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Generation of synthetic tomographic images from biplanar X-ray: a narrative review of history, methods, and the state of the art.

Generation of synthetic tomographic images from biplanar X-ray: a narrative review of history, methods, and the state of the art. Journal of neurosurgical sciences Alakmeh, A., Zanier, O., Bottini, M., Gandia-Gonzalez, M. L., Burström, G., Edström, E., Elmi Terander, A., Schonfeld, E., Veeravagu, A., Regli, L., Serra, C., Staartjes, V. E. 2025; 69 (4): 350-361

Abstract

This narrative review presents deep learning-based strategies for generating synthetic 3D CT-like images from biplanar or multiplanar 2D X-ray data. Current limitations of conventional CT imaging are discussed, hence emphasizing the potential of synthetic CT reconstruction as an alternative technique in certain scenarios. Previous non deep learning approaches for 3D reconstruction from 2D X-rays are presented, indicating their weaknesses and thus pointing out the potential benefits of deep learning techniques. Convolutional neural networks (CNNs), generative adversarial networks (GANs), and conditional diffusion processing (CDP) are introduced, as they demonstrate great potential for synthetic CT generation in multiple studies over the last few years. The review further presents the potential clinical applications, existing challenges and latest research advancements of deep learning strategies for 3D reconstruction from 2D X-rays.

View details for DOI 10.23736/S0390-5616.25.06506-3

View details for PubMedID 40662246