MEASUREMENT OF DIFFERENTIAL IMAGE QUALITY

描述（改编自申请人的摘要）：本发明的目的是 研究的目的是开发一种方法，用于确定诊断差异， 在任何两个模态A和B之间的任务性能。新技术是 称为配对图像（1）观察者性能方法。 虽然研究 将与X射线图像，1方法预计将有更广泛的 适用性（例如，CT对比MRI）。 在1方法中，向读者显示一个 同一受试者的一对图像，来自每种模态。 观察员 选择指定诊断任务的上级图像， 分配等级（A>>B，A>B，A=B，A<B，A B）。 本实验分析 产生微分ROC曲线下的面积（！ROC）。 在显影之后 的方法，它将被应用于体模和临床图像下， 各种加工。 临床任务是检测 微钙化，肿块检测，分类 微钙化和肿块分类。 申请人建议 将该方法扩展到其他观察者性能实验，包括 定位信息，即FROC和LROC，并评估可行性 将其应用于CAD评估。 在每种情况下，他们都会测量 1方法的统计功效优于传统ROC 法 申请人建议将该方法应用于评估 几种压缩算法在数字乳腺摄影中的效果。 的1 该方法具有检测图像中非常细微的差异的潜力 质量，远小于通过本ROC方法检测到的。 它将 允许成像系统的快速优化，而不需要昂贵的 而且常常是包含性的ROC研究。 压缩算法评估为 预计将有助于成像科学家和工程师谁需要设计 更具成本效益的数字乳腺X射线摄影系统。 这将大大 促进在乳房X线摄影中采用数字方法，从而 to improve改善health健康care保健for women妇女with breast乳腺癌cancer癌症.

DESCRIPTION (Adapted from Applicant's Abstract): The objective of this research is to develop a method for determining differences in diagnostic task performance between any two modalities A and B. The new technique is called the paired image (1) observer performance method. While the research will be done with x-ray images, the 1 method is expected to have much wider applicability (e.g., CT verses MRI). In the 1 method, the reader is shown a pair of images of the same subject, from each modality. The observer selects the image that is superior for the specified diagnostic task and assigns a rating (A>>B,A>B,A=B,A<B,A<<B). Analysis of this experiment yields the area under the Differential ROC curve (!ROC). After development of the method, it will be applied to phantom and clinical images under a variety of processings. The clinical tasks are detection of microcalcifications, detection of masses, classification of microcalcifications,and classification of masses. The applicants proposed to extend the method to other observer performance experiments that include localization information, namely, FROC and LROC,and assess the feasibility of applying it to CAD evaluation. In each case, they would measure the improvement in statistical power of the 1 method over the traditional ROC method. The applicants proposed to apply the method to evaluating the effect of several compression algorithms in digital mammography. The 1 method has the potential for detecting very subtle differences in image quality, much smaller than is detectable by the present ROC method. It will allow rapid optimization of imaging systems, without the need for expensive and often inclusive ROC studies. The compression algorithm evaluation is expected to be useful to imaging scientists and engineers who need to design more cost-effective digital mammography systems. This will greatly facilitate the adoption of digital methods in mammography and thereby lead to improved health care for women with breast cancer.