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ReeGAN: MRI Image Edge-preserving Synthesis Based on GANs Trained with Misaligned Data

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Publisher Springer
Date 2024 Feb 24
PMID 38396277
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Abstract

As a crucial medical examination technique, different modalities of magnetic resonance imaging (MRI) complement each other, offering multi-angle and multi-dimensional insights into the body's internal information. Therefore, research on MRI cross-modality conversion is of great significance, and many innovative techniques have been explored. However, most methods are trained on well-aligned data, and the impact of misaligned data has not received sufficient attention. Additionally, many methods focus on transforming the entire image and ignore crucial edge information. To address these challenges, we propose a generative adversarial network based on multi-feature fusion, which effectively preserves edge information while training on noisy data. Notably, we consider images with limited range random transformations as noisy labels and use an additional small auxiliary registration network to help the generator adapt to the noise distribution. Moreover, we inject auxiliary edge information to improve the quality of synthesized target modality images. Our goal is to find the best solution for cross-modality conversion. Comprehensive experiments and ablation studies demonstrate the effectiveness of the proposed method.

References
1.
Zhou T, Fu H, Chen G, Shen J, Shao L . Hi-Net: Hybrid-Fusion Network for Multi-Modal MR Image Synthesis. IEEE Trans Med Imaging. 2020; 39(9):2772-2781. DOI: 10.1109/TMI.2020.2975344. View

2.
Torrado-Carvajal A, Herraiz J, Alcain E, Montemayor A, Garcia-Canamaque L, Hernandez-Tamames J . Fast Patch-Based Pseudo-CT Synthesis from T1-Weighted MR Images for PET/MR Attenuation Correction in Brain Studies. J Nucl Med. 2015; 57(1):136-43. DOI: 10.2967/jnumed.115.156299. View

3.
Klages P, Benslimane I, Riyahi S, Jiang J, Hunt M, Deasy J . Patch-based generative adversarial neural network models for head and neck MR-only planning. Med Phys. 2019; 47(2):626-642. PMC: 7146715. DOI: 10.1002/mp.13927. View

4.
Roy S, Carass A, Prince J . Magnetic Resonance Image Example-Based Contrast Synthesis. IEEE Trans Med Imaging. 2013; 32(12):2348-63. PMC: 3955746. DOI: 10.1109/TMI.2013.2282126. View

5.
Jog A, Carass A, Roy S, Pham D, Prince J . MR image synthesis by contrast learning on neighborhood ensembles. Med Image Anal. 2015; 24(1):63-76. PMC: 4532609. DOI: 10.1016/j.media.2015.05.002. View