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Proceedings Paper

Sparse reconstruction methods in x-ray CT
Author(s): J. F. P. J. Abascal; M. Abella; C. Mory; N. Ducros; C. de Molina; E. Marinetto; F. Peyrin; M. Desco
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Paper Abstract

Recent progress in X-ray CT is contributing to the advent of new clinical applications. A common challenge for these applications is the need for new image reconstruction methods that meet tight constraints in radiation dose and geometrical limitations in the acquisition. The recent developments in sparse reconstruction methods provide a framework that permits obtaining good quality images from drastically reduced signal-to-noise-ratio and limited-view data. In this work, we present our contributions in this field. For dynamic studies (3D+Time), we explored the possibility of extending the exploitation of sparsity to the temporal dimension: a temporal operator based on modelling motion between consecutive temporal points in gated-CT and based on experimental time curves in contrast-enhanced CT. In these cases, we also exploited sparsity by using a prior image estimated from the complete acquired dataset and assessed the effect on image quality of using different sparsity operators. For limited-view CT, we evaluated total-variation regularization in different simulated limited-data scenarios from a real small animal acquisition with a cone-beam microCT scanner, considering different angular span and number of projections. For other emerging imaging modalities, such as spectral CT, the image reconstruction problem is nonlinear, so we explored new efficient approaches to exploit sparsity for multi-energy CT data. In conclusion, we review our approaches to challenging CT data reconstruction problems and show results that support the feasibility for new clinical applications.

Paper Details

Date Published: 4 October 2017
PDF: 10 pages
Proc. SPIE 10391, Developments in X-Ray Tomography XI, 1039112 (4 October 2017); doi: 10.1117/12.2272711
Show Author Affiliations
J. F. P. J. Abascal, Univ. Lyon, INSA-Lyon, Univ. Claude Bernard Lyon 1, Univ. Jean Monnet Saint-Etienne, CREATIS, CNRS (France)
M. Abella, Univ. Carlos III de Madrid (Spain)
Instituto de Investigación Sanitaria Gregorio Marañón (Spain)
C. Mory, Univ. Lyon, INSA-Lyon, Univ. Claude Bernard Lyon 1, Univ. Jean Monnet Saint-Etienne, CREATIS, CNRS (France)
N. Ducros, Univ. Lyon, INSA-Lyon, Univ. Claude Bernard Lyon 1, Univ. Jean Monnet Saint-Etienne, CREATIS, CNRS (France)
C. de Molina, Univ. Carlos III de Madrid (Spain)
Instituto de Investigación Sanitaria Gregorio Marañón (Spain)
E. Marinetto, Univ. Carlos III de Madrid (Spain)
Instituto de Investigación Sanitaria Gregorio Marañón (Spain)
F. Peyrin, Univ. Lyon, INSA-Lyon, Univ. Claude Bernard Lyon 1, Univ. Jean Monnet Saint-Etienne, CREATIS, CNRS (France)
M. Desco, Univ. Carlos III de Madrid (Spain)
Instituto de Investigación Sanitaria Gregorio Marañón (Spain)
Ctr. de Investigación en Red de Salud Mental (Spain)


Published in SPIE Proceedings Vol. 10391:
Developments in X-Ray Tomography XI
Bert Müller; Ge Wang, Editor(s)

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