Scatter reduction for grid-less mammography using the convolution-based image post-processing technique

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Authors: Marimón, E., Nait-Charif, H., Khan, A., Marsden, P.A. and Diaz, O.

http://eprints.bournemouth.ac.uk/29418/

Journal: Progress in Biomedical Optics and Imaging - Proceedings of SPIE

Volume: 10132

ISBN: 9781510607095

ISSN: 1605-7422

DOI: 10.1117/12.2255558

© 2017 SPIE. X-ray Mammography examinations are highly affected by scattered radiation, as it degrades the quality of the image and complicates the diagnosis process. Anti-scatter grids are currently used in planar mammography examinations as the standard physical scattering reduction technique. This method has been found to be inefficient, as it increases the dose delivered to the patient, does not remove all the scattered radiation and increases the price of the equipment. Alternative scattering reduction methods, based on post-processing algorithms, are being investigated to substitute anti-scatter grids. Methods such as the convolution-based scatter estimation have lately become attractive as they are quicker and more flexible than pure Monte Carlo (MC) simulations. In this study we make use of this specific method, which is based on the premise that the scatter in the system is spatially diffuse, thus it can be approximated by a two-dimensional low-pass convolution filter of the primary image. This algorithm uses the narrow pencil beam method to obtain the scatter kernel used to convolve an image, acquired without anti-scatter grid. The results obtained show an image quality comparable, in the worst case, to the grid image, in terms of uniformity and contrast to noise ratio. Further improvement is expected when using clinically-representative phantoms.

This data was imported from Web of Science (Lite):

Authors: Marimon, E., Nait-Charif, H., Khan, A., Marsden, P.A. and Diaz, O.

http://eprints.bournemouth.ac.uk/29418/

Journal: MEDICAL IMAGING 2017: PHYSICS OF MEDICAL IMAGING

Volume: 10132

ISBN: 978-1-5106-0710-1

ISSN: 0277-786X

DOI: 10.1117/12.2255558

The data on this page was last updated at 05:10 on February 17, 2020.