Bayesian restoration of chest radiographs. Scatter compensation with improved signal-to-noise ratio.
OBJECTIVE
The authors introduce a Bayesian algorithm for digital chest radiography that increases the signal-to-noise ratio, and thus detectability, for low-contrast objects.


METHODS
The improved images are formed as a maximum a posteriori probability estimation of a scatter-reduced (contrast-enhanced) image with decreased noise. Noise is constrained by including prior knowledge of image smoothness. Variations between neighboring pixels are penalized for small variations (to suppress Poisson noise), but not for larger variations (to avoid affecting anatomical structure). The technique was optimized to reduce residual scatter in digital radiographs of an anatomical chest phantom.


RESULTS
The contrast in the lung was improved by a factor of two, whereas signal-to-noise ratio was improved by a factor of 1.8. Image resolution was unaffected for objects with a contrast greater than 2%.


CONCLUSIONS
This statistical estimation technique shows promise for improving object detectability in radiographs by simultaneously increasing contrast, while constraining noise.
Department of Radiology, Duke University Medical Center, Durham, NC 27710.
C E
CE
Floyd
A H
AH
Baydush
J Y
JY
Lo
J E
JE
Bowsher
C E
CE
Ravin
D000465:Algorithms; D001499:Bayes Theorem; D006321:Heart / Q000000981:diagnostic imaging; D006801:Humans; D007091:Image Processing, Computer-Assisted; D016013:Likelihood Functions; D008168:Lung / Q000000981:diagnostic imaging; D008482:Mediastinum / Q000000981:diagnostic imaging; D008961:Models, Structural; D016012:Poisson Distribution; D011137:Polystyrenes; D011336:Probability; D011856:Radiographic Image Enhancement / Q000379:methods*; D013902:Radiography, Thoracic / Q000379:methods* / Q000706:statistics & numerical data; D012542:Scattering, Radiation; D012815:Signal Processing, Computer-Assisted; D014965:X-Rays
D011137:Polystyrenes