Selected Publications

A full list of my publications is available in my on-line Curriculum Vitae.

"Bayesian Image Estimation of Digital Chest Radiography: Interdependence of Noise, Resolution, and Scatter Fraction" Baydush AH, and Floyd CE Jr. Medical Physics22, 1255-1261, 1995.: 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.
"Spatially Varying Bayesian Image Estimation" Baydush AH, and Floyd CE Jr. Academic Radiology3, 129-136, 1996.: Rationale and Objectives
Second order neighborhoods and spatially varying prior were incorporated into Bayesian image estimation to improve image contrast to noise ratios (CNR) while preserving image resolution.
Methods
Second order neighborhoods were incorporated into the Bayesian image estimation (BIE) algorithm. A spatially varying BIE (SVBIE) algorithm was developed by incorporating a spatially varying prior. The two algorithms were used to process an anthropomorphic chest phantom image. CNR, resolution, and image appearance were evaluated.
Results
The use of second order neighborhoods, alone, improved CNR in the mediastinum with degradation of resolution. SVBIE demonstrated no degradation of resolution. In the lung region, SVBIE enhanced CNR but did not perform as well as BIE. In the mediastinum, the SVBIE technique outperformed the older technique and provided a dramatic increase in CNR over the original image.
Conclusions
The SVBIE technique provides improved image CNR with no loss of resolution.
"A New Bayesian Scatter Reduction Algorithm for Chest Radiography" Baydush AH, Bowsher, JE, Laading, JK, and Floyd CE Jr. Medical Physics24, 539-545, 1997.: Purpose:
Previously, we have shown that Spatially Varying Bayesian Image Estimation (SVBIE) can be used to reduce scatter and improve contrast-to-noise ratios (CNR) in digital chest radiographs with no degradation of image resolution. This previous algorithm used a model for scatter compensation that was derived for emission tomography. Here, we develop and evaluate a new iterative SVBIE technique that incorporates a scatter model derived for projection radiography.
Materials and Methods:
Portable digital radiographs of an anthropomorphic chest phantom were obtained along with quantitative scatter measurements using a calibrated photostimulable phosphor system. The new iterative SVBIE technique was applied to the phantom image to reduce scatter. Scatter fraction reduction, CNR improvement, and resolution degradation were evaluated.
Results:
Residual scatter fractions were reduced to less than 2% in the lungs and 30% in the mediastinum at 14 iterations. CNR was improved by approximately 50% in the lung region and 187% in the mediastinum. Resolution was not degraded.
Conclusions:
The new SVBIE technique can reduce scatter to levels far below those provided by an anti-scatter grid and can increase CNR without loss of resolution. The new technique outperforms the previous Bayesian techniques.
"Visual Improvements in Bedside Radiographs via Numerical Compensation" Baydush AH, Bowsher JE, Ravin CE, and Floyd CE Jr. Radiographicssubmitted December 1998.: Rationale and Objectives:
A numerical Bayesian image estimation technique was used to produce scatter reduced bedside patient chest radiographs with increased contrast-to-noise ratios.
Materials and Methods:
Five bedside patient images with scatter measurements were acquired in a clinical setting with a calibrated photostimulable phosphor system. These images were processed with the Bayesian technique to reduce scatter. Scatter fractions were, on average, reduced from 67% to 7% in the lungs and from 93% to 25% in the mediastinum without degradation of resolution. Observers at the 1995 RSNA scientific assembly were asked to visually compare the computationally scatter reduced images with the original bedside radiographs.
Results:
Overall preference for the processed images was positive. 79% of the respondents chose the Bayesian images as showing improved visual clarity and increased ease of diagnosis.
Conclusions:
Visual comparison of the images showed improved appearance of the processed radiographs, thus demonstrating the feasibility of numerical scatter compensation.