FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3211406483> ?p ?o ?g. }

• W3211406483 startingPage "1" @default.
• W3211406483 abstract "Magnetic Resonance Imaging (MRI) is a powerful diagnostic imaging modality for numerous diseases due to its versatility and sensitivity to multiple tissue properties. Nevertheless, it is often limited by the lengthy scan times required to collect the data necessary to form an image and it suffers from different types or artifacts arising from multiple causes. For this reason, reducing the scanning time has been one of the most active areas of research, but it might come at the cost of aggravating the effects of certain artifacts or introducing new ones. One way to reduce the acquisition time is by using Parallel Imaging techniques, which acquire only a portion of the data and rely on the availability of different coil channels to further reconstruct the images. However, these techniques require more complex reconstruction algorithms that result in the appearance of spatially varying noise maps. In order to mitigate the impact of noise degradation in subsequent parameter estimation it is important to characterize these noise maps. In particular, its exact characterization has been considered computationally infeasible under a widely used technique termed GRAPPA, which directly reconstructs the missing data in the sampled domain, the so called k–space. The reason lies on the need to carry out a noise propagation analysis through the reconstruction pipeline that involves very large covariance matrices. In this thesis, we show how to overcome this computational load and obtain an exact noise characterization both for 2D and 3D GRAPPA acquisitions by exploiting the presence of extensive symmetries and the block separability in the reconstruction steps. Another common approach to reduce the scan time is by means of Echo–Planar Imaging (EPI). In contrast to Spin–Warp Imaging, where one acquires one line of the k–space per excitation, EPI segments the acquisition into multiple shots by collecting several lines within a single excitation. This modality offers major advantages over conventional Spin–Warp Imaging, which include reduced imaging time, decreased motion artifacts and the ability to image rapid physiologic processes of the human body. In particular, it has become the standard modality in Diffusion MRI (dMRI). However, since dMRI is aimed at capturing the microscopic movements of water molecules, it is sensitive as well to any kind of bulk motion from the patient. Due to the way dMRI sequences are designed, the molecules motion is encoded in the phase of the spins and consequently bulk motion results in phase corruption of the images. If the motion differs from shot to shot, the resulting phase discrepancies lead into ghosting artifacts in the reconstructed images. In this thesis, we propose an algorithm based on a Maximum Likelihood formulation to iteratively reconstruct the images and estimate the phase–maps under the assumption of linearity or smoothness. In this dissertation we include the theoretical derivation of our models and the description of the proposed algorithms to determine the parameters of interest. Finally, simulations, phantom and in–vivo experiments are included to provide empirical support of the properties of our methods, as well as to compare them to previous state of the art approaches." @default.
• W3211406483 created "2021-11-22" @default.
• W3211406483 creator A5005381504 @default.
• W3211406483 date "2019-01-01" @default.
• W3211406483 modified "2023-09-26" @default.
• W3211406483 title "Artifact reduction in magnetic resonance imaging: noise modelling in 2d/3d grappa accelerated acquisitions and motion–induced ghosting correction in multishot diffusion mri" @default.
• W3211406483 hasPublicationYear "2019" @default.
• W3211406483 type Work @default.
• W3211406483 sameAs 3211406483 @default.
• W3211406483 citedByCount "0" @default.
• W3211406483 crossrefType "journal-article" @default.
• W3211406483 hasAuthorship W3211406483A5005381504 @default.
• W3211406483 hasConcept C111335779 @default.
• W3211406483 hasConcept C11413529 @default.
• W3211406483 hasConcept C115961682 @default.
• W3211406483 hasConcept C127413603 @default.
• W3211406483 hasConcept C136536468 @default.
• W3211406483 hasConcept C154945302 @default.
• W3211406483 hasConcept C163294075 @default.
• W3211406483 hasConcept C199360897 @default.
• W3211406483 hasConcept C21200559 @default.
• W3211406483 hasConcept C24326235 @default.
• W3211406483 hasConcept C2524010 @default.
• W3211406483 hasConcept C2779010991 @default.
• W3211406483 hasConcept C2780531524 @default.
• W3211406483 hasConcept C31972630 @default.
• W3211406483 hasConcept C33923547 @default.
• W3211406483 hasConcept C4069607 @default.
• W3211406483 hasConcept C41008148 @default.
• W3211406483 hasConcept C43521106 @default.
• W3211406483 hasConcept C99498987 @default.
• W3211406483 hasConceptScore W3211406483C111335779 @default.
• W3211406483 hasConceptScore W3211406483C11413529 @default.
• W3211406483 hasConceptScore W3211406483C115961682 @default.
• W3211406483 hasConceptScore W3211406483C127413603 @default.
• W3211406483 hasConceptScore W3211406483C136536468 @default.
• W3211406483 hasConceptScore W3211406483C154945302 @default.
• W3211406483 hasConceptScore W3211406483C163294075 @default.
• W3211406483 hasConceptScore W3211406483C199360897 @default.
• W3211406483 hasConceptScore W3211406483C21200559 @default.
• W3211406483 hasConceptScore W3211406483C24326235 @default.
• W3211406483 hasConceptScore W3211406483C2524010 @default.
• W3211406483 hasConceptScore W3211406483C2779010991 @default.
• W3211406483 hasConceptScore W3211406483C2780531524 @default.
• W3211406483 hasConceptScore W3211406483C31972630 @default.
• W3211406483 hasConceptScore W3211406483C33923547 @default.
• W3211406483 hasConceptScore W3211406483C4069607 @default.
• W3211406483 hasConceptScore W3211406483C41008148 @default.
• W3211406483 hasConceptScore W3211406483C43521106 @default.
• W3211406483 hasConceptScore W3211406483C99498987 @default.
• W3211406483 hasLocation W32114064831 @default.
• W3211406483 hasOpenAccess W3211406483 @default.
• W3211406483 hasPrimaryLocation W32114064831 @default.
• W3211406483 hasRelatedWork W112600944 @default.
• W3211406483 hasRelatedWork W1484399365 @default.
• W3211406483 hasRelatedWork W1985025296 @default.
• W3211406483 hasRelatedWork W2054744318 @default.
• W3211406483 hasRelatedWork W2068916130 @default.
• W3211406483 hasRelatedWork W2100577465 @default.
• W3211406483 hasRelatedWork W2157615822 @default.
• W3211406483 hasRelatedWork W2297651635 @default.
• W3211406483 hasRelatedWork W2312515137 @default.
• W3211406483 hasRelatedWork W2370458197 @default.
• W3211406483 hasRelatedWork W2538066401 @default.
• W3211406483 hasRelatedWork W2794991850 @default.
• W3211406483 hasRelatedWork W2807292136 @default.
• W3211406483 hasRelatedWork W2916814360 @default.
• W3211406483 hasRelatedWork W2964007518 @default.
• W3211406483 hasRelatedWork W3008517309 @default.
• W3211406483 hasRelatedWork W3026796014 @default.
• W3211406483 hasRelatedWork W3101240860 @default.
• W3211406483 hasRelatedWork W3161361406 @default.
• W3211406483 hasRelatedWork W2181443731 @default.
• W3211406483 isParatext "false" @default.
• W3211406483 isRetracted "false" @default.
• W3211406483 magId "3211406483" @default.
• W3211406483 workType "article" @default.