Simulating Dynamic Tumor Contrast Enhancement in Breast MRI using Conditional Generative Adversarial Networks
<|reference_start|>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 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.<|reference_end|>
@article{osuala2024simulating,
    title={Simulating Dynamic Tumor Contrast Enhancement in Breast MRI using
  Conditional Generative Adversarial Networks},
    author={Richard Osuala, Smriti Joshi, Apostolia Tsirikoglou, Lidia Garrucho,
  Walter H.L. Pinaya, Daniel M. Lang, Julia A. Schnabel, Oliver Diaz, Karim
  Lekadir},
    journal={arXiv preprint arXiv:2409.18872},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.18872},
    primaryClass={eess.IV cs.CV cs.LG}
}