Show simple item record

Accurate Construction of MR-Based Patient-Specific Tissue Models: Clinical Application in Novel Imaging Modalities

dc.contributor.authorTorrado Carvajal, Ángel
dc.date.accessioned2016-09-22T14:10:30Z
dc.date.available2016-09-22T14:10:30Z
dc.date.issued2016
dc.identifier.urihttp://hdl.handle.net/10115/14105
dc.descriptionTesis Doctoral leída en la Universidad Rey Juan Carlos de Madrid en 2016. Directores de la Tesis: Norberto Malpica González de Vega y Juan Antonio Hernández Tamameses
dc.description.abstractImaging modalities have been evolving to constantly improve and adapt to diagnostic and research needs. New scanners have benefited from technological advancements increasing their acquisition speed, slice resolution, and image contrast. Scanners now produce better images that allow physicians to diagnose patients and carry out medical procedures with greater confidence. Ultra-High Field Magnetic Resonance Imaging (UHF-MRI) and simultaneous Positron Emission Tomography and Magnetic Resonance Imaging (PET/MR) offer exciting new possibilities to image the structure, function and biochemistry of the human body in far greater detail than has previously been possible. However, these new improved UHF-MRI and simultaneous PET/MR scanners present new challenges and limitations such as a greater amount of noise, presence of artifacts, image contrast issues, and safety concerns. In this Thesis we propose improvements to these two novel imaging modalities (UHF-MRI and PET/MR) and their clinical application. On one side, we will deal with the specific absorption rate (SAR), improving the safety in UHF-MRI scanners. The use of computer vision based techniques will allow to generate patient-specific tissue models, the application of which will allow to optimize image quality and resolution for every specific subject while reducing safety concerns. On the other side, we will obtain accurate attenuation correction (AC) maps, improving the accuracy in simultaneous PET/MR scanners. The use of computer vision based techniques will allow to generate patient-specific attenuation correction maps; the application of accurate AC maps will improve the quality of PET imaging in simultaneous PET/MR scanners, avoiding the need of CT images in the imaging protocol for in vivo molecular imaging. The final purpose of the developments in this Thesis is the clinical application. Translational technologies bridge the gap between scientific research and clinical reality, ensuring that the knowledge gained in research laboratories gets translated into a real benefit for the patient. We demonstrate the feasibility of performing an off-line patient-specific 7T MR acquisition planning based on previous MR images by using our patient-specific tissue modeling pipeline. We also show how the use of a computed patient-specific pseudo-CT allows determining accurate AC maps for use in simultaneous PET/MR systems. This approach avoids the over-simplification of most previous proposed methods. Thus, this Thesis describes the development and implementation of computer vision algorithms for accurate construction of MR-based patient-specific tissue models to deal with the SAR ¿improving the safety in UHF-MRI scanners¿, and to obtain accurate AC maps ¿improving the accuracy in simultaneous PET/MR scanners¿.es
dc.language.isoenges
dc.publisherUniversidad Rey Juan Carloses
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectTelecomunicacioneses
dc.titleAccurate Construction of MR-Based Patient-Specific Tissue Models: Clinical Application in Novel Imaging Modalitieses
dc.typeinfo:eu-repo/semantics/doctoralThesises
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.subject.unesco3325 Tecnología de las Telecomunicacioneses
dc.subject.unesco3201.11 Radiologíaes


Files in this item

This item appears in the following Collection(s)

Show simple item record

Atribución-NoComercial-SinDerivadas 3.0 EspañaExcept where otherwise noted, this item's license is described as Atribución-NoComercial-SinDerivadas 3.0 España