Transformers for CT Reconstruction From Monoplanar and Biplanar Radiographs

• URL: http://arxiv.org/abs/2305.06965v1
• Date: Thu, 11 May 2023 16:43:39 GMT
• Title: Transformers for CT Reconstruction From Monoplanar and Biplanar Radiographs
• Authors: Firas Khader, Gustav M\"uller-Franzes, Tianyu Han, Sven Nebelung, Christiane Kuhl, Johannes Stegmaier, Daniel Truhn
• Abstract summary: We tackle the problem of reconstructing CT images from biplanar x-rays only. X-rays are widely available and even if the CT reconstructed from these radiographs is not a replacement of a complete CT in the diagnostic setting, it might serve to spare the patients from radiation. We propose a novel method based on the transformer architecture, by framing the underlying task as a language translation problem.
• Score: 0.11219061154635457
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Computed Tomography (CT) scans provide detailed and accurate information of internal structures in the body. They are constructed by sending x-rays through the body from different directions and combining this information into a three-dimensional volume. Such volumes can then be used to diagnose a wide range of conditions and allow for volumetric measurements of organs. In this work, we tackle the problem of reconstructing CT images from biplanar x-rays only. X-rays are widely available and even if the CT reconstructed from these radiographs is not a replacement of a complete CT in the diagnostic setting, it might serve to spare the patients from radiation where a CT is only acquired for rough measurements such as determining organ size. We propose a novel method based on the transformer architecture, by framing the underlying task as a language translation problem. Radiographs and CT images are first embedded into latent quantized codebook vectors using two different autoencoder networks. We then train a GPT model, to reconstruct the codebook vectors of the CT image, conditioned on the codebook vectors of the x-rays and show that this approach leads to realistic looking images. To encourage further research in this direction, we make our code publicly available on GitHub: XXX.

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