Low rank alternating direction method of multipliers reconstruction for MR fingerprinting

  title={Low rank alternating direction method of multipliers reconstruction for MR fingerprinting},
  author={Jakob Assl{\"a}nder and Martijn A. Cloos and Florian Knoll and Daniel K. Sodickson and J{\"u}rgen Hennig and Riccardo Lattanzi},
  journal={Magnetic Resonance in Medicine},
The proposed reconstruction framework addresses the reconstruction accuracy, noise propagation and computation time for magnetic resonance fingerprinting. 
Sparsity and locally low rank regularization for MR fingerprinting
Develop a sparse and locally low rank (LLR) regularized reconstruction to accelerate MR fingerprinting (MRF) and develop a sparse-and-locally low rank regularization model for MRF reconstruction.
Fast multi‐component analysis using a joint sparsity constraint for MR fingerprinting
To develop an efficient algorithm for multi‐component analysis of magnetic resonance fingerprinting (MRF) data without making a priori assumptions about the exact number of tissues or their
Generalized low‐rank nonrigid motion‐corrected reconstruction for MR fingerprinting
A novel low‐rank motion‐corrected (LRMC) reconstruction for nonrigid motion‐Corrected MR fingerprinting (MRF) and a novel low-rank motion-corrected MRMC reconstruction for Nonrigid Motion Correction (RMC) for MRF.
Optimized multi‐axis spiral projection MR fingerprinting with subspace reconstruction for rapid whole‐brain high‐isotropic‐resolution quantitative imaging
The proposed TGAS-SPI-MRF with optimized spiral-projection trajectory and subspace reconstruction can enable high-resolution quantitative mapping with faster acquisition speed and improve image quality by mitigating blurring caused by off-resonance effect.
Cartesian MR fingerprinting in the eye at 7T using compressed sensing and matrix completion‐based reconstructions
To explore the feasibility of MR Fingerprinting (MRF) to rapidly quantify relaxation times in the human eye at 7T, and to provide a data acquisition and processing framework for future tissue
Fast 3D magnetic resonance fingerprinting for a whole‐brain coverage
The purpose of this study was to accelerate the acquisition and reconstruction time of 3D magnetic resonance fingerprinting scans.
Magnetic resonance fingerprinting review part 2: Technique and directions
This work highlights some of the recent technical developments in MRF, focusing on sequence optimization, modifications for reconstruction and pattern matching, new methods for partial volume analysis, and applications of machine and deep learning.
Flow MR fingerprinting
To investigate the feasibility to quantify blood velocities within the magnetic resonance fingerprinting framework, while providing relaxometric maps of static tissue.
Submillimeter MR fingerprinting using deep learning–based tissue quantification
To develop a rapid 2D MR fingerprinting technique with a submillimeter in‐plane resolution using a deep learning–based tissue quantification approach.
Rigid motion‐corrected magnetic resonance fingerprinting
Develop a method for rigid body motion‐corrected magnetic resonance fingerprinting (MRF) and describe its applications in medicine, dentistry and dentistry.


Fast group matching for MR fingerprinting reconstruction
A large dictionary of Bloch simulations is compared against rapidly acquired data to estimate tissue properties such as T1, T2, proton density, and B0, and this matching process can be a very computationally demanding portion of MRF reconstruction.
Maximum likelihood reconstruction for magnetic resonance fingerprinting
A maximum likelihood formulation is presented to simultaneously estimate multiple parameter maps from highly undersampled, noisy k-space data and shows that compared to the conventional MRF reconstruction, the proposed method yields improved accuracy and/or reduced acquisition time.
AIR-MRF: Accelerated iterative reconstruction for magnetic resonance fingerprinting.
Model-based iterative reconstruction for magnetic resonance fingerprinting
  • Bo Zhao
  • Computer Science
    2015 IEEE International Conference on Image Processing (ICIP)
  • 2015
A new model-based reconstruction method is presented to enable improved parameter estimation from highly under-sampled, noisy k-space data and to develop an efficient iterative algorithm based on variable splitting, the alternating direction method of multipliers, and the variable projection method.
Accelerated MR parameter mapping with low‐rank and sparsity constraints
To enable accurate magnetic resonance (MR) parameter mapping with accelerated data acquisition, utilizing recent advances in constrained imaging with sparse sampling.
Music‐based magnetic resonance fingerprinting to improve patient comfort during MRI examinations
This work proposes a technique for mitigating the noise problem by producing musical sounds directly from the switching magnetic fields while simultaneously quantifying multiple important tissue properties.
A Compressed Sensing Framework for Magnetic Resonance Fingerprinting
It is shown that, theoretically, as long as the excitation sequence possesses an appropriate form of persistent excitation, it is able to accurately recover the proton density, T1, T2, and off-resonance maps simultaneously from a limited number of samples.
SVD Compression for Magnetic Resonance Fingerprinting in the Time Domain
By compressing the size of the dictionary in the time domain, this work is able to speed up the pattern recognition algorithm, by a factor of between 3.4-4.8, without sacrificing the high signal-to-noise ratio of the original scheme presented previously.
Low rank magnetic resonance fingerprinting
A new approach for quantitative MRI using MRF is introduced, called Low Rank MRF, which exploits the low rank property of the temporal domain, on top of the well-known sparsity of the MRF signal in the generated dictionary domain.