Rapid T 1 quantification from high resolution 3D data with model-based reconstruction

Oliver Maier, Jasper Schoormans, Matthias Schloegl, Gustav J Strijkers, Andreas Lesch, Thomas Benkert, Tobias Block, Bram F Coolen, Kristian Bredies, Rudolf Stollberger

Research output: Contribution to journalArticleResearchpeer-review

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.

Original languageEnglish
Pages (from-to)2072-2089
Number of pages18
JournalMagnetic resonance in medicine
Volume81
Issue number3
DOIs
Publication statusPublished - 2019

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Magnetic Resonance Imaging

Keywords

  • constrained reconstruction
  • imaging
  • inversion-recovery Look-Locker
  • model-based reconstruction
  • MRI
  • T1 quantification
  • variable flip angle

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

Cite this

Rapid T 1 quantification from high resolution 3D data with model-based reconstruction. / Maier, Oliver; Schoormans, Jasper; Schloegl, Matthias; Strijkers, Gustav J; Lesch, Andreas; Benkert, Thomas; Block, Tobias; Coolen, Bram F; Bredies, Kristian; Stollberger, Rudolf.

In: Magnetic resonance in medicine, Vol. 81, No. 3, 2019, p. 2072-2089.

Research output: Contribution to journalArticleResearchpeer-review

Maier, Oliver ; Schoormans, Jasper ; Schloegl, Matthias ; Strijkers, Gustav J ; Lesch, Andreas ; Benkert, Thomas ; Block, Tobias ; Coolen, Bram F ; Bredies, Kristian ; Stollberger, Rudolf. / Rapid T 1 quantification from high resolution 3D data with model-based reconstruction. In: Magnetic resonance in medicine. 2019 ; Vol. 81, No. 3. pp. 2072-2089.
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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.",
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author = "Oliver Maier and Jasper Schoormans and Matthias Schloegl and Strijkers, {Gustav J} and Andreas Lesch and Thomas Benkert and Tobias Block and Coolen, {Bram F} and Kristian Bredies and Rudolf Stollberger",
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AU - Schoormans, Jasper

AU - Schloegl, Matthias

AU - Strijkers, Gustav J

AU - Lesch, Andreas

AU - Benkert, Thomas

AU - Block, Tobias

AU - Coolen, Bram F

AU - Bredies, Kristian

AU - Stollberger, Rudolf

N1 - © 2018 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine.

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

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