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

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.Immediate accessThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu