MODEL OBSERVER OPTIMIZATION OF X-RAY CORONARY ANGIOGRAMS

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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.