scholarly journals P-LORAKS: Low-rank modeling of local k-space neighborhoods with parallel imaging data

2015 ◽  
Vol 75 (4) ◽  
pp. 1499-1514 ◽  
Author(s):  
Justin P. Haldar ◽  
Jingwei Zhuo
Keyword(s):  
Parallel Imaging ◽  
Low Rank ◽  
Imaging Data

scholarly journals High-Resolution Cardiovascular MRI by Integrating Parallel Imaging With Low-Rank and Sparse Modeling

2013 ◽  
Vol 60 (11) ◽  
pp. 3083-3092 ◽  
Author(s):  
Anthony G. Christodoulou ◽  
Haosen Zhang ◽  
Bo Zhao ◽  
T. Kevin Hitchens ◽  
Chien Ho ◽  
...  
Keyword(s):  
High Resolution ◽  
Parallel Imaging ◽  
Low Rank ◽  
Cardiovascular Mri ◽  
Sparse Modeling

scholarly journals Multiple Optical Sensor Fusion for Mineral Mapping of Core Samples

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3766
Author(s):  
Behnood Rasti ◽  
Pedram Ghamisi ◽  
Peter Seidel ◽  
Sandra Lorenz ◽  
Richard Gloaguen
Keyword(s):  
Feature Extraction ◽  
Sensor Fusion ◽  
Optical Sensor ◽  
Feature Fusion ◽  
Heterogeneous Data ◽  
Hyperspectral Data ◽  
Low Rank ◽  
Svm Classifier ◽  
Imaging Data ◽  
Rank Analysis

Geological objects are characterized by a high complexity inherent to a strong compositional variability at all scales and usually unclear class boundaries. Therefore, dedicated processing schemes are required for the analysis of such data for mineralogical mapping. On the other hand, the variety of optical sensing technology reveals different data attributes and therefore multi-sensor approaches are adapted to solve such complicated mapping problems. In this paper, we devise an adapted multi-optical sensor fusion (MOSFus) workflow which takes the geological characteristics into account. The proposed processing chain exhaustively covers all relevant stages, including data acquisition, preprocessing, feature fusion, and mineralogical mapping. The concept includes (i) a spatial feature extraction based on morphological profiles on RGB data with high spatial resolution, (ii) a specific noise reduction applied on the hyperspectral data that assumes mixed sparse and Gaussian contamination, and (iii) a subsequent dimensionality reduction using a sparse and smooth low rank analysis. The feature extraction approach allows one to fuse heterogeneous data at variable resolutions, scales, and spectral ranges and improve classification substantially. The last step of the approach, an SVM classifier, is robust to unbalanced and sparse training sets and is particularly efficient with complex imaging data. We evaluate the performance of the procedure with two different multi-optical sensor datasets. The results demonstrate the superiority of this dedicated approach over common strategies.

scholarly journals An Efficient Compressive Hyperspectral Imaging Algorithm Based on Sequential Computations of Alternating Least Squares

2019 ◽  
Vol 11 (24) ◽  
pp. 2932 ◽  
Author(s):  
Geunseop Lee
Keyword(s):  
Least Squares ◽  
Hyperspectral Imaging ◽  
Optimization Problems ◽  
Tensor Decomposition ◽  
Alternating Least Squares ◽  
Low Rank ◽  
Imaging Data ◽  
Significant Difference ◽  
Multilinear Rank ◽  
Value Decomposition

Hyperspectral imaging is widely used to many applications as it includes both spatial and spectral distributions of a target scene. However, a compression, or a low multilinear rank approximation of hyperspectral imaging data, is required owing to the difficult manipulation of the massive amount of data. In this paper, we propose an efficient algorithm for higher order singular value decomposition that enables the decomposition of a tensor into a compressed tensor multiplied by orthogonal factor matrices. Specifically, we sequentially compute low rank factor matrices from the Tucker-1 model optimization problems via an alternating least squares approach. Experiments with real world hyperspectral imaging revealed that the proposed algorithm could compute the compressed tensor with a higher computational speed, but with no significant difference in accuracy of compression compared to the other tensor decomposition-based compression algorithms.

Biplanar MRI for the assessment of the spinal cord in multiple sclerosis

2012 ◽  
Vol 18 (11) ◽  
pp. 1560-1569 ◽  
Author(s):  
Katrin Weier ◽  
Jilla Mazraeh ◽  
Yvonne Naegelin ◽  
Alain Thoeni ◽  
Jochen G Hirsch ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Spinal Cord ◽  
Parallel Imaging ◽  
Proton Density ◽  
Imaging Data ◽  
Lesion Count ◽  
Focal Lesions ◽  
Mri Findings ◽  
Reporting Scheme ◽  
Functional Scores

Objective: To investigate the entire spinal cord (SC) of multiple sclerosis (MS) patients with biplanar MRI and to relate these MRI findings to clinical functional scores. Methods: Two hundred and two patients (140 women, 62 men 24–74 years, Expanded Disability Status Scale (EDSS) scores 0–7.5) were investigated clinically and with biplanar MRI. Sagittal and axial proton density weighted (PDw) and T2 weighted (T2w) images of the whole SC were obtained employing parallel imaging. Data were analyzed by consensus reading using a standardized reporting scheme. Different combinations of findings were compared to EDSS scores with Spearman’s rank correlation coefficient (ρ). Results: The combined analysis of sagittal and axial planes demonstrated slightly differing results in 97/202 (48%) patients. There were 9% additional lesions identified, leading to a higher lesion count in 28% of these patients, but also rejection of equivocal abnormality leading to a lower lesion count in 11% of patients. Considering both sagittal and axial images, SC abnormalities were found in 167/202 (83%) patients. When compared with EDSS scores, the combination of focal lesions, signs of atrophy and diffuse abnormalities showed a moderate correlation (ρ=0.52), that precludes its use for individual patient assessment. Conclusion: Biplanar MRI facilitates a comprehensive identification, localization, and grading of pathological SC findings in MS patients. This improves the confidence and utility of SC imaging.

Automated weak signal extraction of hyperspectral Raman imaging data by adaptive low‐rank matrix approximation

2020 ◽  
Vol 51 (12) ◽  
pp. 2552-2561
Author(s):  
Hao He ◽  
Chun Lin ◽  
Cheng Zong ◽  
Mengxi Xu ◽  
Peng Zheng ◽  
...  
Keyword(s):  
Raman Imaging ◽  
Low Rank ◽  
Signal Extraction ◽  
Weak Signal ◽  
Matrix Approximation ◽  
Imaging Data ◽  
Rank Matrix ◽  
Low Rank Matrix ◽  
Low Rank Matrix Approximation

scholarly journals Denoising MR Spectroscopic Imaging Data With Low-Rank Approximations

2013 ◽  
Vol 60 (1) ◽  
pp. 78-89 ◽  
Author(s):  
Hien M. Nguyen ◽  
Xi Peng ◽  
Minh N. Do ◽  
Zhi-Pei Liang
Keyword(s):  
Spectroscopic Imaging ◽  
Low Rank ◽  
Imaging Data ◽  
Low Rank Approximations ◽  
Mr Spectroscopic Imaging
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