Entry of taxonomy/keywords during proffered abstract submission was optional.
Not all abstracts will appear in search results.
|BReP-SNAP-M-84||Image 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-233||Variations 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 2-4||5DCT 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 1-1||BEST 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 1-2||Phantom-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 1-4||Validation 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