ROI. This second method has the advantage that corrections and variance can be determined for arbitrarily shaped ROIs, but the disadvantage that more processing – and perhaps error propagation – is needed. The specific aims of this proposal include: (i) developing and integrating the initial expert-system tools that will allow for graphical user input and for the execution of ensembles of lesions with the use of different reconstruction algorithms and appropriate ranges for reconstruction parameters; (ii) developing a new method for using embedded point sources to estimate the PSF in each patient's reconstruction as a function of reconstruction algorithm and its associated parameters as an alternative way to estimate bias and variance in SUV measurements; (iii) testing the system with phantoms that have lesions of different size, shape, and SUV values; and (iv) testing the system by embedding clinically relevant lesions of known size, shape, and location, as recommended by our physicians, into archival patient data.