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| MO-K-DBRB-2 | Improved PET Quantification by Adaptive Denoising and Regularized Image Reconstruction: A Feasibility Study M Namias1*, R Jeraj2 , (1) Fundacion Centro Diagnostico Nuclear, Buenos Aires, Argentina, (2) University of Wisconsin, Madison, WI |
| SU-E-205-4 | Impact of Noise On CT Readers B Whiting1*, S Don2 , D Politte3 , A Mitra4 , C Abbey5 , (1) University of Pittsburgh, Pittsburgh, PA, (2) Washington University School of Medicine, St. Louis, Missouri, (3) Washington University School of Medicine, Saint Louis, MO, (4) Washington University, Saint Louis, MO, (5) UC Santa Barbara, Santa Barbara, CA |
| TH-AB-KDBRB1-4 | Deep Learning Based PET Image Noise Reduction Using Both PET and CT Information X Jin1*, J Fan1 , X Rui2 , (1) GE Healthcare, Waukesha, Wisconsin, (2) GE Global Research Center, Niskayuna, New York |
| TU-C930-GePD-F1-4 | Low Dose CT Strategies: In Which Domain Should I Denoise My Data? J Hayes , D Gomez-Cardona , J Cruz-Bastida , R Zhang*, K Li , G Chen , University of Wisconsin Madison, Madison, WI |
| WE-C1030-GePD-F8-3 | Content-Oriented Sparse Representation (COSR) Denoising in Perfusion CT for Dose Reduction Huiqiao Xie, Xiangyang Tang*, Emory University School of Medicine, Atlanta, GA |