Maximum-likelihood estimation of thickness with a linear-scatter model for mammography

Bryce Smith; Joyoni Dey

doi:10.1117/12.2611970

4 April 2022 Maximum-likelihood estimation of thickness with a linear-scatter model for mammography

Bryce Smith, Joyoni Dey

Proceedings Volume 12031, Medical Imaging 2022: Physics of Medical Imaging; 120311Y (2022) https://doi.org/10.1117/12.2611970
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

The objective of this work was to create a MLEM software-based scatter correction algorithm for removing the effect of Compton Scatter from mammography images acquired without scatter grid or with analyzer-less interferometry. We developed an MLEM algorithm with an efficient linear scatter model to estimate the thickness of compressed breast and evaluated the algorithm with breast images acquired with the GEANT4 Monte Carlo software. The thicknesses estimated from the algorithm on the GEANT4 images were compared to the true geometric thicknesses of the ellipsoid for each pixel of the detector and matched to within ~2mm RMS error.

Conference Presentation

Citation Download Citation

Bryce Smith and Joyoni Dey "Maximum-likelihood estimation of thickness with a linear-scatter model for mammography", Proc. SPIE 12031, Medical Imaging 2022: Physics of Medical Imaging, 120311Y (4 April 2022); https://doi.org/10.1117/12.2611970

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