Quantitative Image Quality for Optimization of MRI

描述(由申请人提供)： 我们建议定量评估磁共振成像(MRI)中的图像质量，并使用它来评估和优化几乎无限数量的获取和重建图像的方法。在快速磁共振成像中，人们可以增加时间来改善图像质量，其方式类似于增加X射线和核成像的剂量。一种明智的权衡需要图像质量的定量测量，到目前为止还没有在磁共振成像中使用。我们将重点介绍快速磁共振成像中的重要变量，包括与采集采样和重建相关的变量。就像在x射线和核成像中所做的那样，我们将使用人类观察者和孤立病理的模型检测来评估图像质量。然而，由于许多原因，可探测性是不够的，例如，因为多个图像特征是重要的，并且因为目标可能是用于干预到容易看到的目标的针的引导。作为评估快速MR图像质量的第二种可能也是更有用的方法，我们将使用我们实验室开发的知觉差分计算机模型(PDM)，该模型确定高质量参考图像与更快获得但可能退化的MR图像之间的视觉差异。较低的PDM值表示图像的相似性，并且有一个阈值，低于该阈值，人们无法区分两者之间的区别。我们假设这两种定量图像质量方法将有助于评估和优化MR图像。考虑到大的参数空间和图像的数量，用于评估图像质量的计算机方法(如pdm)是重要的；例如，螺旋研究可以容易地生成数千个不同的图像，排除了传统的人类-观察者ROC分析。我们将在螺旋、径向和联合采集技术(CAT)快速成像方法中精选的重要设计问题上演示该方法。当这项研究成功完成后，我们将建立有用的工具来评估和优化几乎无限数量的磁共振成像方法。

DESCRIPTION (provided by applicant): We propose to quantitatively assess image quality in magnetic resonance imaging (MRI) and use it to evaluate and optimize the almost unlimited number of ways to acquire and reconstruct an image. In fast MR, one can increase time to improve image quality in a manner analogous to the way that one can increase dose in x-ray and nuclear imaging. An informed trade-off requires a quantitative measure of image quality, heretofore not used in MR imaging. We will focus on important variables in fast MR imaging, including those associated with acquisition sampling and reconstruction. As done in x-ray and nuclear imaging, we will use human observer and model detection of isolated pathologies to assess image quality. However, detectability is insufficient for many reasons, e.g, because multiple image features are important and because the goal might be guidance of a needle for intervention to a readily seen target. As a second, and probably more useful, method for evaluating image quality in fast MR, we will use a perceptual difference computer model (PDM) developed in our laboratory that determines the visual difference between a high quality reference image and a more quickly obtained, but possibly degraded, MR image. A low PDM score indicates similarity of the images, and there is a threshold, below which, one cannot tell the difference between the two. We hypothesize that these two quantitative image quality approaches will be useful for assessing and optimizing MR images. A computer method such as PDM for assessing image quality is significant given the large parameter space and numbers of images; e.g., a spiral study can easily generate thousands of different images, precluding a conventional human-observer ROC analysis. We will demonstrate the method on selected, important design issues in spiral, radial, and combined acquisition technique (CAT) fast imaging methods. When this research is successfully completed, we will have established useful tools for evaluating and optimizing the almost unlimited number of MR imaging methods.