T1-mapping from Variable Flip Angle Data Utilizing Constrained Model-based Reconstruction

Publikation: StudienabschlussarbeitMasterarbeitForschung

Abstract

Quantitative imaging techniques are a main topic of ongoing research in Magnetic Res-
onance Imaging (MRI). Current challenges involve the speed up of acquisition as well
as maintaining good accuracy. The present work describes a new accelerated T1 map-
ping method on the basis of model-based reconstruction for Variable Flip Angle (VFA)
data. The reconstruction problem is solved with an Iterative Regularized Gauss-Newton
(IRGN)-Total-Generalized-Variation (TGV) algorithm. Reconstructed parameter maps
for numerical, phantom, and in vivo knee data were in reasonable agreement with ref-
erence values up to a 12 fold acceleration. In order to minimize systematic errors it is
crucial to have knowledge of the exact flip angle distribution. The blurring at sharp
NMR-parameter changes provides an area for future improvements. In this context the
influence of the regularization functional could be subject of further investigations.
Originalspracheenglisch
QualifikationMaster of Science
Gradverleihende Hochschule
  • Institut für Medizintechnik (7170)
Betreuer/-in / Berater/-in
  • Stollberger, Rudolf, Betreuer
Datum der Bewilligung3 Mär 2016
PublikationsstatusVeröffentlicht - 3 Mär 2016

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blurring
imaging techniques
systematic errors
newton
acquisition

Dies zitieren

T1-mapping from Variable Flip Angle Data Utilizing Constrained Model-based Reconstruction. / Maier, Oliver.

2016.

Publikation: StudienabschlussarbeitMasterarbeitForschung

Maier, O 2016, 'T1-mapping from Variable Flip Angle Data Utilizing Constrained Model-based Reconstruction', Master of Science, Institut für Medizintechnik (7170).
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title = "T1-mapping from Variable Flip Angle Data Utilizing Constrained Model-based Reconstruction",
abstract = "Quantitative imaging techniques are a main topic of ongoing research in Magnetic Res-onance Imaging (MRI). Current challenges involve the speed up of acquisition as wellas maintaining good accuracy. The present work describes a new accelerated T1 map-ping method on the basis of model-based reconstruction for Variable Flip Angle (VFA)data. The reconstruction problem is solved with an Iterative Regularized Gauss-Newton(IRGN)-Total-Generalized-Variation (TGV) algorithm. Reconstructed parameter mapsfor numerical, phantom, and in vivo knee data were in reasonable agreement with ref-erence values up to a 12 fold acceleration. In order to minimize systematic errors it iscrucial to have knowledge of the exact flip angle distribution. The blurring at sharpNMR-parameter changes provides an area for future improvements. In this context theinfluence of the regularization functional could be subject of further investigations.",
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N2 - Quantitative imaging techniques are a main topic of ongoing research in Magnetic Res-onance Imaging (MRI). Current challenges involve the speed up of acquisition as wellas maintaining good accuracy. The present work describes a new accelerated T1 map-ping method on the basis of model-based reconstruction for Variable Flip Angle (VFA)data. The reconstruction problem is solved with an Iterative Regularized Gauss-Newton(IRGN)-Total-Generalized-Variation (TGV) algorithm. Reconstructed parameter mapsfor numerical, phantom, and in vivo knee data were in reasonable agreement with ref-erence values up to a 12 fold acceleration. In order to minimize systematic errors it iscrucial to have knowledge of the exact flip angle distribution. The blurring at sharpNMR-parameter changes provides an area for future improvements. In this context theinfluence of the regularization functional could be subject of further investigations.

AB - Quantitative imaging techniques are a main topic of ongoing research in Magnetic Res-onance Imaging (MRI). Current challenges involve the speed up of acquisition as wellas maintaining good accuracy. The present work describes a new accelerated T1 map-ping method on the basis of model-based reconstruction for Variable Flip Angle (VFA)data. The reconstruction problem is solved with an Iterative Regularized Gauss-Newton(IRGN)-Total-Generalized-Variation (TGV) algorithm. Reconstructed parameter mapsfor numerical, phantom, and in vivo knee data were in reasonable agreement with ref-erence values up to a 12 fold acceleration. In order to minimize systematic errors it iscrucial to have knowledge of the exact flip angle distribution. The blurring at sharpNMR-parameter changes provides an area for future improvements. In this context theinfluence of the regularization functional could be subject of further investigations.

M3 - Master's Thesis

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