Spectral correction of photon-counting detectors in contrast-enhanced spectral mammography using a light-weight convolutional neural network

Colin Schaeffer; Stephen J. Glick; Bahaa Ghammraoui

doi:10.1117/12.3000734

1 April 2024 Spectral correction of photon-counting detectors in contrast-enhanced spectral mammography using a light-weight convolutional neural network

Colin Schaeffer, Stephen J. Glick, Bahaa Ghammraoui

Author Affiliations +

Proceedings Volume 12925, Medical Imaging 2024: Physics of Medical Imaging; 129252L (2024) https://doi.org/10.1117/12.3000734
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

Contrast-enhanced mammography (CEM) has shown increased sensitivity for detecting breast cancer when compared to traditional full-field digital mammography with performance comparable to MRI. While all current CEM systems use a dual-energy approach, photon-counting detectors can similarly be used by acquiring two or more energy bins to subtract anatomical noise and highlight iodine uptake. Photon-counting detectors have several advantages over dual-energy such as the simultaneous acquisition of multiple energy bins and the potential for electronic noise rejection. However, photon-counting detectors suffer from several physical phenomena such as charge sharing and k-shell fluorescence that degrade its spatial and spectral response. Solving for true counts given measured photon counts constitutes an inverse problem. While, analytically difficult to solve, machine learning techniques can be an alternative method. In this simulated study, we investigated the use a light-weight convolutional neural network to correct for spatial and spectral degradations in CEM acquisitions using a photon-counting detector.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Colin Schaeffer, Stephen J. Glick, and Bahaa Ghammraoui "Spectral correction of photon-counting detectors in contrast-enhanced spectral mammography using a light-weight convolutional neural network", Proc. SPIE 12925, Medical Imaging 2024: Physics of Medical Imaging, 129252L (1 April 2024); https://doi.org/10.1117/12.3000734

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