Enhancing Gamma Knife Cone-beam Computed Tomography Image Quality Using Pix2pix Generative Adversarial Networks: A Deep Learning Approach
The sCT images show reduced noise and artifacts, closely matching CT in HU values, and demonstrate a high degree of similarity to CT images, highlighting the potential of deep learning to significantly improve CBCT image quality for radiosurgery applications.
Authors
- Matthew Foote (ORCID: https://orcid.org/0000-0001-9025-2639)
- Michael Huo (ORCID: https://orcid.org/0000-0003-4075-2648)
- Prabhakar Ramachandran
- Mark B. Pinkham (ORCID: https://orcid.org/0000-0001-8886-728X)
- Daniel Arrington (ORCID: https://orcid.org/0009-0009-2896-2950)
- Darcie Anderson
- Andrew L. Fielding (ORCID: https://orcid.org/0000-0001-9955-4609)
- Zachery Morton Colbert (ORCID: https://orcid.org/0009-0009-1173-2577)
Institutions
- Queensland University of Technology (AU)
- Princess Alexandra Hospital (AU)
Publication Details
- Journal
- Journal of Medical Physics
- Published
- 2025-01-01
- DOI
- https://doi.org/10.4103/jmp.jmp_140_24
- Primary Topic
- Advanced X-ray and CT Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00