Optimization of detector pixel size for stent visualization in x-ray fluoroscopy.
Pixel size is of great interest in the flat-panel detector design because of its potential impact on image quality. In the particular case of angiographic x-ray fluoroscopy, small pixels are required in order to adequately visualize interventional devices such as guidewires and stents which have wire diameters as small as 200 and 50 microm, respectively. We used quantitative experimental and modeling techniques to investigate the optimal pixel size for imaging stents. Image quality was evaluated by the ability of subjects to perform two tasks: detect the presence of a stent and discriminate a partially deployed stent from a fully deployed one in synthetic images. With measurements at 50, 100, 200, and 300 microm, the 100 microm pixel size gave the maximum contrast sensitivity for the detection experiment with the idealized direct detector. For an idealized indirect detector with a scintillating layer, an optimal pixel size was obtained at 200 microm pixel size. A channelized human observer model predicted a peak at 150 and 170 microm, for the idealized direct and indirect detectors, respectively. With regard to the stent deployment task for both detector types, smaller pixel sizes are favored and there is a steep drop in performance with larger pixels. In general, with the increasing exposures, the model and measurements give the enhanced contrast sensitivities and a smaller optimal pixel size. The effects of electronic noise and fill factor were investigated using the model. We believe that the experimental results and human observer model predications can help guide the flat-panel detector design. In addition, the human observer model should work on the similar images and be applicable to the future model and actual flat-panel implementations.
Jiang
Yuhao
Y
Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio 44106, USA.
Wilson
David L
DL
D000465:Algorithms; D015350:Contrast Sensitivity; D005471:Fluoroscopy / Q000379:methods*; D006801:Humans; D010364:Pattern Recognition, Visual; D011856:Radiographic Image Enhancement / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D014963:X-Ray Intensifying Screens