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Journal of Digital Imaging|September 14, 2019
Deep Learning for Low-Dose CT Denoising Using Perceptual Loss and Edge Detection LayerMaryam Gholizadeh-Ansari, Javad Alirezaie, Paul BabynJournal of Digital Imaging|September 15, 2019
Imaging Properties of Additive Manufactured (3D Printed) Materials for Potential Use for Phantom ModelsElizabeth Silvestro, Khalil N Betts, Michael L Francavilla, et al.Journal of Digital Imaging|August 28, 2019
Improving Image Resolution of Whole-Heart Coronary MRA Using Convolutional Neural NetworkHiroki Kobayashi, Ryohei Nakayama, Akiyoshi Hizukuri, et al.Journal of Digital Imaging|August 15, 2019
Can a Machine Learn from Radiologists' Visual Search Behaviour and Their Interpretation of Mammograms-a Deep-Learning StudySuneeta Mall, Patrick C Brennan, Claudia Mello-ThomsJournal of Digital Imaging|November 1, 1988
Why bother with a computerized scheduling system?R ArensonJournal of Digital Imaging|November 1, 1988
Local area networks for radiologyS J Dwyer, N J Mankovich, G G Cox, et al.Journal of Digital Imaging|November 1, 1988
Radiology systems of the nineties: meeting the challenge of changeG S Lodwick, J L TaaffeJournal of Digital Imaging|April 12, 2011
Evaluation of novel genetic algorithm generated schemes for positron emission tomography (PET)/magnetic resonance imaging (MRI) image fusionK G Baum, E Schmidt, K Rafferty, et al.Journal of Digital Imaging|April 23, 2011
Characterizing the clustered microcalcifications on mammograms to predict the pathological classification and grading: a mathematical modeling approachYuan-Zhi Shao, Li-Zhi Liu, Meng-Jie Bie, et al.Journal of Digital Imaging|March 2, 2011
Multiresolution reconstruction for cone-beam tomography from raw data projections using 3D ridgeletsFrancisco Gómez, Cristina Santa Marta, Eduardo RomeroPageof 266