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MR中，可以以类似于可以在X射线和核成像中增加剂量的方式来增加时间以改善图像质量。知情的权衡需要图像质量的定量测量，迄今为止未在MR成像中使用。我们将重点关注快速MR成像中的重要变量，包括与采集采样和重建相关的变量。正如在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变的模型检测来评估图像质量。然而，由于许多原因，可检测性是不足的，例如，因为多个图像特征是重要的，并且因为目标可能是引导针以用于对容易看到的目标进行干预。作为第二个，可能更有用的，在快速MR的图像质量评价方法，我们将使用感知差异计算机模型（PDM）在我们的实验室开发，确定高质量的参考图像和更快地获得，但可能退化，MR图像之间的视觉差异。低PDM分数表示图像的相似性，并且存在阈值，低于该阈值，人们无法区分两者之间的差异。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数量，用于评估图像质量的诸如PDM的计算机方法是重要的;例如，螺旋研究可以很容易地产生数千个不同的图像，排除了传统的人类观察者ROC分析。我们将展示在螺旋，径向和联合采集技术（CAT）快速成像方法的选择，重要的设计问题的方法。当这项研究成功完成时，我们将建立有用的工具来评估和优化几乎无限数量的MR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.