Compressed sensing for body MRI

  title={Compressed sensing for body MRI},
  author={Li Feng and Thomas Benkert and Kai Tobias Block and Daniel K. Sodickson and Ricardo Otazo and Hersh Chandarana},
  journal={Journal of Magnetic Resonance Imaging},
The introduction of compressed sensing for increasing imaging speed in magnetic resonance imaging (MRI) has raised significant interest among researchers and clinicians, and has initiated a large body of research across multiple clinical applications over the last decade. Compressed sensing aims to reconstruct unaliased images from fewer measurements than are traditionally required in MRI by exploiting image compressibility or sparsity. Moreover, appropriate combinations of compressed sensing… 
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  • D. Tamada
  • Medicine
    Japanese Journal of Magnetic Resonance in Medicine
  • 2018
An overview of the implementation of compressed sensing on clinical scanners, including trajectories, reconstruction schemes, and algorithms, and Clinically available applications that use compressed sensing techniques in clinical scanners are introduced.
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The current state of the art of CS methods for rapid compositional mapping of knee cartilage, including data acquisition strategies, image reconstruction algorithms, and data fitting models are discussed.
Compressed sensing MRI with variable density averaging (CS-VDA) outperforms full sampling at low SNR
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Evaluation of compressed sensing MRI for accelerated bowel motility imaging
Compared to SENSE, current implementation of CS-PI performed less or equally good in terms of image quality and diagnostic quality and did not show advantages over SENSE for three-dimensional bowel motility imaging.
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Compressed sensing MRI: a review of the clinical literature.
Compressed sensing (CS) is a method for accelerating MRI acquisition by acquiring less data through undersampling of k-space, which has the potential to mitigate the time-intensiveness of MRI.
Compressed sensing in dynamic MRI
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.
Distributed Compressed Sensing for Accelerated MRI
This work presents a greedy reconstruction algorithm that takes advantage of both intraand inter-coil correlations, and assess its performance with respect to the number of coils.
Accelerating SENSE using compressed sensing
A novel method to combine sensitivity encoding (SENSE), one of the standard methods for parallel MRI, and compressed sensing for rapid MR imaging (SparseMRI), a recently proposed method for applying CS in MR imaging with Cartesian trajectories is proposed.
Sparsesense: Application of compressed sensing in parallel MRI
  • Bo Liu, Y. Zou, L. Ying
  • Computer Science
    2008 International Conference on Information Technology and Applications in Biomedicine
  • 2008
The sampling scheme for incoherence is discussed and reconstruction method using Begman iteration is proposed, and the combined method, named SparseSENSE, can achieve a reduction factor higher than the number of channels.
Practical parallel imaging compressed sensing MRI: Summary of two years of experience in accelerating body MRI of pediatric patients
Clinical results showing higher quality reconstruction and better diagnostic confidence than parallel imaging alone at accelerations on the order of number of coils and an on-line parallelized implementation of ℓ1-SPIRiT on multi-core CPU and General Purpose Graphics Processors that achieves sub-minute 3D reconstructions with 8-channels.
Sparse MRI: The application of compressed sensing for rapid MR imaging
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.
Motion corrected compressed sensing for free‐breathing dynamic cardiac MRI
This work proposes a novel motion corrected CS framework for free‐breathing dynamic cardiac MRI that incorporates a general motion correction formulation directly into the CS reconstruction, and shows that this approach allows for the reconstruction of respiratory motion corrected cardiac CINE images with similar quality to breath‐held acquisitions.
SENSE: Sensitivity encoding for fast MRI
The problem of image reconstruction from sensitivity encoded data is formulated in a general fashion and solved for arbitrary coil configurations and k‐space sampling patterns and special attention is given to the currently most practical case, namely, sampling a common Cartesian grid with reduced density.