Journal ArticleDOI
Magnetic resonance image reconstruction using trained geometric directions in 2D redundant wavelets domain and non-convex optimization.
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TLDR
Numerical simulation results on vivo magnetic resonance images demonstrate that the proposed PBDW-based MRI reconstruction is improved from two aspects: an efficient non-convex minimization algorithm is modified to enhance image quality and P BDW are extended into shift-invariant discrete wavelet domain to enhance the ability of transform on sparsifying piecewise smooth image features.About:
This article is published in Magnetic Resonance Imaging.The article was published on 2013-11-01. It has received 77 citations till now. The article focuses on the topics: Image quality & Wavelet.read more
Citations
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Journal ArticleDOI
Magnetic resonance image reconstruction from undersampled measurements using a patch-based nonlocal operator
TL;DR: This paper designs a patch-based nonlocal operator (PANO) to sparsify magnetic resonance images by making use of the similarity of image patches to achieve lower reconstruction error and higher visual quality than conventional CS-MRI methods.
Journal ArticleDOI
Fast Multiclass Dictionaries Learning With Geometrical Directions in MRI Reconstruction
TL;DR: The proposed method can be exploited in undersampled magnetic resonance imaging to reduce data acquisition time and reconstruct images with better image quality and the computation of the proposed approach is much faster than the typical K-SVD dictionary learning method in magnetic resonance image reconstruction.
Journal ArticleDOI
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.
Journal ArticleDOI
Image Reconstruction: From Sparsity to Data-Adaptive Methods and Machine Learning
TL;DR: The field of medical image reconstruction has seen roughly four types of methods: analytical methods, such as filtered backprojection (FBP) for X-ray computed tomography (CT) and the inverse Fourier transform for magnetic resonance imaging (MRI), based on simple mathematical models for the imaging systems.
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Efficient Blind Compressed Sensing Using Sparsifying Transforms with Convergence Guarantees and Application to Magnetic Resonance Imaging
TL;DR: This work focuses on blind compressed sensing, and proposes a framework to simultaneously reconstruct the underlying image as well as the sparsifying transform from highly undersampled measurements, and proves that the proposed block coordinate descent-type algorithms involve highly efficient optimal updates.
References
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Journal ArticleDOI
Image quality assessment: from error visibility to structural similarity
TL;DR: In this article, a structural similarity index is proposed for image quality assessment based on the degradation of structural information, which can be applied to both subjective ratings and objective methods on a database of images compressed with JPEG and JPEG2000.
Book
A wavelet tour of signal processing
TL;DR: An introduction to a Transient World and an Approximation Tour of Wavelet Packet and Local Cosine Bases.
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Sparse MRI: The application of compressed sensing for rapid MR imaging.
TL;DR: 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.
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Enhancing Sparsity by Reweighted ℓ 1 Minimization
TL;DR: A novel method for sparse signal recovery that in many situations outperforms ℓ1 minimization in the sense that substantially fewer measurements are needed for exact recovery.
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MR Image Reconstruction From Highly Undersampled k-Space Data by Dictionary Learning
TL;DR: Dramatic improvements on the order of 4-18 dB in reconstruction error and doubling of the acceptable undersampling factor using the proposed adaptive dictionary as compared to previous CS methods are demonstrated.