Thomas Lab at CUAnschutz / TJU

Department of Radiation Oncology, CUAnschutz

A fast and effective denoising solution using deep learning for real time X-ray Acoustic Computed Tomography


Journal article


David Thomas, Farnoush Forghani, Adam Mahl, Bernard Jones, Mark Borden, Moyed Miften
Bulletin of the American Physical Society, vol. 65, APS, 2020

Cite

Cite

APA   Click to copy
Thomas, D., Forghani, F., Mahl, A., Jones, B., Borden, M., & Miften, M. (2020). A fast and effective denoising solution using deep learning for real time X-ray Acoustic Computed Tomography. Bulletin of the American Physical Society, 65.


Chicago/Turabian   Click to copy
Thomas, David, Farnoush Forghani, Adam Mahl, Bernard Jones, Mark Borden, and Moyed Miften. “A Fast and Effective Denoising Solution Using Deep Learning for Real Time X-Ray Acoustic Computed Tomography.” Bulletin of the American Physical Society 65 (2020).


MLA   Click to copy
Thomas, David, et al. “A Fast and Effective Denoising Solution Using Deep Learning for Real Time X-Ray Acoustic Computed Tomography.” Bulletin of the American Physical Society, vol. 65, APS, 2020.


BibTeX   Click to copy

@article{thomas2020a,
  title = {A fast and effective denoising solution using deep learning for real time X-ray Acoustic Computed Tomography},
  year = {2020},
  journal = {Bulletin of the American Physical Society},
  publisher = {APS},
  volume = {65},
  author = {Thomas, David and Forghani, Farnoush and Mahl, Adam and Jones, Bernard and Borden, Mark and Miften, Moyed}
}