Rapid T1 quantification from high resolution 3D data with model‐based reconstruction

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.

[1]  M. Maier.  Quantitative MRI of the brain—measuring changes caused by disease , 2004 .

[2]  Jens Frahm,et al.  Model‐based reconstruction for T1 mapping using single‐shot inversion‐recovery radial FLASH , 2016, Int. J. Imaging Syst. Technol..

[3]  Antonin Chambolle,et al.  A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging , 2011, Journal of Mathematical Imaging and Vision.

[4]  B K Rutt,et al.  A fast 3D look-locker method for volumetric T1 mapping. , 1999, Magnetic resonance imaging.

[5]  P. Boesiger,et al.  SENSE: Sensitivity encoding for fast MRI , 1999, Magnetic resonance in medicine.

[6]  F. Wiesinger,et al.  B1 mapping by Bloch‐Siegert shift , 2010, Magnetic resonance in medicine.

[7]  Peter Börnert,et al.  DREAM—a novel approach for robust, ultrafast, multislice B1 mapping , 2012, Magnetic resonance in medicine.

[8]  Daniel K. Sodickson,et al.  Improving the Robustness of Clinical T 1-Weighted MRI Using Radial VIBE , 2013 .

[9]  Richard Kijowski,et al.  Fast Realistic MRI Simulations Based on Generalized Multi-Pool Exchange Tissue Model , 2016, IEEE Transactions on Medical Imaging.

[10]  Daniel K Sodickson,et al.  gpuNUFFT - An Open Source GPU Library for 3D Regridding with Direct Matlab Interface , 2014 .

[11]  B. Hofmann,et al.  Convergence rates for the iteratively regularized Gauss–Newton method in Banach spaces , 2010 .

[12]  Thomas Pock,et al.  A First-Order Primal-Dual Algorithm with Linesearch , 2016, SIAM J. Optim..

[13]  Zhi-Pei Liang,et al.  Accelerated MR parameter mapping with low‐rank and sparsity constraints , 2015, Magnetic resonance in medicine.

[14]  L R Schad,et al.  Fast and precise T1 imaging using a TOMROP sequence. , 1990, Magnetic resonance imaging.

[15]  Kristian Bredies,et al.  A TGV-Based Framework for Variational Image Decompression, Zooming, and Reconstruction. Part I: Analytics , 2015, SIAM J. Imaging Sci..

[16]  A. Alexander,et al.  Accelerating MR parameter mapping using sparsity‐promoting regularization in parametric dimension , 2013, Magnetic resonance in medicine.

[17]  Saverio Salzo,et al.  Convergence analysis of a proximal Gauss-Newton method , 2012, Comput. Optim. Appl..

[18]  J. Pauly,et al.  Accelerating parameter mapping with a locally low rank constraint , 2015, Magnetic resonance in medicine.

[19]  Christine L. Tardif,et al.  On the accuracy of T1 mapping: Searching for common ground , 2015, Magnetic resonance in medicine.

[20]  Mariya Doneva,et al.  Compressed sensing reconstruction for magnetic resonance parameter mapping , 2010, Magnetic resonance in medicine.

[21]  Johannes Tran-Gia,et al.  Model-Based Acceleration of Look-Locker T1 Mapping , 2015, PloS one.

[22]  Bhaskar D. Rao,et al.  Sparse Bayesian learning for basis selection , 2004, IEEE Transactions on Signal Processing.

[23]  Kristian Bredies,et al.  Ultrafast 3D Bloch–Siegert B1+‐mapping using variational modeling , 2018, Magnetic Resonance in Medicine.

[24]  T. Hohage,et al.  Image reconstruction by regularized nonlinear inversion—Joint estimation of coil sensitivities and image content , 2008, Magnetic resonance in medicine.

[25]  Ali Bilgin,et al.  T2 mapping from highly undersampled data by reconstruction of principal component coefficient maps using compressed sensing , 2012, Magnetic resonance in medicine.

