Quantitative image quality evaluation of pixel-binning in a flat-panel detector for x-ray fluoroscopy.
X-ray fluoroscopy places stringent design requirements on new flat-panel (FP) detectors, requiring both low-noise electronics and high data transfer rates. Pixel-binning, wherein data from more that one detector pixel are collected simultaneously, not only lowers the data transfer rate but also increases x-ray counts and pixel signal-to-noise ratio (SNR). In this study, we quantitatively assessed image quality of image sequences from four acquisition methods; no-binning and three types of binning; in synthetic images using a clinically relevant task of detecting an extended guidewire in a four-alternative forced-choice paradigm. Binning methods were conventional data-line (D) and gate-line (G) binning, and a novel method in which alternate frames in an image sequence used D and G binning. Two detector orientations placed the data lines either parallel or perpendicular to the guide wire. At a low exposure of 0.6 microR (1.548 x 10(-10) C/kg) per frame, irrespective of detector orientation, D binning with its reduced electronic noise was significantly (p<0.1) better than the other acquisition methods. On average, alternate binning performed better than G binning. At a higher exposure of 4.0 microR (10.32 x 10(-10) C/kg) per frame, with data lines parallel to the guidewire, detection with D binning was significantly (p<0.1) better than G binning. However, with data lines perpendicular to the guidewire, G binning was significantly (p<0.1) better than D binning because the partial area effect was reduced. Alternate binning was the best binning method when results were averaged over both orientations, and it was as good as the best binning method at either orientation. In addition, at low and high exposures, alternate binning gave a temporally fused image with a smooth guidewire, an important image quality feature not assessed in a detection experiment. While at high exposure, detection with no binning was as good, or better, than the best binning method, it might be impractical at fluoroscopy imaging rates. A computational observer model based on signal detection theory successfully fit data and was used to predict effects of similar acquisition methods. Results from this study suggest the use of exposure-dependent detector binning in fluoroscopy that switches between D binning and alternate binning at low and high exposures, respectively.
Srinivas
Yogesh
Y
Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio 44106, USA.
Wilson
David L
DL
D005471:Fluoroscopy; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012815:Signal Processing, Computer-Assisted; D014963:X-Ray Intensifying Screens