Are Virtual DES Images a Valid Alternative to the Real Ones?

TL;DR

This study evaluates the feasibility of generating virtual dual-energy subtracted (DES) images from low-energy CESM images using deep learning models, and assesses their impact on lesion classification accuracy.

Contribution

It introduces and compares three models for virtual DES image generation and evaluates their effect on malignant versus non-malignant lesion classification.

Findings

Pre-trained U-Net achieved 85.59% F1 score with virtual DES images.

Real DES images yielded higher classification accuracy (90.35%).

Virtual DES images show promise for reducing radiation exposure in CESM.

Abstract

Contrast-enhanced spectral mammography (CESM) is an imaging modality that provides two types of images, commonly known as low-energy (LE) and dual-energy subtracted (DES) images. In many domains, particularly in medicine, the emergence of image-to-image translation techniques has enabled the artificial generation of images using other images as input. Within CESM, applying such techniques to generate DES images from LE images could be highly beneficial, potentially reducing patient exposure to radiation associated with high-energy image acquisition. In this study, we investigated three models for the artificial generation of DES images (virtual DES): a pre-trained U-Net model, a U-Net trained end-to-end model, and a CycleGAN model. We also performed a series of experiments to assess the impact of using virtual DES images on the classification of CESM examinations into malignant and non-malignant categories. To our knowledge, this is the first study to evaluate the impact of virtual DES images on CESM lesion classification. The results demonstrate that the best performance was achieved with the pre-trained U-Net model, yielding an F1 score of 85.59% when using the virtual DES images, compared to 90.35% with the real DES images. This discrepancy likely results from the additional diagnostic information in real DES images, which contributes to a higher classification accuracy. Nevertheless, the potential for virtual DES image generation is considerable and future advancements may narrow this performance gap to a level where exclusive reliance on virtual DES images becomes clinically viable.