XProspeCT: CT Volume Generation from Paired X-Rays

Paulson, Benjamin, Goldshteyn, Joshua, Balboni, Sydney, Cisler, John, Crisler, Andrew, Bukowski, Natalia, Kalish, Julia, Colwell, Theodore

arXiv.org Artificial Intelligence 

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.

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