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Research on multi-sequence MR image synthesis CT algorithm based on unsupervised method
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DOI：10.1016/j.bspc.2024.106819.png)
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Abstract
En 中文
Background: Using multi-sequence MRI to synthesize CT, combining different MRI sequences, and processing synthetic CT (sCT) based on unsupervised algorithms, to conduct more in-depth and comprehensive research on MR-based radiotherapy planning. Methods: First, we compare and analyze the effects of single-sequence MRI synthesis to CT to determine the quality of the synthesis and find a set of optimal sequence combinations. In addition, the study introduces a Staged Multi-Sequence Fusion Network (SMSF-Net) that employs an unsupervised approach to effectively fuse information from multiple MRI sequences. The staged sequence fusion module can efficiently extract information from different sequences and combine complementary information from different sequences. Finally, image encoding is processed using a multi-layer convolutional network to generate synthetic CT images. Results: In this study, the mean absolute error (MAE), root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) of the SMSF-Net are 18.85, 84.62, 34.31, and 0.962, which are superior to other single-sequence models and multi-sequence models. Experiments show that the improvement of SMSFNet is statistically significant. In the qualitative comparison experiment, sCT synthesized by SMSF-Net is superior to other models in detail in the synthesis of complex areas of bones and nasal cavities. Conclusion: This study proposes a Staged SMSF-Net. As a multi-sequence MRI synthesis sCT algorithm based on unsupervised learning, SMSF-Net can use the complementary information between multiple sequences to improve the quality of synthetic sCT. This study has reference value for MRI-only radiotherapy planning.
Keywords:
CT synthesis
Multi-sequence MRI
Medical image translation
MRI-only based radiotherapy
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