David Thomas

Associate Professor



720-848-0134


Department of Radiation Oncology

University of Colorado Anschutz Medical Campus

Department of Radiation Oncology – University of Colorado Denver | Anschutz Medical Campus
1665 Aurora Court, Suite 1032
Mail Stop F706
Aurora, CO 80045



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}
}

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