MODEL OBSERVERS FOR COMPRESSION OF CORONARY ANGIOGRAMS

基于观察者研究的评估既耗时又昂贵，而且优化是不可行的。因此，具有观察者研究的预测准确性的图像质量测量具有很强的理论基础，但这是时间和成本效益。我们在之前关于计算机模型观测器的工作的基础上，将模型观测器的应用从病变不变的基本检测任务（检测/信号-在D-SKE任务中确切已知）扩展到更现实的任务，其中图像中出现的病变可以是不同类型和大小的（检测-分类/统计已知信号或DC-SKS任务）。我们的目标是开发一个计算机模型观察者，可以用来预测图像压缩在这些更现实的检测/分类任务中的效果。该模型可用于在这些较为现实的任务中根据任务性能对压缩算法参数进行优化。为了实现这一目标，我们提出了五个具体目标：1)开发计算机模型观察者，用于观察者必须检测和分类病变以及病变具有不同大小和/或方向的任务。2)对DC-SKS任务中五种不同的图像压缩算法（其中一些被认为是JPEG 2000国际标准）进行心理物理测量，并将其与我们之前使用D-SKE任务的结果进行比较。3)比较新提出的DC-SKS模型观察者对特定目标2中测量的观察者任务表现的预测能力。4)利用预测能力最高的DC-SKS模型观测器，利用模拟退火技术对压缩算法进行自动优化。5)对默认压缩参数、DCS-SKS模型优化参数和更传统的D-SKE模型优化参数的压缩算法在任务上的效果进行心理物理比较。如果成功，我们将把模型观察者的使用扩展到更现实的任务中。这项研究的影响将是改进基于计算机的成本和时间效率的医学图像质量评估指标，以及更快速和成本效益的数字冠状动脉造影通信和存储。

Evaluation based on observer studies is time consuming and costly and optimization is just not viable. There is therefore a strong rationale for image quality measures with the predictive accuracy of observer studies but that are time and cost efficient. We build on our previous work on computer model observers to extend the application of model observers from basic detection tasks where the lesion is invariant (detection/signal- known exactly on D-SKE tasks) to more realist tasks where the lesion appearing in the image can be of different types and sizes (detection- classification/signal known statistically or DC-SKS tasks). Our goal is to develop a computer model observer that can be used to predict the effect of image compression in these more realistic detection/classification tasks with lesion variability. This model could be used for optimization of compression algorithm parameters with respect to task performance in these more realistic tasks. To achieve this goal we propose five specific aims: 1) To develop computer model observers for tasks where the observers have to detect and classify a lesion and where the lesions have different sizes and/or orientations. 2) To perform psychophysical measurements of five different state of the art image compression algorithms (some of them which are being considered as the JPEG 2000 international standard) in the DC-SKS task and compare it to our previous results with D-SKE tasks. 3) To compare the newly proposed DC-SKS model observers with respect to their ability to predict the observer task performance measured in specific aim 2. 4) To use the DC-SKS model observer with highest predictive power to perform automated optimization of compression algorithms using stimulated annealing techniques. 5) To perform psychophysical comparisons of the effects of the compression algorithms on tasks with the default compression parameters, DCS-SKS model optimized parameters, and the more traditional D-SKE model optimized parameters. If successful, we will extend the use of model observers to more realistic tasks. The impact of this research will be improved computer based metrics for cost and time efficient evaluation of medical image quality as well as more rapid and cost effective communication and storage of digital coronary angiograms.