CycleGAN Models for MRI Image Translation

TL;DR

This paper develops a CycleGAN model to translate neuroimages between different MRI field strengths, improving data augmentation and robustness in medical imaging with promising quantitative results.

Contribution

It introduces a CycleGAN-based approach for MRI image translation across field strengths, demonstrating its effectiveness compared to DCGAN architectures.

Findings

CycleGAN achieved a PSNR of 25.69 dB in image translation.

The model demonstrated reasonable accuracy in synthetic image generation.

It outperformed DCGAN in translating neuroimages between MRI field strengths.

Abstract

Image-to-image translation has gained popularity in the medical field to transform images from one domain to another. Medical image synthesis via domain transformation is advantageous in its ability to augment an image dataset where images for a given class is limited. From the learning perspective, this process contributes to data-oriented robustness of the model by inherently broadening the model's exposure to more diverse visual data and enabling it to learn more generalized features. In the case of generating additional neuroimages, it is advantageous to obtain unidentifiable medical data and augment smaller annotated datasets. This study proposes the development of a CycleGAN model for translating neuroimages from one field strength to another (e.g., 3 Tesla to 1.5). This model was compared to a model based on DCGAN architecture. CycleGAN was able to generate the synthetic and reconstructed images with reasonable accuracy. The mapping function from the source (3 Tesla) to target domain (1.5 Tesla) performed optimally with an average PSNR value of 25.69 $\pm$ 2.49 dB and an MAE value of 2106.27 $\pm$ 1218.37.