XProspeCT: CT Volume Generation from Paired X-Rays
Benjamin Paulson Joshua Goldshteyn Sydney Balboni John Cisler Andrew Crisler Natalia Bukowski Julia Kalish Theodore Colwell
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.