Potential of compressed sensing in quantitative MR imaging of cancer

@article{Smith2013PotentialOC,
  title={Potential of compressed sensing in quantitative MR imaging of cancer},
  author={David S. Smith and Xia Li and Richard G. Abramson and C. Chad Quarles and Thomas E. Yankeelov and E. Brian Welch},
  journal={Cancer Imaging},
  year={2013},
  volume={13},
  pages={633 - 644}
}
Abstract Classic signal processing theory dictates that, in order to faithfully reconstruct a band-limited signal (e.g., an image), the sampling rate must be at least twice the maximum frequency contained within the signal, i.e., the Nyquist frequency. Recent developments in applied mathematics, however, have shown that it is often possible to reconstruct signals sampled below the Nyquist rate. This new method of compressed sensing (CS) requires that the signal have a concise and extremely… 

Figures from this paper

Quantitative Evaluation of Temporal Regularizers in Compressed Sensing Dynamic Contrast Enhanced MRI of the Breast
TLDR
TV/TGVα2 should be used as temporal constraints for CS DCE-MRI of the breast using common temporal sparsity-promoting regularizers on 4.5x retrospectively undersampled Cartesian in vivo breast data.
Compressed sensing in spectroscopy for chemical analysis
TLDR
The main idea is to perform compression during data acquisition which leads to several advantages, including faster analysis time and availability of cost effective alternatives to array detectors, in this short review.
Feasibility of accelerated 3D T1-weighted MRI using compressed sensing: application to quantitative volume measurements of human brain structures
TLDR
Subcortical volumes obtained from the CS-4 images are consistent among different post-processing packages, and the maximum biases of SENSE-4 andCS-4 were found in the Thalamus with the mean of differences of 1.60 ml and 0.18 ml.
Three-dimensional MR Cholangiopancreatography in a Breath Hold with Sparsity-based Reconstruction of Highly Undersampled Data.
TLDR
BH SPARSE-SPACE showed similar or superior image quality for the pancreatic and common duct compared with that of RT SPACE despite 17-fold shorter acquisition time.
Two-dimensional XD-GRASP provides better image quality than conventional 2D cardiac cine MRI for patients who cannot suspend respiration
TLDR
Although FB XD-GRASP CCMRI was visually inferior to conventional BH CCMRI in general, it provided improved image quality in the subgroup of patients with respiratory-motion-induced artifacts on BH images.
Reproducibility of radiomic features in SENSE and compressed SENSE: impact of acceleration factors
TLDR
Reproducibility of radiomic features in reference to the original image was lower with higher acceleration factors in both sensitivity encoding (SENSE) and compressed SENSE (CS) across all anatomical locations (p < .001); run percentage of GLRLM with wavelet D was identified as the most reproducible feature.
Acceleration of skeletal age MR examination using compressed sensing
To examine the feasibility of accelerating magnetic resonance (MR) image acquisition for children using compressed sensing (CS). Skeletal age assessment using MRI sometimes suffers from motion
...
...

References

SHOWING 1-10 OF 65 REFERENCES
Compressed sensing in dynamic MRI
TLDR
Given sufficient data sparsity and base signal‐to‐noise ratio (SNR), CS is demonstrated to result in improved temporal fidelity compared to k‐t BLAST reconstructions for the example data sets used in this work.
Spatially regularized compressed sensing of diffusion MRI data
TLDR
A novel way to combine the diffusion- and spatial-domain constraints to achieve a maximal reduction in the number of diffusion measurements, while sacrificing little in terms of reconstruction accuracy is described.
Quantitative effects of using compressed sensing in dynamic contrast enhanced MRI.
TLDR
The accuracy of extracted pharmacokinetic parameters from DCE-MRI data obtained as part of pre-clinical and clinical studies in which fully sampled acquisitions have been retrospectively undersampled by factors of 2, 3 and 4 in Fourier space and then reconstructed with CS are demonstrated.
Sparse MRI: The application of compressed sensing for rapid MR imaging
TLDR
Practical incoherent undersampling schemes are developed and analyzed by means of their aliasing interference and demonstrate improved spatial resolution and accelerated acquisition for multislice fast spin‐echo brain imaging and 3D contrast enhanced angiography.
Compressed sensing reconstruction for magnetic resonance parameter mapping
TLDR
This work presents a CS reconstruction for magnetic resonance (MR) parameter mapping, which applies an overcomplete dictionary, learned from the data model to sparsify the signal.
A Fast Compressed Sensing Approach to 3D MR Image Reconstruction
TLDR
The main contribution of this paper is to combine both spatio-temporal correlations and 3D filtering strategies in a compressed sensing framework by exploiting the gradient sparsity of the image volume to solve the constrained 3D minimization problem.
Multi‐contrast reconstruction with Bayesian compressed sensing
TLDR
This work presents a reconstruction algorithm based on Bayesian compressed sensing to jointly reconstruct a set of images from undersampled k‐space data with higher fidelity than when the images are reconstructed either individually or jointly by a previously proposed algorithm, M‐FOCUSS.
Compressed sensing in hyperpolarized 3He Lung MRI
TLDR
In this work, the application of compressed sensing techniques to the acquisition and reconstruction of hyperpolarized 3He lung MR images was investigated and the feasibility of producing accurate functional apparent diffusion coefficient (ADC) maps from undersampled data acquired with fewer radiofrequency pulses was demonstrated.
The influence of radial undersampling schemes on compressed sensing reconstruction in breast MRI
TLDR
Results from point spread function studies, simulations, phantom and in vivo experiments show that the choice of radial sampling pattern influences the quality of the final image reconstructed by the compressed sensing algorithm, and golden‐angle radial sampling results in the least overall error when various temporal resolutions are considered.
Robustness of Quantitative Compressive Sensing MRI: The Effect of Random Undersampling Patterns on Derived Parameters for DCE- and DSC-MRI
TLDR
Across 11000 different CS reconstructions, the authors saw no outliers in the distribution of parameters, suggesting that, despite the random undersampling schemes, CS accelerated quantitative MRI may have a predictable level of performance.
...
...