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Abstract
PURPOSE: Magnetic resonance imaging protocols for the assessment of quantitative information suffer from long acquisition times since multiple measurements in a parametric dimension are required. To facilitate the clinical applicability, accelerating the acquisition is of high importance. To this end, we propose a model-based optimization framework in conjunction with undersampling 3D radial stack-of-stars data.
THEORY AND METHODS: High resolution 3D T1 maps are generated from subsampled data by employing model-based reconstruction combined with a regularization functional, coupling information from the spatial and parametric dimension, to exploit redundancies in the acquired parameter encodings and across parameter maps. To cope with the resulting non-linear, non-differentiable optimization problem, we propose a solution strategy based on the iteratively regularized Gauss-Newton method. The importance of 3D-spectral regularization is demonstrated by a comparison to 2D-spectral regularized results. The algorithm is validated for the variable flip angle (VFA) and inversion recovery Look-Locker (IRLL) method on numerical simulated data, MRI phantoms, and in vivo data.
RESULTS: Evaluation of the proposed method using numerical simulations and phantom scans shows excellent quantitative agreement and image quality. T1 maps from accelerated 3D in vivo measurements, e.g. 1.8 s/slice with the VFA method, are in high accordance with fully sampled reference reconstructions.
CONCLUSIONS: The proposed algorithm is able to recover T1 maps with an isotropic resolution of 1 mm3 from highly undersampled radial data by exploiting structural similarities in the imaging volume and across parameter maps.
Originalsprache | englisch |
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Seiten (von - bis) | 2072-2089 |
Seitenumfang | 18 |
Fachzeitschrift | Magnetic Resonance in Medicine |
Jahrgang | 81 |
Ausgabenummer | 3 |
DOIs | |
Publikationsstatus | Veröffentlicht - 2019 |
ASJC Scopus subject areas
- !!Radiology Nuclear Medicine and imaging
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Projekte
- 1 Laufend
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qMRI - Variationsmethoden zur schnellen Multiparameter-Quantifizierung in der MRT
1/07/18 → 30/06/21
Projekt: Foschungsprojekt