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Foundations Of Image Science
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The article was published on 2016-01-01 and is currently open access. It has received 167 citations till now.read more
Citations
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Journal ArticleDOI
Deep Learning Techniques for Inverse Problems in Imaging
Gregory Ongie,Ajil Jalal,Christopher A. Metzler,Richard G. Baraniuk,Alexandros G. Dimakis,Rebecca Willett +5 more
TL;DR: A taxonomy that can be used to categorize different problems and reconstruction methods in deep neural networks and discusses the tradeoffs associated with these different reconstruction approaches, caveats and common failure modes.
Journal ArticleDOI
Image quality in CT: From physical measurements to model observers.
Francis R. Verdun,Damien Racine,Julien G. Ott,M.J. Tapiovaara,P. Toroi,François Bochud,Wouter J. H. Veldkamp,Wouter J. H. Veldkamp,Alexander Schegerer,Ramona W. Bouwman,I. Hernandez Giron,Nicholas Marshall,Sue Edyvean +12 more
TL;DR: The spectrum of various methods that have been used to characterise image quality in CT: from objective measurements of physical parameters to clinically task-based approaches (i.e. model observer (MO) approach) including pure human observer approach are presented.
BookDOI
Industrial X-Ray Computed Tomography
TL;DR: The concept of industrial computed tomography (CT) was introduced by Thompson et al. as discussed by the authors, and the industrial requirements of CT are discussed, and the content of the following chapters is briefly outlined.
Journal ArticleDOI
Pigeons (Columba livia) as Trainable Observers of Pathology and Radiology Breast Cancer Images.
TL;DR: It is reported here that pigeons (Columba livia)—which share many visual system properties with humans—can serve as promising surrogate observers of medical images, a capability not previously documented.
Posted Content
Deep Learning-Guided Image Reconstruction from Incomplete Data
TL;DR: An approach to incorporate deep learning within an iterative image reconstruction framework to reconstruct images from severely incomplete measurement data is presented, which utilizes a convolutional neural network as a quasi-projection operator within a least squares minimization procedure.
References
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Journal ArticleDOI
Deep Learning Techniques for Inverse Problems in Imaging
Gregory Ongie,Ajil Jalal,Christopher A. Metzler,Richard G. Baraniuk,Alexandros G. Dimakis,Rebecca Willett +5 more
TL;DR: A taxonomy that can be used to categorize different problems and reconstruction methods in deep neural networks and discusses the tradeoffs associated with these different reconstruction approaches, caveats and common failure modes.
Journal ArticleDOI
Image quality in CT: From physical measurements to model observers.
Francis R. Verdun,Damien Racine,Julien G. Ott,M.J. Tapiovaara,P. Toroi,François Bochud,Wouter J. H. Veldkamp,Wouter J. H. Veldkamp,Alexander Schegerer,Ramona W. Bouwman,I. Hernandez Giron,Nicholas Marshall,Sue Edyvean +12 more
TL;DR: The spectrum of various methods that have been used to characterise image quality in CT: from objective measurements of physical parameters to clinically task-based approaches (i.e. model observer (MO) approach) including pure human observer approach are presented.
BookDOI
Industrial X-Ray Computed Tomography
TL;DR: The concept of industrial computed tomography (CT) was introduced by Thompson et al. as discussed by the authors, and the industrial requirements of CT are discussed, and the content of the following chapters is briefly outlined.
Journal ArticleDOI
Pigeons (Columba livia) as Trainable Observers of Pathology and Radiology Breast Cancer Images.
TL;DR: It is reported here that pigeons (Columba livia)—which share many visual system properties with humans—can serve as promising surrogate observers of medical images, a capability not previously documented.
Posted Content
Deep Learning-Guided Image Reconstruction from Incomplete Data
TL;DR: An approach to incorporate deep learning within an iterative image reconstruction framework to reconstruct images from severely incomplete measurement data is presented, which utilizes a convolutional neural network as a quasi-projection operator within a least squares minimization procedure.