TeNCA, a temporal neural cellular automata trained with adaptive losses at irregularly spaced time points, produces synthetic post-contrast breast MRI images that match ground truth more closely than U-Net and latent diffusion baselines.
Simulating Dynamic Tumor Contrast Enhancement in Breast MRI using Conditional Generative Adversarial Networks
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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.
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Temporal Neural Cellular Automata: Application to modeling of contrast enhancement in breast MRI
TeNCA, a temporal neural cellular automata trained with adaptive losses at irregularly spaced time points, produces synthetic post-contrast breast MRI images that match ground truth more closely than U-Net and latent diffusion baselines.