MODEL OBSERVER OPTIMIZATION OF X-RAY CORONARY ANGIOGRAMS

描述（由申请人提供）： 基于人类观察者研究的医学图像质量评价是耗时且昂贵的，并且优化是不可行的。因此，具有观察者研究的预测准确性但具有时间和成本效益的图像质量测量有很强的理由。我们建立在我们以前的工作优化图像压缩使用计算机模型观测器扩展其应用到复杂的分类任务（例如，支架展开评估）和患者结构化背景的图像序列。我们的新目标是开发一种计算机模型观察器，可用于优化新引入的平板数字探测器采集的动态X射线冠状动脉造影图像的处理和显示。为了实现这一目标，我们提出了六个具体的目标：1）开发一个大的测试图像集结合X射线冠状动脉血管造影与平板数字探测器和模拟动脉段和病变采集; 2）开发计算机模型观察员更复杂的分类任务和序列的病人结构化背景; 3）进行人类视觉心理物理学研究，评估不同的处理和显示参数，包括：14位到8位的转换、像素合并、显示帧速率和帧数; 3）比较新提出的模型观测器预测观测器任务性能的能力; 4）使用具有最高预测能力的模型观测器来执行处理算法方法的自动优化; 5）执行心理物理验证研究，将优化的处理函数与其他标准函数进行比较。本研究的影响有两个方面：1）改进了基于计算机的医学图像质量指标，用于更复杂的分类任务和医学图像序列; 2）改进了数字采集的X射线冠状动脉造影片的处理和显示，从而提高了临床相关任务的准确性。

DESCRIPTION (provided by applicant): Evaluation of medical image quality based on human 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 optimization of image compression using computer model observers to extend their application to complex classification tasks (e.g., stent deployment assessment) and image sequences of patient structured backgrounds. Our new goal is to develop a computer model observer that can be used to optimize the processing and display of dynamic x-ray coronary angiographic images acquired with the newly introduced flat panel digital detectors. To achieve this goal we propose six specific aims: 1) To develop a large set of test images combining x-ray coronary angiograms acquired with the flat panel digital detectors and simulated arterial segments and lesions; 2) To develop computer model observers for more complex classification tasks and sequences of patient structured backgrounds; 3) To perform human visual psychophysical studies evaluating different processing and display parameters including: 14 bit to 8 bit transformations, pixel binning, display frame rate and number of frames; 3) To compare the newly proposed model observers with respect to their ability to predict the observer task performance; 4) To use the model observer with highest predictive power to perform automated optimization of the processing algorithm methods; 5) To perform a psychophysical validation study comparing the optimized processing functions to other standard functions. The impact of this research is two-fold: 1) Improved computer based metrics of medical image quality for more complex classification tasks and medical image sequences; 2) Improved processing and display of digitally acquired x-ray coronary angiograms leading to increased accuracy in clinically relevant tasks.