Abstract
Magnetic Resonance Imaging (MRI) has been established as an important diagnostic tool for research and clinical purposes. Multi-contrast scans can enhance the accuracy for many deep learning algorithms. However, these scans may not be available in some situations. Thus, it is valuable to synthetically generate non-existent contrasts from the available one. Existing methods based on Generative Adversarial Networks (GANs) lack the freedom to map one image to multiple contrasts using only a single generator and discriminator, hence, requiring training of multiple models for multi-contrast MR synthesis. We present a novel method for multi-contrast MR image synthesis with unpaired data using GANs. Our method leverages the strength of Star-GAN to translate a given image to n contrasts using a single generator and discriminator. We also introduce a new generation loss function, which enforces the generator to produce high-quality images which are perceptually closer to the real ones and exhibit high structural similarity as well. We experiment on IXI dataset to learn all possible mappings among T\(_1\)-weighted, T\(_2\)-weighted, Proton Density (PD) weighted and Magnetic Resonance Angiography (MRA) images. Qualitative and quantitative comparison against baseline method shows the superiority of our approach.
This work was supported by National Natural Science Foundation (NNSF) of China under Grant 61873166, 61673275 and 61473184.
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Sohail, M., Riaz, M.N., Wu, J., Long, C., Li, S. (2019). Unpaired Multi-contrast MR Image Synthesis Using Generative Adversarial Networks. In: Burgos, N., Gooya, A., Svoboda, D. (eds) Simulation and Synthesis in Medical Imaging. SASHIMI 2019. Lecture Notes in Computer Science(), vol 11827. Springer, Cham. https://doi.org/10.1007/978-3-030-32778-1_3
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