@prefix dcat: . @prefix dct: . @prefix foaf: . @prefix vcard: . @prefix xsd: . a dcat:Dataset ; dct:description """ Computerized tomography (CT) aims at the retrieval of 3-D information from a set of projections acquired at different angles around the object of interest (OOI). One of its most common applications, which is the framework of this Ph.D. thesis, is X-ray CT medical imaging. This reconstruction can be severely impaired by the patient’s breath (respiratory) motion and cardiac beating. This is a major challenge in radiotherapy, where the precise localization of the tumor is a prerequisite for cancer cells irradiation with preservation of surrounding healthy tissues. The field of methods dealing with the reconstruction of a dynamic sequence of the OOI is called Dynamic CT. Some state-of-the-art methods increase the number of projections, allowing an independent reconstruction of several phases of the time sampled sequence. Other methods use motion compensation in the reconstruction, by a beforehand estimation on a previous data set, getting the explicit motion through a deformation model. Our work takes a different path ; it uses dynamic reconstruction, based on inverse problems theory, without any additional information, nor explicit knowledge of the motion. The dynamic sequence is reconstructed out of a single data set, only assuming the motion’s continuity and periodicity. This inverse problem is considered as a minimization of an error term combined with a regularization. One of the most original features of this Ph.D. thesis, typical of dynamic CT, is the elaboration of a reconstruction method from very sparse data, using Total Variation (TV) as a very efficient regularization term. We also implement a new rigorously defined and computationally efficient tomographic projector, based on B-splines separable functions, outperforming usual reconstruction quality in a data sparsity context. This reconstruction method is then inserted into a coherent dynamic reconstruction scheme, applying an efficient spatio-temporal TV regularization. Our method exploits current data information only, in an optimal way ; moreover, its implementation is rather straightforward. We first demonstrate the strength of our approach on 2-D+t reconstructions from numerically simulated dynamic data. Then the practical feasibility of our method is established on 2-D and 3-D+t reconstructions of a mechanical phantom and real patient data """ ; dct:identifier "NNT: 2013STET4006" ; dct:issued "2026-05-05T10:01:52.455298"^^xsd:dateTime ; dct:language "fr" ; dct:modified "2026-05-05T10:01:52.455302"^^xsd:dateTime ; dct:publisher ; dct:title "Reconstruction in dynamic tomography by an inverse approach without motion compensation" ; dcat:contactPoint [ a vcard:Organization ; vcard:fn "CCSD" ] ; dcat:distribution ; dcat:keyword "b-splines", "bio-medical-imaging", "dynamic-tomography", "imagerie-medicale", "infoeu-reposemanticsdoctoralthesis", "inverse-problems", "physcondcm-genphysics-physicscondensed-matter-cond-matother-cond-matother", "problemes-inverses", "reconstruction", "signal-processing", "theses", "tomographie", "tomographie-dynamique", "tomography", "traitement-du-signal" ; dcat:landingPage . a dcat:Distribution ; dct:format "HTML" ; dct:issued "2026-05-05T10:01:52.462819"^^xsd:dateTime ; dct:modified "2026-05-05T10:01:52.442357"^^xsd:dateTime ; dct:title "Reconstruction in dynamic tomography by an inverse approach without motion compensation" ; dcat:accessURL . a foaf:Agent ; foaf:name "test_moissonnage_selune" . a foaf:Document .