XProspeCT: CT Volume Generation from Paired X-Rays
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866916144665853952 |
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| author | Paulson, Benjamin Goldshteyn, Joshua Balboni, Sydney Cisler, John Crisler, Andrew Bukowski, Natalia Kalish, Julia Colwell, Theodore |
| author_facet | Paulson, Benjamin Goldshteyn, Joshua Balboni, Sydney Cisler, John Crisler, Andrew Bukowski, Natalia Kalish, Julia Colwell, Theodore |
| contents | Computed tomography (CT) is a beneficial imaging tool for diagnostic purposes. CT scans provide detailed information concerning the internal anatomic structures of a patient, but present higher radiation dose and costs compared to X-ray imaging. In this paper, we build on previous research to convert orthogonal X-ray images into simulated CT volumes by exploring larger datasets and various model structures. Significant model variations include UNet architectures, custom connections, activation functions, loss functions, optimizers, and a novel back projection approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_00771 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | XProspeCT: CT Volume Generation from Paired X-Rays Paulson, Benjamin Goldshteyn, Joshua Balboni, Sydney Cisler, John Crisler, Andrew Bukowski, Natalia Kalish, Julia Colwell, Theodore Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Medical Physics Computed tomography (CT) is a beneficial imaging tool for diagnostic purposes. CT scans provide detailed information concerning the internal anatomic structures of a patient, but present higher radiation dose and costs compared to X-ray imaging. In this paper, we build on previous research to convert orthogonal X-ray images into simulated CT volumes by exploring larger datasets and various model structures. Significant model variations include UNet architectures, custom connections, activation functions, loss functions, optimizers, and a novel back projection approach. |
| title | XProspeCT: CT Volume Generation from Paired X-Rays |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Medical Physics |
| url | https://arxiv.org/abs/2403.00771 |