XProspeCT: CT Volume Generation from Paired X-Rays
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 4 of its 13 references that are in the map. First posted on arXiv 11 Feb 2024.
Builds on
4 of 13 references.
References · 4
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
- 2019CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparisoncited by 1,410
- 2019X2CT-GAN: Reconstructing CT from Biplanar X-Rays with Generative Adversarial Networkscited by 85
Built on by
Citing papers per year: 2024: 1.
1 paper.