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Bayesian image estimation of digital chest radiography: Interdependence of noise, resolution, and scatter fraction
Author(s) -
Baydush Alan H.,
Floyd Carey E.
Publication year - 1995
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.597563
Subject(s) - radiography , imaging phantom , noise (video) , magnitude (astronomy) , residual , digital radiography , mathematics , kernel (algebra) , computed radiography , image noise , nuclear medicine , image quality , physics , optics , artificial intelligence , medicine , algorithm , computer science , image (mathematics) , combinatorics , astronomy , nuclear physics
Previously, it has been shown that Bayesian image estimation (BIE) can reduce the effects of scattered radiation and improve contrast‐to‐noise ratios (CNR) in digital radiographs of anthropomorphic chest phantoms by improving contrast while constraining noise. Here, the use of BIE as a noise reduction technique is reported. An anthropomorphic phantom was imaged with a previously calibrated photostimulable phosphor system using standard bedside chest radiography protocols. The Bayesian technique was then used to process this image. BIE incorporates a radial exponential convolution scatter model with two adjustable parameters. In previous reports, these parameters were optimized to reduce the residual fraction of scattered radiation in the processed image. Here, the parameters were adjusted to evaluate the potential of BIE to reduce image noise. While the full width at half maximum of the scatter model was held constant, the magnitude was varied. Evaluation was based on residual scatter fractions and CNR. The magnitude of the kernel in the scatter model was varied from 0.0 to 2.5 in steps of 0.5. Previously, it was found that an “ideal” scatter kernel magnitude of 2.33 provided a minimum residual scatter fraction. This magnitude corresponds to the average scatter‐to‐primary ratio in the chest radiograph. As the magnitude was increased, the residual scatter fraction decreased and the CNR increased in both the lungs and the mediastinum. However, as the magnitude was decreased, the percent noise also decreased; therefore, a lower magnitude kernel reduces noise. By varying the magnitude of the kernel used, differing amounts of noise reduction and contrast enhancement can be obtained. These results demonstrate that Bayesian image estimation can be used to both increase contrast and decrease noise in digital chest radiography.