BAYESIAN RESTORATION OF CHEST RADIOGRAPHS - SCATTER COMPENSATION WITHIMPROVED SIGNAL-TO-NOISE RATIO

OBJECTIVES. The authors introduce a Bayesian algorithm for digital che st radiography that increases the signal-to-noise ratio, and thus dete ctability, for low-contrast objects. METHOD. The improved images are f ormed as a maximum a posteriori probability estimation of a scatter-re duced (contrast-enhanced) image with decreased noise. Noise is constra ined by including prior knowledge of image smoothness. Variations betw een neighboring pixels are penalized for small variations (to suppress Poisson noise), but not for larger variations (to avoid affecting ana tomical structure). The technique was optimized to reduce residual sca tter in digital radiographs of an anatomical chest phantom. RESULTS. T he contrast in the lung was improved by a factor of two, whereas signa l-to-noise ratio was improved by a factor of 1.8. Image resolution was unaffected for objects with a contrast greater than 2%. CONCLUSION. T his statistical estimation technique shows promise for improving objec t detectability in radiographs by simultaneously increasing contrast, while constraining noise.