# Sparse reconstruction of compressive sensing MRI using cross-domain stochastically fully connected conditional random fields

@article{Li2016SparseRO,
title={Sparse reconstruction of compressive sensing MRI using cross-domain stochastically fully connected conditional random fields},
author={Edward Li and Farzad Khalvati and Mohammad Javad Shafiee and Masoom A. Haider and Alexander Wong},
journal={BMC Medical Imaging},
year={2016},
volume={16}
}
• E. Li, +2 authors A. Wong
• Published 25 December 2015
• Computer Science, Medicine, Physics, Mathematics
• BMC Medical Imaging
BackgroundMagnetic Resonance Imaging (MRI) is a crucial medical imaging technology for the screening and diagnosis of frequently occurring cancers. However, image quality may suffer from long acquisition times for MRIs due to patient motion, which also leads to patient discomfort. Reducing MRI acquisition times can reduce patient discomfort leading to reduced motion artifacts from the acquisition process. Compressive sensing strategies applied to MRI have been demonstrated to be effective in…
3 Citations
Sparse Reconstruction of Compressive Sensing Magnetic Resonance Imagery using a Cross Domain Stochastic Fully Connected Conditional Random Field Framework
This work presents a comprehensive framework for a cross-domain stochastically fully connected conditional random field (CD-SFCRF) reconstruction approach to facilitate compressive sensing MRI and shows noticeable improvements over state of the art methods.
Joint sparse reconstruction of multi-contrast MRI images with graph based redundant wavelet transform
The proposed joint reconstruction method can achieve lower reconstruction errors and better preserve image structures than the compared joint reconstruction methods, and outperforms single image reconstruction with joint sparsity constraint of multi-contrast images.
Toeplitz-Structured Xampling System for Multipulse Signal
• Computer Science
Circuits Syst. Signal Process.
• 2020
This paper proposes the Toeplitz-structured Xampling system for the multipulse signal to achieve the simplified system construction and improved system performance and shows low complexity, improved sampling efficiency and good reconstruction effect.

## References

SHOWING 1-10 OF 48 REFERENCES
Sparse MRI: The application of compressed sensing for rapid MR imaging
• Mathematics, Medicine
Magnetic resonance in medicine
• 2007
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 MRI using Singular Value Decomposition based sparsity basis
• Computer Science, Medicine
2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
• 2011
Singular Value Decomposition is proposed as a data-adaptive sparsity basis for compressed sensing MRI that can potentially sparsify a broader range of MRI images and improve the image quality, thus providing a simple and effective solution for the application of compressed sensing in MRI.
Sparse Reconstruction of Breast MRI Using Homotopic $L_0$ Minimization in a Regional Sparsified Domain
• Mathematics, Computer Science
IEEE Transactions on Biomedical Engineering
• 2013
Experimental results show that good breast MRI reconstruction accuracy can be achieved compared to existing methods, and a regional differential sparsifying transform is investigated for use within a homotopic L0 minimization framework for reconstructing breast MRI.
Accelerating SENSE using compressed sensing
• Mathematics, Medicine
Magnetic resonance in medicine
• 2009
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.
Highly Undersampled Magnetic Resonance Image Reconstruction via Homotopic $\ell_{0}$ -Minimization
• Mathematics, Computer Science
IEEE Transactions on Medical Imaging
• 2009
A generalization of the CS paradigm based on homotopic approximation of the lscr0 quasi-norm is proposed and how MR image reconstruction can be pushed even further below the Nyquist limit and significantly closer to the theoretical bound is shown.
Dual-stage correlated diffusion imaging
• Medicine, Computer Science
2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)
• 2015
Quantitative evaluation using sensitivity, specificity, and accuracy measures, and visual assessment by an expert radiologist for datasets of 13 patient cases with confirmed prostate cancer suggest that D-CDI not only provides strong delineation between healthy and cancerous tissues, it allows for accurate cancer localization in the prostate gland without the need for additional MRI modalities to be studied.
Undersampled radial MRI with multiple coils. Iterative image reconstruction using a total variation constraint
• Mathematics, Medicine
Magnetic resonance in medicine
• 2007
An iterative reconstruction method for undersampled radial MRI which is based on a nonlinear optimization, allows for the incorporation of prior knowledge with use of penalty functions, and deals with data from multiple coils is developed.
Compressed sensing MRI with combined sparsifying transforms and smoothed l0 norm minimization
• Mathematics, Computer Science
2010 IEEE International Conference on Acoustics, Speech and Signal Processing
• 2010
Simulation results demonstrate that the proposed method can improve image quality when comparing to single sparsifying transform, and is implemented via the state-of-art smoothed l0 norm in overcomplete sparse decomposition.
Enhanced dual-stage correlated diffusion imaging
• Medicine, Computer Science
2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
• 2016
The enhanced D-CDI (eD- CDI) is evaluated using area under the ROC curve for datasets of 17 patient cases with confirmed prostate cancer and the results show that eD-CDi outperforms the original D-cdI as well as T2 weighted images and diffusion-weighed images used in the form of apparent diffusion coefficient maps.
Projection reconstruction MR imaging using FOCUSS
• Mathematics, Medicine
Magnetic resonance in medicine
• 2007
FOCUSS is effective for projection reconstruction MRI, since medical images are usually sparse in some sense and the center region of the undersampled radial k‐space samples still provides a low resolution, yet meaningful, image essential for the convergence of FOCUSS.