Abstract
Image-to-image translation from a source to a target domain by means of generative adversarial neural network (GAN) has gained a lot of attention in the medical imaging field due to their capability to learn the mapping characteristics between different modalities. CycleGAN has been proposed for image-to-image translation with unpaired images by means of a cycle-consistency loss function, which is optimized to reduce the difference between the image reconstructed from the synthetically-generated domain and the original input. However, CycleGAN inherently implies that the mapping between both domains is invertible, i.e., given a mapping G (forward cycle) from domain A to B, there is a mapping F (backward cycle) that is the inverse of G. This is assumption is not always true. For example, when we want to learn functional activity from structural modalities. Although it is well-recognized the relation between structure and function in different physiological processes, the problem is not invertible as the original modality cannot be recovered from a given functional response.
In this paper, we propose a functional-consistent CycleGAN that leverages the usage of a proxy structural image in a third domain, shared between source and target, to help the network learn fundamental characteristics while being cycle consistent. To demonstrate the strength of the proposed strategy, we present the application of our method to estimate iodine perfusion maps from contrast CT scans, and we compare the performance of this technique to a traditional CycleGAN framework.
This work has been partially funded by the National Institutes of Health NHLBI awards R01HL116473, R01HL149877, and by the Brigham Health BWH Radiology Department Pilot Grant Award. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the GPU used for this research.
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Nardelli, P., San José Estépar, R., Rahaghi, F.N., San José Estépar, R. (2020). Functional-Consistent CycleGAN for CT to Iodine Perfusion Map Translation. In: Petersen, J., et al. Thoracic Image Analysis. TIA 2020. Lecture Notes in Computer Science(), vol 12502. Springer, Cham. https://doi.org/10.1007/978-3-030-62469-9_10
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