Optimization of flat-panel detector characteristics and processing using quantitative image quality techniques

Abstract

Although physical measurements such as detective quantum efficiency (DQE) and modulation transfer function (MTF) provide insights, quantitative optimization of x-ray flat panel detectors requires consideration of image quality as perceived by humans. Using experiments and human observer models, we quantified image quality as the ability to detect targets such as stents and guidewires used in interventional angiography. We realistically simulated direct and indirect flat panel detectors over a range of exposures to create realistic fluoroscopy sequences. We performed objective, m-alternative adaptive forced choice experiments and applied models of human detection to fit almost all experiments and predict results for similar tasks and processing. With regard to pixel size, the best size at low fluoroscopic exposures for detecting a 400 μm guide wire with a realistic, indirect detector was at about 200 μm and depended upon such device parameters as electronic noise. For both indirect and direct detectors at higher exposures, noise was not limiting, and a small pixel was desirable. With regard to binning, we determined that binning is desirable at low exposures even for detection of small objects such as a guide wire. A new alternate binning method was found to have superior image quality by utilizing the ability of humans to temporally fuse alternating images binned at different orientations. Comparing to magnification of analog image intensifiers, we determined that flat panel images can be digitally magnified without loss of image quality at a decreased average exposure.