- -

System matrix analysis for computed tomography imaging

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

System matrix analysis for computed tomography imaging

Mostrar el registro sencillo del ítem

Ficheros en el ítem

Thumbnail
Nombre: -Flores;Vicent ...
Tamaño: 7.237Mb
Formato: PDF
Descripción: Versión editorial
Abrir
dc.contributor.author Flores, Liubov Alexandrovna es_ES
dc.contributor.author Vicent Vidal es_ES
dc.contributor.author Verdú Martín, Gumersindo Jesús es_ES
dc.date.accessioned 2016-06-03T09:25:33Z
dc.date.available 2016-06-03T09:25:33Z
dc.date.issued 2015-11-17
dc.identifier.issn 1932-6203
dc.identifier.uri http://hdl.handle.net/10251/65185
dc.description.abstract In practical applications of computed tomography imaging (CT), it is often the case that the set of projection data is incomplete owing to the physical conditions of the data acquisition process. On the other hand, the high radiation dose imposed on patients is also undesired. These issues demand that high quality CT images can be reconstructed from limited projection data. For this reason, iterative methods of image reconstruction have become a topic of increased research interest. Several algorithms have been proposed for few-view CT. We consider that the accurate solution of the reconstruction problem also depends on the system matrix that simulates the scanning process. In this work, we analyze the application of the Siddon method to generate elements of the matrix and we present results based on real projection data. es_ES
dc.description.sponsorship This work has been supported by Universitat Politecnica de Valencia and partially funded by ANITRAN PROMETEOII/2014/008 of the Generalitat Valenciana of Spain. en_EN
dc.language Inglés es_ES
dc.publisher Public Library of Science es_ES
dc.relation.ispartof PLoS ONE es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Computed tomography imaging es_ES
dc.subject System matrix analysis es_ES
dc.subject Siddon es_ES
dc.subject Reconstruction es_ES
dc.subject.classification CIENCIAS DE LA COMPUTACION E INTELIGENCIA ARTIFICIAL es_ES
dc.subject.classification INGENIERIA NUCLEAR es_ES
dc.title System matrix analysis for computed tomography imaging es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1371/journal.pone.0143202
dc.relation.projectID info:eu-repo/grantAgreement/GVA//PROMETEOII%2F2014%2F008/ES/New improved capacities in 3d-VALKIN (Valencian Neutronic Kinetisc). N3D-VALKIN/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Química y Nuclear - Departament d'Enginyeria Química i Nuclear es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto de Seguridad Industrial, Radiofísica y Medioambiental - Institut de Seguretat Industrial, Radiofísica i Mediambiental es_ES
dc.description.bibliographicCitation Flores, LA.; Vicent Vidal; Verdú Martín, GJ. (2015). System matrix analysis for computed tomography imaging. PLoS ONE. 10(11):1-12. https://doi.org/10.1371/journal.pone.0143202 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/:10.1371/journal. pone.0143202 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 12 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 10 es_ES
dc.description.issue 11 es_ES
dc.relation.senia 298040 es_ES
dc.identifier.pmid 26575482 en_EN
dc.identifier.pmcid PMC4648504
dc.contributor.funder Generalitat Valenciana es_ES
dc.contributor.funder Universitat Politècnica de València es_ES
dc.description.references Herman, G. T. (2009). Fundamentals of Computerized Tomography. Advances in Pattern Recognition. doi:10.1007/978-1-84628-723-7 es_ES
dc.description.references Wang, G., Yu, H., & De Man, B. (2008). An outlook on x-ray CT research and development. Medical Physics, 35(3), 1051-1064. doi:10.1118/1.2836950 es_ES
dc.description.references Donoho, D. L. (2006). Compressed sensing. IEEE Transactions on Information Theory, 52(4), 1289-1306. doi:10.1109/tit.2006.871582 es_ES
dc.description.references Candès, E. J., Romberg, J. K., & Tao, T. (2006). Stable signal recovery from incomplete and inaccurate measurements. Communications on Pure and Applied Mathematics, 59(8), 1207-1223. doi:10.1002/cpa.20124 es_ES
dc.description.references Yu, H., & Wang, G. (2010). A soft-threshold filtering approach for reconstruction from a limited number of projections. Physics in Medicine and Biology, 55(13), 3905-3916. doi:10.1088/0031-9155/55/13/022 es_ES
dc.description.references Andersen, A. H., & Kak, A. C. (1984). Simultaneous Algebraic Reconstruction Technique (SART): A Superior Implementation of the Art Algorithm. Ultrasonic Imaging, 6(1), 81-94. doi:10.1177/016173468400600107 es_ES
dc.description.references Flores, L. A., Vidal, V., Mayo, P., Rodenas, F., & Verdú, G. (2013). CT Image Reconstruction Based on GPUs. Procedia Computer Science, 18, 1412-1420. doi:10.1016/j.procs.2013.05.308 es_ES
dc.description.references Flores, L., Vidal, V., & Verdú, G. (2015). Iterative Reconstruction from Few-view Projections. Procedia Computer Science, 51, 703-712. doi:10.1016/j.procs.2015.05.188 es_ES
dc.description.references Cibeles Mora Mora, T. Métodos de reconstrucción volumétrica algebraica de imágenes tomográficas. 2008. Tesis PhD. Spain. es_ES
dc.description.references Siddon, R. L. (1985). Fast calculation of the exact radiological path for a three-dimensional CT array. Medical Physics, 12(2), 252-255. doi:10.1118/1.595715 es_ES
dc.description.references Joseph, P. M. (1982). An Improved Algorithm for Reprojecting Rays through Pixel Images. IEEE Transactions on Medical Imaging, 1(3), 192-196. doi:10.1109/tmi.1982.4307572 es_ES
dc.description.references Paige, C. C., & Saunders, M. A. (1982). LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares. ACM Transactions on Mathematical Software, 8(1), 43-71. doi:10.1145/355984.355989 es_ES
dc.description.references Higham, N. J. (1993). The Accuracy of Floating Point Summation. SIAM Journal on Scientific Computing, 14(4), 783-799. doi:10.1137/0914050 es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem