Towards improved lesion quantification and volume estimation with contrast-enhanced digital breast tomosynthesis using convolutional neural networks: a simulation study

Abstract

Iodine contrast-enhanced spectral mammography (CEM) combines an iodinated contrast agent, such as one used for a typical CT scan, with mammography imaging. The contrast enhancement improves the ability to visualize some cancers, and so it has been proposed as a costeffective and robust alternative to magnetic resonance imaging (MRI) for breast cancer imaging, especially in dense breasts. However, one drawback is poor quantification of contrast agent due to the two-dimensional projection in mammogram images. Digital breast tomosynthesis (DBT) is a pseudo-three-dimensional (3D) imaging modality that uses limited angle tomography. DBT typically exhibits high in-plane resolution, with poor out-of-plane resolution. This out-of-plane blur in DBT distorts the reconstructed lesion and can degrade lesion quantification and volume estimation. This work will explore whether convolutional neural networks (CNN) can be trained to predict a full angle CT reconstruction of the lesion from a limited angle DBT input image of the lesion. Various networks were trained to perform this image restoration using a large number of Monte-Carlo simulated lesion volumes-of-interest (VOI) from DBT and breast CT reconstructions. Our preliminary results show that the output images from the trained neural networks yield a more accurate values in terms of lesion quantification and volume estimation than those estimated from their DBT counterparts. © 2022 SPIE.