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A novel structure preserving generative adversarial network for CT to MR modality translation of spine

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Abstract

This paper proposes a structure preserving generative adversarial network (S-P GAN) to solve the problem of small structure information loss during the modality translation from computed tomography (CT) to magnetic resonance (MR). A novel generator of the S-P GAN is designed to encode features from CT, where an original CT information branch and its corresponding high-frequency information branch form a dual-branch structure. The small details are highlighted by using a filter in the high-frequency information branch, which offers a complement to the integrity of structural information in CT. Meanwhile, a mixed attention mechanism is introduced to better fuse the dual-branch features for decoding features to MR, where small structure features get more attention in channel and space. Additionally, a new joint loss function is proposed to guide the adversarial training of S-P GAN, which contains structural consistency constrain, pixel translation constrain, and adversarial constrain, so that global similarity and local detail consistency are obtained at the same time. Experimental results show that the results of the S-P GAN are superior to the state-of-the-art models in mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM). In real clinical situations, the proposed method has shown good performance in the diagnosis of lumbar disk herniation, the new and old degree of compressibility fracture, and the Modic change of cartilage end plate.

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Data Availability

The datasets generated during and/or analyzed during the current study are not available due to proprietary reasons.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant No. 62,273,163, the Key R &D Project of Shandong Province under Grant No. 2022CXGC010503, and the Youth Foundation of Shandong Province under Grant No. ZR202102230323.

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  1. Guangxin Dai and Junxiao Su have contributed equally to this work.

Authors and Affiliations

  1. School of Electrical Engineering, University of Jinan, Jinan, 250000, Shandong, China

    Guangxin Dai, Menghua Zhang & Weijie Huang

  2. Department of Orthopedics, Qilu Hospital of Shandong University, Jinan, 250012, Shandong, China

    Junxiao Su & Xinyu Liu

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  1. Guangxin Dai
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Dai, G., Su, J., Zhang, M. et al. A novel structure preserving generative adversarial network for CT to MR modality translation of spine. Neural Comput & Applic 36, 4101–4114 (2024). https://doi.org/10.1007/s00521-023-09254-w

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