Simulating Dynamic Tumor Contrast Enhancement in Breast MRI using Conditional Generative Adversarial Networks

TL;DR

This paper introduces a novel method using conditional GANs to generate synthetic DCE-MRI images from non-contrast MRI scans, enabling tumor analysis without contrast agents.

Contribution

It presents a new deep learning approach for virtual contrast enhancement in breast MRI, including multi-sequence generation and evaluation metrics.

Findings

Synthetic DCE-MRI images are realistic and useful for tumor segmentation.

The method reduces the need for contrast agents, lowering health risks.

Generated sequences capture relevant temporal patterns in tumor enhancement.

Abstract

This paper presents a method for virtual contrast enhancement in breast MRI, offering a promising non-invasive alternative to traditional contrast agent-based DCE-MRI acquisition. Using a conditional generative adversarial network, we predict DCE-MRI images, including jointly-generated sequences of multiple corresponding DCE-MRI timepoints, from non-contrast-enhanced MRIs, enabling tumor localization and characterization without the associated health risks. Furthermore, we qualitatively and quantitatively evaluate the synthetic DCE-MRI images, proposing a multi-metric Scaled Aggregate Measure (SAMe), assessing their utility in a tumor segmentation downstream task, and conclude with an analysis of the temporal patterns in multi-sequence DCE-MRI generation. Our approach demonstrates promising results in generating realistic and useful DCE-MRI sequences, highlighting the potential of virtual contrast enhancement for improving breast cancer diagnosis and treatment, particularly for patients where contrast agent administration is contraindicated.