Quantitative Susceptibility Map Reconstruction via a Total Generalized Variation Regularization

Simple item page
dc.contributor.authorYáñez Lang, Felipe Andrés
dc.contributor.authorFan, Audrey
dc.contributor.authorBilgic, Berkin
dc.contributor.authorMilovic Fabregat, Carlos Andrés
dc.contributor.authorAdalsteinsson, Elfar
dc.contributor.authorIrarrázaval Mena, Pablo
dc.date.accessioned2022-05-11T20:05:44Z
dc.date.available2022-05-11T20:05:44Z
dc.date.issued2013
dc.description.abstractQuantitative susceptibility mapping (QSM) is a last decade new concept which allows to determine the magnetic susceptibility distribution of tissue in-vivo. Nowadays it has several applications such as venous blood oxygenation and iron concentration quantification. To reconstruct high quality maps, a regularized scheme must be used to solve this ill-posed problem, due to the dipole kernel under sampling k-space. A widely used regularization penalty is Total Variation (TV), however, we can find stair casing artifacts in reconstructions due to the assumption that images are piecewise constant, not always true in MRI. In this sense, we propose a less restrictive functional, to avoid this problem and to improve QSM quality. A second order Total Generalized Variation (TGV) does not assume piecewise constancy in the images and is equivalent to TV in terms of edge preservation and noise removal. This work describes how TGV penalty addresses an increase in imaging efficiency in magnetic susceptibility maps from numerical phantom and in-vivo data. Currently, we report higher specificity with the proposed regularization. Moreover, the robustness of TGV suggest that is a possible alternative to tissue susceptibility mapping.
dc.fuente.origenIEEE
dc.identifier.doi10.1109/PRNI.2013.59
dc.identifier.isbn978-0769550619
dc.identifier.urihttps://doi.org/10.1109/PRNI.2013.59
dc.identifier.urihttps://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6603591
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/63763
dc.information.autorucEscuela de ingeniería ; Yáñez Lang, Felipe Andrés ; S/I ; 171128
dc.information.autorucEscuela de ingeniería ; Milovic Fabregat, Carlos Andrés ; S/I ; 120377
dc.information.autorucEscuela de ingeniería ; Irarrázaval Mena, Pablo ; S/I ; 57376
dc.language.isoen
dc.nota.accesoContenido parcial
dc.publisherIEEE
dc.relation.ispartofInternational Workshop on Pattern Recognition in Neuroimaging (2013 : Philadelphia, PA, Estados Unidos)
dc.rightsacceso restringido
dc.subjectTV
dc.subjectImage reconstruction
dc.subjectMagnetic resonance
dc.subjectBiomedical imaging
dc.subjectMagnetic resonance imaging
dc.subjectMagnetic susceptibility
dc.subjectEquations
dc.titleQuantitative Susceptibility Map Reconstruction via a Total Generalized Variation Regularizationes_ES
dc.typecomunicación de congreso
sipa.codpersvinculados171128
sipa.codpersvinculados120377
sipa.codpersvinculados57376
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Quantitative Susceptibility Map Reconstruction via a Total Generalized Variation Regularization.pdf
Size:
2.97 KB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Artículos de conferencia