A. Kak and M. Slaney, Principles of Computerized Tomographic Imaging, 2001.

D. Brenner and E. Hall, Computed Tomography ??? An Increasing Source of Radiation Exposure, New England Journal of Medicine, vol.357, issue.22, pp.2277-2284, 2007.
DOI : 10.1056/NEJMra072149

R. Smith-bindman, J. Lipson, and R. Marcus, Radiation Dose Associated With Common Computed Tomography Examinations and the Associated Lifetime Attributable Risk of Cancer, Archives of Internal Medicine, vol.169, issue.22, pp.2078-2086, 2009.
DOI : 10.1001/archinternmed.2009.427

M. Kalra and M. Michael, Strategies for CT Radiation Dose Optimization, Radiology, vol.230, issue.3, pp.619-628, 2004.
DOI : 10.1148/radiol.2303021726

M. Yazdi and L. Beaulieu, Artifacts in Spiral X-ray CT Scanners: Problems and Solutions, International Journal of Biological and Medical Sciences, vol.4, pp.135-139, 2008.

J. Xu, M. M. Tsui, and B. , Is Iterative Reconstruction Ready for MDCT?, Journal of the American College of Radiology, vol.6, issue.4, pp.274-276, 2009.
DOI : 10.1016/j.jacr.2008.12.014

J. Thibault, K. Sauer, and C. Bouman, A three-dimensional statistical approach to improved image quality for multislice helical CT, Medical Physics, vol.6498, issue.11, pp.4526-4544, 2007.
DOI : 10.1088/0031-9155/48/3/305

E. Sidky and X. Pan, Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization, Physics in Medicine and Biology, vol.53, issue.17, pp.4777-4807, 2008.
DOI : 10.1088/0031-9155/53/17/021

Y. Chen, Q. Feng, and L. Luo, Nonlocal Prior Bayesian Tomographic Reconstruction, Journal of Mathematical Imaging and Vision, vol.4, issue.4, pp.133-146, 2008.
DOI : 10.1007/s10851-007-0042-5

Y. Chen, L. Luo, and W. Chen, Bayesian statistical reconstruction for low-dose X-ray computed tomography using an adaptive-weighting nonlocal prior, Computerized Medical Imaging and Graphics, vol.33, issue.7, pp.495-500, 2009.
DOI : 10.1016/j.compmedimag.2008.12.007

M. Lubner, P. Pickhardt, and J. Tang, Reduced Image Noise at Low-Dose Multidetector CT of the Abdomen with Prior Image Constrained Compressed Sensing Algorithm, Radiology, vol.260, issue.1, pp.248-256, 2011.
DOI : 10.1148/radiol.11101380

A. Silva, H. Lawder, and A. Hara, Innovations in CT Dose Reduction Strategy: Application of the Adaptive Statistical Iterative Reconstruction Algorithm, American Journal of Roentgenology, vol.194, issue.1, pp.191-199, 2010.
DOI : 10.2214/AJR.09.2953

O. Rapalino and S. Kamalian, Cranial CT with Adaptive Statistical Iterative Reconstruction: Improved Image Quality with Concomitant Radiation Dose Reduction, American Journal of Neuroradiology, vol.33, issue.4, pp.609-615, 2012.
DOI : 10.3174/ajnr.A2826

K. Kilic, G. Erbas, and M. Guryildirim, Lowering the Dose in Head CT Using Adaptive Statistical Iterative Reconstruction, American Journal of Neuroradiology, vol.32, issue.9, pp.1578-1582, 2011.
DOI : 10.3174/ajnr.A2585

A. Korn, M. Fenchel, and B. Bender, Iterative Reconstruction in Head CT: Image Quality of Routine and Low-Dose Protocols in Comparison with Standard Filtered Back-Projection, American Journal of Neuroradiology, vol.33, issue.2, pp.218-224, 2012.
DOI : 10.3174/ajnr.A2749

T. Wu, S. Hung, and J. Sun, How far can the radiation dose be lowered in head CT with iterative reconstruction? Analysis of imaging quality and diagnostic accuracy, European Radiology, vol.28, issue.9, pp.330-343, 1007.
DOI : 10.1007/s00330-013-2846-6

J. Leipsic, G. Nguyen, and J. Brown, A Prospective Evaluation of Dose Reduction and Image Quality in Chest CT Using Adaptive Statistical Iterative Reconstruction, American Journal of Roentgenology, vol.195, issue.5, pp.1095-99, 2010.
DOI : 10.2214/AJR.09.4050

S. Singh, M. Kalra, and J. Hsieh, Abdominal CT: Comparison of Adaptive Statistical Iterative and Filtered Back Projection Reconstruction Techniques, Radiology, vol.257, issue.2, pp.373-383, 2010.
DOI : 10.1148/radiol.10092212

M. Lewic, B. Olshausen, and D. Field, Emergence of simple-cell receptive field properties by learning a sparse code for natural images, Nature, vol.381, pp.607-609, 1996.