[26]  B. Rutt,et al.  Rapid combined T1 and T2 mapping using gradient recalled acquisition in the steady state , 2003, Magnetic resonance in medicine.

[27]  Zhi-Pei Liang,et al.  Model-based MR parameter mapping with sparsity constraint , 2013, 2013 IEEE 10th International Symposium on Biomedical Imaging.

[28]  Jens Frahm,et al.  Model-Based Iterative Reconstruction for Radial Fast Spin-Echo MRI , 2009, IEEE Transactions on Medical Imaging.

[29]  T. Pock,et al.  Second order total generalized variation (TGV) for MRI , 2011, Magnetic resonance in medicine.

[30]  K. T. Block,et al.  Undersampled radial MRI with multiple coils. Iterative image reconstruction using a total variation constraint , 2007, Magnetic resonance in medicine.

[31]  Thomas Pock,et al.  Learning a variational network for reconstruction of accelerated MRI data , 2017, Magnetic resonance in medicine.

[32]  Thomas Pock,et al.  Improved Accelerated Model-based Parameter Quantification with Total-Generalized- Variation Regularization. , 2017 .

[33]  Karl Kunisch,et al.  Total Generalized Variation , 2010, SIAM J. Imaging Sci..

[34]  N J Pelc,et al.  Rapid calculation of T1 using variable flip angle gradient refocused imaging. , 1987, Magnetic resonance imaging.

[35]  Yong Chen,et al.  Multiscale reconstruction for MR fingerprinting , 2016, Magnetic resonance in medicine.

[36]  Kristian Bredies,et al.  A TGV-Based Framework for Variational Image Decompression, Zooming, and Reconstruction. Part I: Analytics , 2015, SIAM J. Imaging Sci..

[37]  Josef Pfeuffer,et al.  AIR-MRF: Accelerated iterative reconstruction for magnetic resonance fingerprinting. , 2017, Magnetic resonance imaging.

[38]  K. T. Block,et al.  Simple Method for Adaptive Gradient-Delay Compensation in Radial MRI , 2010 .

[39]  Olaf Dössel,et al.  An Optimal Radial Profile Order Based on the Golden Ratio for Time-Resolved MRI , 2007, IEEE Transactions on Medical Imaging.

[40]  Jens Frahm,et al.  Fast T2 Mapping With Improved Accuracy Using Undersampled Spin-Echo MRI and Model-Based Reconstructions With a Generating Function , 2014, IEEE Transactions on Medical Imaging.

[41]  D. Hahn,et al.  Model‐based Acceleration of Parameter mapping (MAP) for saturation prepared radially acquired data , 2013, Magnetic resonance in medicine.

[42]  D. Look,et al.  Time Saving in Measurement of NMR and EPR Relaxation Times , 1970 .

[43]  Jens Frahm,et al.  Model‐based T1 mapping with sparsity constraints using single‐shot inversion‐recovery radial FLASH , 2018, Magnetic resonance in medicine.

[44]  R. Otazo,et al.  Accelerating 3D‐T1ρ mapping of cartilage using compressed sensing with different sparse and low rank models , 2018, Magnetic resonance in medicine.

[45]  William W. Hager,et al.  A New Conjugate Gradient Method with Guaranteed Descent and an Efficient Line Search , 2005, SIAM J. Optim..

[46]  Marc Teboulle,et al.  A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems , 2009, SIAM J. Imaging Sci..

[47]  Robin M Heidemann,et al.  Generalized autocalibrating partially parallel acquisitions (GRAPPA) , 2002, Magnetic resonance in medicine.

[48]  K. Bredies,et al.  Parallel imaging with nonlinear reconstruction using variational penalties , 2012, Magnetic resonance in medicine.

[49]  Ya-Xiang Yuan,et al.  A Nonlinear Conjugate Gradient Method with a Strong Global Convergence Property , 1999, SIAM J. Optim..