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估是耗时和昂贵的，并且优化是不可行的。因此，使用观察者研究的预测准确性来衡量图像质量是有充分理由的，但这样做既省时又省钱。我们利用计算机模型观察器在优化图像压缩方面的先前工作基础上，将其应用扩展到复杂的分类任务(例如支架部署评估)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，它可以用来优化用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目标：1)开发一大组测试图像，将平板数字探测器获取的X射线冠状动脉造影与模拟的动脉节段和病变相结合；2)为更复杂的分类任务和患者结构背景序列开发计算机模型观察者；3)进行人类视觉心理物理研究，评估不同的处理和显示参数，包括：14位到8位转换、像素入库、显示帧速率和帧数目；3)比较新提出的模型观察者预测观察者任务性能的能力；4)使用预测能力最高的模型观测器执行处理算法方法的自动优化；5)进行心理物理验证研究，将优化后的处理函数与其他标准函数进行比较。这项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，适用于更复杂的分类任务和医学图像序列；2)改进了数字获取的X-射线冠状动脉造影的处理和显示，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.