# DC2 Anet: Generating Lumbar Spine MR Images from CT Scan Data Based on Semi-Supervised Learning
## 3 Method
### Optimization of DC\({}^{2}\)Anet with Semi-Supervised Learning
From left to right: input CT image, synthesis MR images from multi-channel GAN [33], deep MR-to-CT [41], DiscoGAN [44], MR-GAN [45], the proposed DC\({}^{2}\)Anet, and the reference MR image. that our proposed method significantly outperformed four state-of-the-art approaches, thus providing its suitability for cross-modality image synthesis. Thus, it represents a very promising method that can be employed in the diagnosis of lumbar disc conditions for patients who are prevented from receiving an MRI due to claustrophobia or the presence of a cardiac pacemaker. Future research intends to further validate the quality of the synthesis results for downstream tasks such as segmentation or classification. Extending the method to handle cross-sectional views (axial, sagittal, and coronal) and multi-sequence CT images will also be considered in future work. Conceptualization, C.-B.J. and X.C. ; data curation, S.J. ; investigation, S.J. ; software, E.P. and Y.S.A. ; supervision, H.K. ; validation, I.H.H., J.I.L., and J.H.L. ; writing, original draft, C.-B.J. ; writing, review and editing, H.K., M.L. The authors declare no conflict of interest. ## References

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