Biomedical subjects

A H Baydush

Publications and source records attributed to A H Baydush.

6 recordsLinked to original sources

PubMed1998

Characteristics of regions suspicious for pulmonary nodules at chest radiography.

RATIONALE AND OBJECTIVES: This study was performed to determine physical characteristics of areas on chest radiographs that are suspicious but not definitive for the presence of a pulmonary nodule and the characteristics of areas that contain an obvious nodule. MATERIALS AND METHODS: Two groups of patients were identified: those who had an area at plain radiography that was suspicious for a pulmonary nodule and underwent fluoroscopy for further evaluation (138 patients, 142 areas) and those who had an obvious nodule at plain radiography who underwent computed tomography for further evaluation (72 patients, 97 areas). The measured characteristics of the region of interest included size, circularity, compactness, contrast, and location. RESULTS: A comparison of the data show that while there was some difference between these groups of patients with regard to location of the nodules, there were essentially no differences with regard to size, circularity, compactness, and contrast of the regions of interest. CONCLUSION: Size, circularity, compactness, contrast, and location are not sufficient to distinguish pulmonary nodules from other suspicious regions on the chest radiograph.

Fluoroscopy↗

PubMed1997

Improved Bayesian image estimation for digital chest radiography.

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 antiscatter grid and can increase CNR without loss of resolution. The new technique outperforms the previous Bayesian techniques.

Bayes Theorem↗

PubMed1996

Spatially varying Bayesian image estimation.

RATIONALE AND OBJECTIVES: Second-order neighborhoods and a spatially varying prior were incorporated into Bayesian image estimation (BIE) to improve image contrast-to-noise ratios (CNRs) while preserving image resolution. METHODS: Second-order neighborhoods were incorporated into the 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. CNRs, resolution, and image appearance were evaluated. RESULTS: The use of second-order neighborhoods alone improved the CNR in the mediastinum and degraded the resolution. SVBIE demonstrated no degradation of resolution. In the lung region, SVBIE enhanced the 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 the CNR over the original image. CONCLUSION: The SVBIE technique provides improved image CNR with no loss of resolution.

Algorithms↗

PubMed1995

Bayesian image estimation of digital chest radiography: interdependence of noise, resolution, and scatter fraction.

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.(ABSTRACT TRUNCATED AT 250 WORDS)

Bayes Theorem↗

PubMed1994

Bayesian restoration of chest radiographs. Scatter compensation with improved signal-to-noise ratio.

OBJECTIVES: The authors introduce a Bayesian algorithm for digital chest radiography that increases the signal-to-noise ratio, and thus detectability, for low-contrast objects. METHOD: 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%. CONCLUSION: This statistical estimation technique shows promise for improving object detectability in radiographs by simultaneously increasing contrast, while constraining noise.

Algorithms↗

PubMed1993

Scatter compensation for digital chest radiography using maximum likelihood expectation maximization.

RATIONALE AND OBJECTIVES: An iterative maximum likelihood expectation maximization algorithm (MLEM) has been developed for scatter compensation in chest radiography. METHODS: The MLEM technique produces a scatter-reduced image which maximizes the probability of observing the measured image. We examined the scatter content and the low-contrast signal-to-noise ratio (SNR) in digital radiographs of anatomical phantoms before and after compensation. RESULTS: MLEM converged to an accurate (6.4% RMS residual scatter error) estimate within 12 iterations. Both contrast and noise were increased in the processed images as iteration progressed. In the lung, contrast was increased 108% and SNR was improved by 10%. In the retrocardiac region, contrast was increased 180% while SNR decreased by 6%. CONCLUSIONS: This is the first report of a post-acquisition scatter compensation technique which can increase SNR. These results suggest that statistical estimation techniques can enhance image quality and quantitative accuracy for digital chest radiography.

Algorithms↗