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Keywords: Low-dose CT
BReP-SNAP-M-84Image Processing System by Super-Resolution Using Deep Learning Leading to Exposure Dose Reduction
H Miyauchi1,2*, Y Tanaka1, K Takahashi1, M Nakano2, T Hasegawa3, M Hashimoto3, (1) Kitasato University Graduate School of Medical Sciences, Sagamihara, Kanagawa, JP, (2) Cancer Institute Hospital of JFCR, Koto-ku, Tokyo, JP, (3) Faculty of Allied Health Sciences, Kitasato university, Sagamihara, Kanagawa, JP
PO-GeP-I-233Variations in Image Artifacts at Ultra-Low Radiation Dose Levels Due to Differences in Scanner Make and Model: Implications for CT Screening Applications
J Browne, M Bruesewitz, Z Long*, T Vrieze, C McCollough, L Yu, Mayo Clinic, Rochester, MN
SU-B-TRACK 2-45DCT Reconstruction Accuracy and Elasticity Estimation Performance for Low Dose Fast-Helical Free Breathing CT
M Lauria1*, B Stiehl1, D O'Connell1, S Hsieh2, I Barjaktarevic1, A Santhanam1, D Low1, (1) UCLA, Los Angeles, CA, (2) Mayo Clinic, Rochester, MN
SU-E-TRACK 1-1BEST IN PHYSICS (IMAGING): Comparison of Loss Functions in Dual-Domain Convolutional Neural Networks for Low-Dose CT Enhancement
KJ Chung1-3*, R Souza4,5, R Frayne4,5, TY Lee1-3, (1) University of Western Ontario, London, ON, CA, (2) Robarts Research Institute, London, ON, CA, (3) Lawson Health Research Institute, London, ON, CA, (4) University Of Calgary, Calgary, AB, CA, (5) Foothills Medical Centre, Calgary, AB, CA
SU-E-TRACK 1-2Phantom-Based Training Framework for Deep Convolutional Neural Network CT Noise Reduction
N Huber*, A Missert, H Gong, S Leng, L Yu, C McCollough, Mayo Clinic, Rochester, MN
WE-C-TRACK 1-4Validation of a Two-Volume Dynamic CT Renal Perfusion Technique
B Flynn*, Y Zhao, L Hubbard, S Malkasian, P Abbona, S Molloi, University of California-Irvine, Irvine, CA