Zeng, Rongping, Lin, Claire Yilin, Li, Qin, Jiang, Lu, Skopec, Marlene, Fessler, Jeffrey A., and Myers, Kyle J. Performance of a deep learning‐based CT image denoising method: Generalizability over dose, reconstruction kernel, and slice thickness. Retrieved from https://par.nsf.gov/biblio/10397185. Medical Physics 49.2 Web. doi:10.1002/mp.15430.

Zeng, Rongping, Lin, Claire Yilin, Li, Qin, Jiang, Lu, Skopec, Marlene, Fessler, Jeffrey A., & Myers, Kyle J. Performance of a deep learning‐based CT image denoising method: Generalizability over dose, reconstruction kernel, and slice thickness. Medical Physics, 49 (2). Retrieved from https://par.nsf.gov/biblio/10397185. https://doi.org/10.1002/mp.15430

Zeng, Rongping, Lin, Claire Yilin, Li, Qin, Jiang, Lu, Skopec, Marlene, Fessler, Jeffrey A., and Myers, Kyle J. "Performance of a deep learning‐based CT image denoising method: Generalizability over dose, reconstruction kernel, and slice thickness". Medical Physics 49 (2). Country unknown/Code not available. https://doi.org/10.1002/mp.15430. https://par.nsf.gov/biblio/10397185.

@article{osti_10397185,
place = {Country unknown/Code not available}, title = {Performance of a deep learning‐based CT image denoising method: Generalizability over dose, reconstruction kernel, and slice thickness}, url = {https://par.nsf.gov/biblio/10397185}, DOI = {10.1002/mp.15430}, abstractNote = {}, journal = {Medical Physics}, volume = {49}, number = {2}, author = {Zeng, Rongping and Lin, Claire Yilin and Li, Qin and Jiang, Lu and Skopec, Marlene and Fessler, Jeffrey A. and Myers, Kyle J.}, }