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Rapid T 1 quantification from high resolution 3D data with model‐based reconstruction
Author(s) -
Maier Oliver,
Schoormans Jasper,
Schloegl Matthias,
Strijkers Gustav J.,
Lesch Andreas,
Benkert Thomas,
Block Tobias,
Coolen Bram F.,
Bredies Kristian,
Stollberger Rudolf
Publication year - 2019
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.27502
Subject(s) - undersampling , algorithm , computer science , imaging phantom , regularization (linguistics) , parametric statistics , image resolution , iterative reconstruction , data consistency , mathematics , artificial intelligence , physics , optics , statistics , operating system
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 T 1 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. T 1 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 T 1 maps with an isotropic resolution of 1 mm 3 from highly undersampled radial data by exploiting structural similarities in the imaging volume and across parameter maps.