scholarly journals Low-Rank and Sparse Decomposition Model for Accelerating Dynamic MRI Reconstruction

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Junbo Chen ◽  
Shouyin Liu ◽  
Min Huang
Keyword(s):  
Inverse Problem ◽  
Convex Optimization ◽  
Principal Component ◽  
Dynamic Mri ◽  
Low Rank ◽  
Cine Image ◽  
Sparse Decomposition ◽  
Decomposition Model ◽  
Reconstruction Methods ◽  
Space Data

The reconstruction of dynamic magnetic resonance imaging (dMRI) from partially sampled k-space data has to deal with a trade-off between the spatial resolution and temporal resolution. In this paper, a low-rank and sparse decomposition model is introduced to resolve this issue, which is formulated as an inverse problem regularized by robust principal component analysis (RPCA). The inverse problem can be solved by convex optimization method. We propose a scalable and fast algorithm based on the inexact augmented Lagrange multipliers (IALM) to carry out the convex optimization. The experimental results demonstrate that our proposed algorithm can achieve superior reconstruction quality and faster reconstruction speed in cardiac cine image compared to existing state-of-art reconstruction methods.

scholarly journals Rank-One and Transformed Sparse Decomposition for Dynamic Cardiac MRI

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Xianchao Xiu ◽  
Lingchen Kong
Keyword(s):  
Cardiac Mri ◽  
Low Rank ◽  
Cine Mri ◽  
Sparse Decomposition ◽  
Sparse Model ◽  
Cardiac Cine ◽  
Rank One ◽  
Magnetic Resonance Imaging Mri ◽  
Alternative Direction Method ◽  
Space Data

It is challenging and inspiring for us to achieve high spatiotemporal resolutions in dynamic cardiac magnetic resonance imaging (MRI). In this paper, we introduce two novel models and algorithms to reconstruct dynamic cardiac MRI data from under-sampledk-tspace data. In contrast to classical low-rank and sparse model, we use rank-one and transformed sparse model to exploit the correlations in the dataset. In addition, we propose projected alternative direction method (PADM) and alternative hard thresholding method (AHTM) to solve our proposed models. Numerical experiments of cardiac perfusion and cardiac cine MRI data demonstrate improvement in performance.

Task-free brainprint recognition based on low-rank and sparse decomposition model

2019 ◽  
Vol 22 (3) ◽  
pp. 280 ◽  
Author(s):  
Wanzeng Kong ◽  
Xianghao Kong ◽  
Qiaonan Fan ◽  
Qibin Zhao ◽  
Andrzej Cichocki
Keyword(s):  
Low Rank ◽  
Sparse Decomposition ◽  
Decomposition Model

Task-free brainprint recognition based on low-rank and sparse decomposition model

2019 ◽  
Vol 22 (3) ◽  
pp. 280
Author(s):  
Wanzeng Kong ◽  
Xianghao Kong ◽  
Qiaonan Fan ◽  
Qibin Zhao ◽  
Andrzej Cichocki
Keyword(s):  
Low Rank ◽  
Sparse Decomposition ◽  
Decomposition Model

Multi-dimensional low rank plus sparse decomposition for reconstruction of under-sampled dynamic MRI

2017 ◽  
Vol 63 ◽  
pp. 667-679 ◽  
Author(s):  
Shahrooz F. Roohi ◽  
Dornoosh Zonoobi ◽  
Ashraf A. Kassim ◽  
Jacob L. Jaremko
Keyword(s):  
Dynamic Mri ◽  
Low Rank ◽  
Sparse Decomposition

scholarly journals Robust Principal Component Analysis-Based Infrared Small Target Detection

2019 ◽  
Vol 33 ◽  
pp. 9925-9926
Author(s):  
Qiwei Chen ◽  
Cheng Wu ◽  
Yiming Wang
Keyword(s):  
Iteration Process ◽  
Principal Component ◽  
Component Analysis ◽  
Low Rank ◽  
Small Target ◽  
Frame Sequence ◽  
Augmented Lagrange Multiplier ◽  
Small Targets ◽  
Infrared Small Target ◽  
Augmented Lagrange Multiplier Method

A method based on Robust Principle Component Analysis (RPCA) technique is proposed to detect small targets in infrared images. Using the low rank characteristic of background and the sparse characteristic of target, the observed image is regarded as the sum of a low-rank background matrix and a sparse outlier matrix, and then the decomposition is solved by the RPCA. The infrared small target is extracted from the single-frame image or multi-frame sequence. In order to get more efficient algorithm, the iteration process in the augmented Lagrange multiplier method is improved. The simulation results show that the method can detect out the small target precisely and efficiently.

Computing Robust Principal Components by A* Search

2018 ◽  
Vol 27 (07) ◽  
pp. 1860013 ◽  
Author(s):  
Swair Shah ◽  
Baokun He ◽  
Crystal Maung ◽  
Haim Schweitzer
Keyword(s):  
State Of The Art ◽  
Principal Component ◽  
Low Rank ◽  
A Algorithm ◽  
Running Time ◽  
Current State ◽  
Dimensionality Reduction Technique ◽  
Related Variant ◽  
Low Rank Representation ◽  
The Cost

Principal Component Analysis (PCA) is a classical dimensionality reduction technique that computes a low rank representation of the data. Recent studies have shown how to compute this low rank representation from most of the data, excluding a small amount of outlier data. We show how to convert this problem into graph search, and describe an algorithm that solves this problem optimally by applying a variant of the A* algorithm to search for the outliers. The results obtained by our algorithm are optimal in terms of accuracy, and are shown to be more accurate than results obtained by the current state-of-the- art algorithms which are shown not to be optimal. This comes at the cost of running time, which is typically slower than the current state of the art. We also describe a related variant of the A* algorithm that runs much faster than the optimal variant and produces a solution that is guaranteed to be near the optimal. This variant is shown experimentally to be more accurate than the current state-of-the-art and has a comparable running time.

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