Tags

Type your tag names separated by a space and hit enter

Resolution enhancement in digital x-ray imaging.
Phys Med Biol. 2006 May 21; 51(10):2415-39.PM

Abstract

We have developed a restoration method for radiographs that enhances image sharpness and reveals bone microstructures that were initially hidden in the soft-tissue glare. The method is two fold: the image is first deconvolved using the Richardson-Lucy algorithm and is then divided with a signal modelling the soft-tissue distribution to increase the overall contrast. Each step has its own merits but the power of the restoration method lies in their combination. The originality of the method is its reliance on a priori information at each step in the processing. We have measured and modelled analytically the point-spread function of a low-dose gas microstrip x-ray detector at several beam energies. We measured the relationship between the local image intensity and the noise variance for these images. The soft-tissue signal was also modelled using a minimum-curvature filtering technique. These results were then combined into an image deconvolution procedure that uses wavelet filtering to reduce restoration noise while keeping the enhanced small-scale features. The method was applied successfully to images of a human-torso phantom and improved the contrast of small details on the bones and in the soft tissues. We measured a mean 54% increase in signal to noise ratio and a mean 105% increase in contrast to noise ratio in the 70 and 140 kVp images we analysed. The method was designed to facilitate the analysis of radiographs by relying on two levels of visual inspection. The contrast of the full image is first enhanced by division with the signal modelling the soft-tissue distribution. Based on the result, a radiologist might decide to zoom in on a given image section. The full restoration method is then applied to that region of interest. Indeed, full image deconvolution is often unnecessary since enhanced small-scale details are not visible at large scale; only the section of interest is processed which is more efficient.

Authors+Show Affiliations

Gravel P
Génie de la production automatisée, Ecole de technologie supérieure, Montréal, Canada. pierre.gravel@etsmtl.ca
Després P
No affiliation info available
Beaudoin G
No affiliation info available
de Guise JA
No affiliation info available

MeSH

AlgorithmsHumansInformation Storage and RetrievalPhantoms, ImagingRadiographic Image EnhancementRadiographic Image Interpretation, Computer-AssistedReproducibility of ResultsSensitivity and SpecificitySignal Processing, Computer-Assisted

Pub Type(s)

Evaluation Study
Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

16675861

Citation

Gravel, Pierre, et al. "Resolution Enhancement in Digital X-ray Imaging." Physics in Medicine and Biology, vol. 51, no. 10, 2006, pp. 2415-39.
Gravel P, Després P, Beaudoin G, et al. Resolution enhancement in digital x-ray imaging. Phys Med Biol. 2006;51(10):2415-39.
Gravel, P., Després, P., Beaudoin, G., & de Guise, J. A. (2006). Resolution enhancement in digital x-ray imaging. Physics in Medicine and Biology, 51(10), 2415-39.
Gravel P, et al. Resolution Enhancement in Digital X-ray Imaging. Phys Med Biol. 2006 May 21;51(10):2415-39. PubMed PMID: 16675861.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Resolution enhancement in digital x-ray imaging. AU - Gravel,Pierre, AU - Després,Philippe, AU - Beaudoin,Gilles, AU - de Guise,Jacques A, Y1 - 2006/04/26/ PY - 2006/5/6/pubmed PY - 2006/7/28/medline PY - 2006/5/6/entrez SP - 2415 EP - 39 JF - Physics in medicine and biology JO - Phys Med Biol VL - 51 IS - 10 N2 - We have developed a restoration method for radiographs that enhances image sharpness and reveals bone microstructures that were initially hidden in the soft-tissue glare. The method is two fold: the image is first deconvolved using the Richardson-Lucy algorithm and is then divided with a signal modelling the soft-tissue distribution to increase the overall contrast. Each step has its own merits but the power of the restoration method lies in their combination. The originality of the method is its reliance on a priori information at each step in the processing. We have measured and modelled analytically the point-spread function of a low-dose gas microstrip x-ray detector at several beam energies. We measured the relationship between the local image intensity and the noise variance for these images. The soft-tissue signal was also modelled using a minimum-curvature filtering technique. These results were then combined into an image deconvolution procedure that uses wavelet filtering to reduce restoration noise while keeping the enhanced small-scale features. The method was applied successfully to images of a human-torso phantom and improved the contrast of small details on the bones and in the soft tissues. We measured a mean 54% increase in signal to noise ratio and a mean 105% increase in contrast to noise ratio in the 70 and 140 kVp images we analysed. The method was designed to facilitate the analysis of radiographs by relying on two levels of visual inspection. The contrast of the full image is first enhanced by division with the signal modelling the soft-tissue distribution. Based on the result, a radiologist might decide to zoom in on a given image section. The full restoration method is then applied to that region of interest. Indeed, full image deconvolution is often unnecessary since enhanced small-scale details are not visible at large scale; only the section of interest is processed which is more efficient. SN - 0031-9155 UR - https://www.unboundmedicine.com/medline/citation/16675861/Resolution_enhancement_in_digital_x_ray_imaging_ DB - PRIME DP - Unbound Medicine ER -