A Perspective on Deep Imaging
Top Cited Papers
Open Access
- 3 November 2016
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 4, 8914-8924
- https://doi.org/10.1109/access.2016.2624938
Abstract
The combination of tomographic imaging and deep learning, or machine learning in general, promises to empower not only image analysis but also image reconstruction. The latter aspect is considered in this perspective article with an emphasis on medical imaging to develop a new generation of image reconstruction theories and techniques. This direction might lead to intelligent utilization of domain knowledge from big data, innovative approaches for image reconstruction, and superior performance in clinical and preclinical applications. To realize the full impact of machine learning for tomographic imaging, major theoretical, technical and translational efforts are immediately needed.Keywords
Other Versions
This publication has 29 references indexed in Scilit:
- Achieving Routine Submillisievert CT Scanning: Report from the Summit on Management of Radiation Dose in CTRadiology, 2012
- Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic ScreeningThe New England Journal of Medicine, 2011
- Image reconstruction from a small number of projectionsInverse Problems, 2008
- A boundary-representation method for designing whole-body radiation dosimetry models: pregnant females at the ends of three gestational periods—RPI-P3, -P6 and -P9Physics in Medicine & Biology, 2007
- A Fast Learning Algorithm for Deep Belief NetsNeural Computation, 2006
- An improved exact filtered backprojection algorithm for spiral computed tomographyAdvances in Applied Mathematics, 2004
- Neural network reconstruction of single-photon emission computed tomography imagesJournal of Digital Imaging, 1995
- An artificial neural network approach to quantitative single photon emission computed tomographic reconstruction with collimator, attenuation, and scatter compensationMedical Physics, 1994
- Approximation capabilities of multilayer feedforward networksNeural Networks, 1991
- Multilayer feedforward networks are universal approximatorsNeural Networks, 1989