M. Lewicki and T. Sejnowski, Learning Overcomplete Representations, Neural Computation, vol.33, issue.2, pp.337-365, 2000.
DOI : 10.1109/18.119725

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

K. Delgado and J. Murray, Dictionary Learning Algorithms for Sparse Representation, Neural Computation, vol.15, issue.2, pp.349-396, 2003.
DOI : 10.1162/089976601300014385

D. Donoho and D. Elad, Optimally sparse representation in general (nonorthogonal) dictionaries via ??1 minimization, Proceedings of the National Academy of Sciences, vol.100, issue.5, pp.2197-2202, 2003.
DOI : 10.1073/pnas.0437847100

M. Elad and A. M. , Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries, IEEE Transactions on Image Processing, vol.15, issue.12, pp.3736-3745, 2006.
DOI : 10.1109/TIP.2006.881969

J. Mairal, M. Elad, and G. Sapiro, Sparse Representation for Color Image Restoration, IEEE Transactions on Image Processing, vol.17, issue.1, pp.53-69, 2007.
DOI : 10.1109/TIP.2007.911828

J. Mairal, G. Sapiro, and M. Elad, Learning Multiscale Sparse Representations for Image and Video Restoration, Multiscale Modeling & Simulation, vol.7, issue.1, pp.214-241, 2008.
DOI : 10.1137/070697653

J. Wright, A. Yang, and A. Ganesh, Robust Face Recognition via Sparse Representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.31, issue.2, pp.210-227, 2008.
DOI : 10.1109/TPAMI.2008.79

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

S. Ravishankar and Y. Bresler, MR Image Reconstruction From Highly Undersampled k-Space Data by Dictionary Learning, IEEE Transactions on Medical Imaging, vol.30, issue.5, pp.1028-1041, 2011.
DOI : 10.1109/TMI.2010.2090538

L. Ma, L. Moisan, and J. Yu, A Dictionary learning approach for Poisson image deblurring, IEEE Transactions on Medical Imaging, vol.32, pp.1277-1289, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00834517

H. Yu and G. Wang, Compressed sensing based interior tomography, Physics in Medicine and Biology, vol.54, issue.9, pp.2791-805, 2009.
DOI : 10.1088/0031-9155/54/9/014

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2863332

Q. Xu, H. Yu, and X. Mou, Low-Dose X-ray CT Reconstruction via Dictionary Learning, IEEE Transaction on Medical Imaging, vol.31, pp.1682-1697, 2012.

S. Li, L. Fang, and H. Yin, An efficient dictionary learning algorithm and its application to 3-D medical image denoising, IEEE Transaction on Biomedical Engineering, vol.59, pp.417-427, 2012.

Y. Lu, J. Zhao, and G. Wang, Few-view image reconstruction with dual dictionaries, Physics in Medicine and Biology, vol.57, issue.1, pp.173-189, 2012.
DOI : 10.1088/0031-9155/57/1/173

B. Zhao, H. Ding, Y. Lu, and G. Wang, Dual-dictionary learning-based iterative image reconstruction for spectral computed tomography application, Physics in Medicine and Biology, vol.57, issue.24, pp.8217-8229, 2012.
DOI : 10.1088/0031-9155/57/24/8217

Y. Chen, X. Yin, and L. Shi, Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing, Physics in Medicine and Biology, vol.58, issue.16, pp.5803-5820, 2013.
DOI : 10.1088/0031-9155/58/16/5803

URL : https://hal.archives-ouvertes.fr/inserm-00874944

D. E. Heaney and C. Norvill, A Comparison of reduction in CT dose through the use of gantry angulations or bismuth shields, Australasian Physics & Engineering Sciences in Medicine, vol.77, issue.5, pp.172-178, 2006.
DOI : 10.1007/BF03178890

K. Jessen, W. Panzer, and P. Shrimpton, European Guidelines on Quality Criteria for Computed Tomography. Paper presented at: Office for Official Publications of the European Communities, 2000.

P. Shrimpton, M. Hillier, and M. Lewis, National survey of doses from CT in the UK: 2003, The British Journal of Radiology, vol.79, issue.948, pp.968-980, 2006.
DOI : 10.1259/bjr/93277434

M. Hakansson, S. Svensson, and S. Zachrisson, ViewDEX 2.0: a Java-based DICOM-compatible software for observer performance studies, Medical Imaging 2009: Image Perception, Observer Performance, and Technology Assessment, pp.72631-72632, 2009.
DOI : 10.1117/12.811511

Y. Chen, Z. Yang, and Y. Hu, Thoracic low-dose CT image processing using an artifact suppressed large-scale nonlocal means, Physics in Medicine and Biology, vol.57, issue.9, pp.2667-2688, 2012.
DOI : 10.1088/0031-9155/57/9/2667

URL : https://hal.archives-ouvertes.fr/inserm-00677979

Q. Feng, M. Foskey, and W. Chen, Segmenting CT prostate images using population and patient-specific statistics for radiotherapy, Medical Physics, vol.11, issue.8, pp.4121-4132, 2010.
DOI : 10.1118/1.3464799

J. Yang, Y. Wang, and W. Chen, Multiresolution Elastic Registration of X-Ray Angiography Images Using Thin-Plate Spline, IEEE Transactions on Nuclear Science, vol.54, issue.1, pp.152-166, 2007.
DOI : 10.1109/TNS.2006.889161