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

arXiv 2409.18872 ↗First posted 27 Sep 2024eess.IV cs.CV cs.LGcited by 6

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 46 of its 108 references that are in the map. First posted on arXiv 27 Sep 2024.

Builds on

46 of 108 references.

References · 46

2014Adam: A Method for Stochastic Optimizationcited by 36,960
2020Denoising Diffusion Probabilistic Modelscited by 20,851
2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,466
2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510

34 more

2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
2021Palette: Image-to-Image Diffusion Modelscited by 1,296
2019Semantic Image Synthesis with Spatially-Adaptive Normalizationcited by 1,295
2017High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANscited by 1,176
2017StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translationcited by 1,036
2016Least Squares Generative Adversarial Networkscited by 1,000
2018Pros and Cons of GAN Evaluation Measurescited by 224
2024Adam-mini: Use Fewer Learning Rates To Gain Morecited by 134
2018Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial Networkscited by 131
2020You Only Need Adversarial Supervision for Semantic Image Synthesiscited by 129
2021Pros and Cons of GAN Evaluation Measures: New Developmentscited by 121
2018Distribution Matching Losses Can Hallucinate Features in Medical Image Translationcited by 99
2017Deep MR to CT Synthesis using Unpaired Datacited by 86
2024Anatomically-Controllable Medical Image Generation with Segmentation-Guided Diffusion Modelscited by 69
2023Generative AI for Medical Imaging: extending the MONAI Frameworkcited by 68
2022CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentationcited by 58
2024Why Warmup the Learning Rate? Underlying Mechanisms and Improvementscited by 45
2024Hallucination Index: An Image Quality Metric for Generative Reconstruction Modelscited by 25
2023Diffusion Models for Contrast Harmonization of Magnetic Resonance Imagescited by 19
2021Data synthesis and adversarial networks: A review and meta-analysis in cancer imagingcited by 15
2022medigan: a Python library of pretrained generative models for medical image synthesiscited by 14
2023A Deep Learning Approach for Virtual Contrast Enhancement in Contrast Enhanced Spectral Mammographycited by 12
2022High-resolution synthesis of high-density breast mammograms: Application to improved fairness in deep learning based mass detectioncited by 11
2023Synthesis-based Imaging-Differentiation Representation Learning for Multi-Sequence 3D/4D MRIcited by 11
2024Towards Learning Contrast Kinetics with Multi-Condition Latent Diffusion Modelscited by 11
2023You Don't Have to Be Perfect to Be Amazing: Unveil the Utility of Synthetic Imagescited by 10
2023Pre- to Post-Contrast Breast MRI Synthesis for Enhanced Tumour Segmentationcited by 9
2023Synthesis of Contrast-Enhanced Breast MRI Using Multi-b-Value DWI-based Hierarchical Fusion Network with Attention Mechanismcited by 8
2021Synthesizing MR Image Contrast Enhancement Using 3D High-resolution ConvNetscited by 5
2023Faithful Synthesis of Low-dose Contrast-enhanced Brain MRI Scans using Noise-preserving Conditional GANscited by 5
2024Progressive Growing of Patch Size: Resource-Efficient Curriculum Learning for Dense Prediction Taskscited by 5
2024CAVM: Conditional Autoregressive Vision Model for Contrast-Enhanced Brain Tumor MRI Synthesiscited by 3
2024Enhancing the Utility of Privacy-Preserving Cancer Classification using Synthetic Datacited by 3
2023Mind the (optimality) Gap: A Gap-Aware Learning Rate Scheduler for Adversarial Netscited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 5.

6 papers.