A Deep Learning Approach for Virtual Contrast Enhancement in Contrast Enhanced Spectral Mammography

TL;DR

This paper introduces deep learning models, including autoencoders and GANs, to generate synthetic contrast-enhanced images from low-energy CESM scans, aiming to eliminate contrast media and reduce radiation dose.

Contribution

It presents a novel approach using deep generative models for virtual contrast enhancement in CESM, and provides a new publicly available dataset of 1138 images.

Findings

CycleGAN outperforms other models in generating synthetic recombined images

Deep models can produce high-quality contrast-free CESM images

The approach has potential to reduce contrast media use and radiation dose

Abstract

Contrast Enhanced Spectral Mammography (CESM) is a dual-energy mammographic imaging technique that first needs intravenously administration of an iodinated contrast medium; then, it collects both a low-energy image, comparable to standard mammography, and a high-energy image. The two scans are then combined to get a recombined image showing contrast enhancement. Despite CESM diagnostic advantages for breast cancer diagnosis, the use of contrast medium can cause side effects, and CESM also beams patients with a higher radiation dose compared to standard mammography. To address these limitations this work proposes to use deep generative models for virtual contrast enhancement on CESM, aiming to make the CESM contrast-free as well as to reduce the radiation dose. Our deep networks, consisting of an autoencoder and two Generative Adversarial Networks, the Pix2Pix, and the CycleGAN, generate synthetic recombined images solely from low-energy images. We perform an extensive quantitative and qualitative analysis of the model's performance, also exploiting radiologists' assessments, on a novel CESM dataset that includes 1138 images that, as a further contribution of this work, we make publicly available. The results show that CycleGAN is the most promising deep network to generate synthetic recombined images, highlighting the potential of artificial intelligence techniques for virtual contrast enhancement in this field.