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

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...

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Bibliographic Details
Published in:arXiv.org 2024-09
Main Authors: Osuala, Richard, Joshi, Smriti, Tsirikoglou, Apostolia, Garrucho, Lidia, Pinaya, Walter H L, Lang, Daniel M, Schnabel, Julia A, Diaz, Oliver, Lekadir, Karim
Format: Article
Language:English
Subjects:
Contrast agents
Generative adversarial networks
Image acquisition
Image contrast
Image enhancement
Image segmentation
Magnetic resonance imaging
Medical imaging
Tumors
Virtual networks
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Summary: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.
ISSN:2331-8422