Rapid T1 quantification from high resolution 3D data with model-based reconstruction.
Magn Reson Med
; 81(3): 2072-2089, 2019 03.
Article
em En
| MEDLINE
| ID: mdl-30346053
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 ANDMETHODS:
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.Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Processamento de Imagem Assistida por Computador
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Encéfalo
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Imageamento por Ressonância Magnética
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Imageamento Tridimensional
Tipo de estudo:
Guideline
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Prognostic_studies
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Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Magn Reson Med
Assunto da revista:
DIAGNOSTICO POR IMAGEM
Ano de publicação:
2019
Tipo de documento:
Article
País de afiliação:
Áustria