Expert System for Personalized Reconstruction of PET Acquisitions

本研究的目的是验证正电子发射成像特性的两个假设 断层扫描（PET）：（1）可以获得重建量化的实质性改善， 正电子发射断层扫描（PET）系统，通过利用自动化的专家系统， 用于特定患者中的特定病变的量化的最佳算法和重建参数;以及 (2)局部PSF的确定将允许系统的更精确和灵活的使用。重建 是PET成像的重要组成部分。已经开发了许多算法，但是给出 最可靠的SUV测量以复杂的方式取决于采集的许多情况， 包括但不限于计数水平、损伤大小、损伤形状、损伤位置、背景水平和 结构（例如，膀胱附近的病变对肝脏）和患者大小。此外，定量 该重建的响应取决于该方法的参数，诸如迭代次数、点- 扩散函数（PSF）模型参数、滤波和特定病变。我们将合成嵌入 将已知大小、形状、位置和活性浓度的病变合并到现有数据集中。这将使我们能够 了解真相并提取重建的反应。然后我们可以补偿这种反应。在 另外，我们可以处理不同的算法和重建参数，以确定最佳组合 每一个病人的每一个病灶。我们的第二种方法综合嵌入点源数据非常接近 病变，而不是嵌入类似大小的病变。这将给我们两个数据集：有和没有 点源然后，我们将重建这两个集合，并取其差值来估计局部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， 和方差可以确定任意形状的ROI，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：（一）发展和 集成最初的专家系统工具，这些工具将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， 重建参数;（ii）开发一种新的方法，用于使用嵌入式点源来估计 每个患者重建中的PSF作为重建算法及其相关参数的函数， 另一种方法来估计偏差和方差SUV测量;（iii）测试系统与幻影 具有不同大小、形状和SUV值的病变;以及（iv）通过临床上嵌入 根据我们医生的建议，将已知大小、形状和位置的相关病变纳入存档患者 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.