k‐t FOCUSS: A general compressed sensing framework for high resolution dynamic MRI

By Hong Jung, K. Sung, K. Nayak, E. Kim, Jong Chul Ye. Magnetic Resonance in Medicine, 2009

746 citations recorded by the source corpus.

Subfields: Computational Mechanics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/d745f5742efa629485a086ed3952803272693c8b

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In Computational Mechanics

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

MR Image Reconstruction From Highly Undersampled k-Space Data by Dictionary Learning (2011, methodology): “Compressed Sensing has been applied to a variety of MR modalities such as static MRI [16], [19], [20], dynamic MRI [17],[21],[41],[ 42 ],andperfusionimaginganddiffusiontensor imaging (DTI) [45].”
Modified-CS: Modifying compressive sensing for problems with partially known support (2009, background): “But, with the exception of [8], [9], most existing solutions for the time series problem are noncausal or batch solutions with very high complexity since they jointly reconstruct the entire video by first collecting all the observations.”
Compressed Sensing MRI Reconstruction Using a Generative Adversarial Network With a Cyclic Loss (2017, methodology): “Conventional CS-MRI reconstruction methods have been developed to leverage the sparsity of signal by using universal sparsifying transforms, such as Fourier transform, Total Variation (TV), and Wavelets [3], and to exploit the spatio-temporal correlations, such as k− t FOCUSS [10], [11] This sparsit”
Golden-Angle Radial Sparse Parallel MRI: Combination of Compressed Sensing, Parallel Imaging, and Golden-Angle Radial Sampling for Fast and Flexible Dynamic Volumetric MRI (2013, methodology): “CS methods exploit spatial and temporal correlations by employing irregular undersampling schemes to create incoherent aliasing artifacts and using a non-linear reconstruction to enforce sparsity in a suitable transform domain (17-24).”
Compressed Sensing (2014, neutral): “Estas abarcan diversas áreas como: imagenoloǵıa medica [49, 45], conversión analógico/digital [68], detección mediante RADAR [4, 42], corrección de errores [18], cámaras digitales [34, 21, 51], Sismoloǵıa [41], Radioastronomı́a [71], comunicación inalámbrica [63, 3, 65] y redes de sensores inalámbri”
Magnetic resonance image reconstruction from undersampled measurements using a patch-based nonlocal operator (2014, methodology): “For the cardiac imaging, the temporal sparsity is represented with Fourier transform (Gamper et al., 2008; Jung et al., 2009; Otazo et al., 2010) and combined with low-rank strcture (Lingala et al., 2011; Zhao et al., 2012).”
RODEO: Robust DE-aliasing autoencOder for real-time medical image reconstruction (2017, background): “There are many extensions and improvements of the basic formulation [14-18].”
Deep Convolutional Neural Network for Inverse Problems in Imaging (2016, background): “The class is broad, encompassing at least denoising, deconvolution, and reconstruction of MRI, CT, and diffraction tomography images.”
Performance comparison between total variation (TV)-based compressed sensing and statistical iterative reconstruction algorithms (2009, result): “This result is consistent with the in vivo results of previous studies in both MRI (Block et al 2007, Lustig et al 2007, Gamper et al 2008, Jung et al 2009) and micro-CT (Song et al 2007).”
Compressed sensing reconstruction for magnetic resonance parameter mapping (2010, methodology): “For example, in dynamic imaging, sparsity along the temporal dimension is promoted by subtracting a composite image or applying a Fourier transform in case of periodic motion (10,11).”
Deep Residual Learning for Accelerated MRI Using Magnitude and Phase Networks (2018, background): “These algorithms are usually combined to provide further acceleration [5], [6].”
Deep-Learning Methods for Parallel Magnetic Resonance Imaging Reconstruction: A Survey of the Current Approaches, Trends, and Issues (2019, background): “Since the introduction of compressed sensing and its adoption for MRI [7], [8], nonlinear regularization terms, in particular, 1 , -norm-based ones, are popular in image reconstruction and commonly used in parallel imaging [8]–[14].”