Journal ArticleDOI
Sparse representation-based MRI super-resolution reconstruction
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TLDR
A novel dictionary training method for sparse reconstruction for enhancing the similarity of sparse representations between the low resolution and high resolution MRI block pairs through simultaneous training two dictionaries.About:
This article is published in Measurement.The article was published on 2014-01-01. It has received 73 citations till now. The article focuses on the topics: Real-time MRI & Sparse approximation.read more
Citations
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Journal ArticleDOI
Image super-resolution
TL;DR: This paper aims to provide a review of SR from the perspective of techniques and applications, and especially the main contributions in recent years, and discusses the current obstacles for future research.
Journal ArticleDOI
Bioelectrical Impedance Methods for Noninvasive Health Monitoring: A Review.
TL;DR: The working principles, applications, merits, and demerits of these methods has been discussed in detail along with their other technical issues followed by present status and future trends.
Journal ArticleDOI
Super-resolution musculoskeletal MRI using deep learning.
Akshay S. Chaudhari,Zhongnan Fang,Feliks Kogan,Jeffrey P. Wood,Kathryn J. Stevens,Eric K. Gibbons,Jin Hyung Lee,Garry E. Gold,Brian A. Hargreaves +8 more
TL;DR: To develop a super‐resolution technique using convolutional neural networks for generating thin‐slice knee MR images from thicker input slices, and compare this method with alternative through‐plane interpolation methods.
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Image reconstruction of compressed sensing MRI using graph-based redundant wavelet transform.
TL;DR: A graph-based redundant wavelet transform is introduced to sparsely represent magnetic resonance images in iterative image reconstructions and outperforms several state-of-the-art reconstruction methods in removing artifacts and achieves fewer reconstruction errors on the tested datasets.
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Simultaneous single- and multi-contrast super-resolution for brain MRI images based on a convolutional neural network.
TL;DR: Experimental results show that the proposed deep convolutional neural network model outperforms state-of-the-art MRI super-resolution methods in terms of visual quality and objective quality criteria such as peak signal-to-noise ratio and structural similarity.
References
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Feature-based Face Detection Against Skin-color Like Backgrounds with Varying Illumination
TL;DR: Experimental results show that the proposed three-stage scheme for real-time reliable face detection has good performance in the face detection of faces in various poses, faces in skin color-like backgrounds, faces under varying illumination, and faces of various races.
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Decoding visual brain states from fMRI using an ensemble of classifiers
TL;DR: The results indicate that an ensemble of classifiers provides an advantageous alternative to commonly used single classifiers, particularly when decoding stimuli associated with specific brain areas, when decoding fMRI data.
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A Sparse Representation Method for Magnetic Resonance Spectroscopy Quantification
TL;DR: Simulation results show good performance of this wavelet filtering-based strategy in separating the overlapping components between the baselines and the spectra of interest, when no appropriate model function for the baseline is available.
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Bayesian hypothesis testing for pattern discrimination in brain decoding
TL;DR: This paper presents an application on nine brain decoding investigations from a real functional magnetic resonance imaging (fMRI) experiment about the relation between mental calculation and eye movements, based on a Beta-Binomial model for the population of generalization errors of classifiers from multi-subject studies within the Bayesian hypothesis testing framework.