Improving image synthesis quality in multi-contrast MRI using transfer learning via autoencoders

Date
2022-08-29
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Source Title
Signal Processing and Communications Applications Conference (SIU)
Print ISSN
2165-0608
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Publisher
IEEE
Volume
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Pages
[1] - [4]
Language
English
Type
Conference Paper
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Abstract

The capacity of magnetic resonance imaging (MRI) to capture several contrasts within a session enables it to obtain increased diagnostic information. However, such multi-contrast MRI tests take a long time to scan, resulting in acquiring just a part of the essential contrasts. Synthetic multi-contrast MRI has the potential to improve radiological observations and consequent image analysis activities. Because of its ability to generate realistic results, generative adversarial networks (GAN) have recently been the most popular choice for medical imaging synthesis. This paper proposes a novel generative adversarial framework to improve the image synthesis quality in multi-contrast MRI. Our method uses transfer learning to adapt pre-trained autoencoder networks to the synthesis task and enhances the image synthesis quality by initializing the training process with more optimal network parameters. We demonstrate that the proposed method outperforms competing synthesis models by 0.95 dB on average on a well-known multi-contrast MRI dataset.

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Keywords
Multi-contrast MRI, Autoencoder, Transfer learning, Generative adversarial networks
Citation
Published Version (Please cite this version)