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

arXiv: https://arxiv.org/abs/2308.00471 · HTML: https://mlsanity.com/paper/2308.00471/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 32 references that are in the map.
- First posted on arXiv 1 Aug 2023.

## Builds on (most cited first, 3 of 3)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [A Unified Conditional Disentanglement Framework for Multimodal Brain MR Image Translation](https://mlsanity.com/paper/2101.05434/) (2021)
- [CT Image Denoising with Perceptive Deep Neural Networks](https://mlsanity.com/paper/1702.07019/) (2017)

## Built on by (most cited first, 12 of 12)

- [A Systematic Review of Intermediate Fusion in Multimodal Deep Learning for Biomedical Applications](https://mlsanity.com/paper/2408.02686/) (2024)
- [A graph neural network-based model with Out-of-Distribution Robustness for enhancing Antiretroviral Therapy Outcome Prediction for HIV-1](https://mlsanity.com/paper/2312.17506/) (2023)
- [Simulating Dynamic Tumor Contrast Enhancement in Breast MRI using Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2409.18872/) (2024)
- [Whole-Body Image-to-Image Translation for a Virtual Scanner in a Healthcare Digital Twin](https://mlsanity.com/paper/2503.15555/) (2025)
- [Cross-modality image synthesis from TOF-MRA to CTA using diffusion-based models](https://mlsanity.com/paper/2409.10089/) (2024)
- [Augmented Intelligence for Multimodal Virtual Biopsy in Breast Cancer Using Generative Artificial Intelligence](https://mlsanity.com/paper/2501.19176/) (2025)
- [Any-to-Any Vision-Language Model for Multimodal X-ray Imaging and Radiological Report Generation](https://mlsanity.com/paper/2505.01091/) (2025)
- [Lesion-Aware Generative Artificial Intelligence for Virtual Contrast-Enhanced Mammography in Breast Cancer](https://mlsanity.com/paper/2505.03018/) (2025)
- [Beyond a Single Mode: GAN Ensembles for Diverse Medical Data Generation](https://mlsanity.com/paper/2503.24258/) (2025)
- [Evaluating Vision Language Model Adaptations for Radiology Report Generation in Low-Resource Languages](https://mlsanity.com/paper/2505.01096/) (2025)
- [Are Virtual DES Images a Valid Alternative to the Real Ones?](https://mlsanity.com/paper/2508.15594/) (2025)
- [Virtual Scanning for NSCLC Histology: Investigating the Discriminatory Power of Synthetic PET](https://mlsanity.com/paper/2605.02746/) (2026)

## Abstract (arXiv)

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.