mlsanityData to 7 Oct 2026

XProspeCT: CT Volume Generation from Paired X-Rays

arXiv 2403.00771 ↗First posted 11 Feb 2024eess.IV cs.CV cs.LGcited by 1

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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

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2019CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparisoncited by 1,410
  4. 2019X2CT-GAN: Reconstructing CT from Biplanar X-Rays with Generative Adversarial Networkscited by 85

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Citing papers per year: 2024: 1.

1 paper.