scholarly journals Analysis on Characteristics of Magnetic Resonance Imaging Image under Low-Rank Matrix Denoising Algorithm in the Diagnosis of Cerebral Aneurysm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Jun Li ◽  
Jin Li ◽  
Qin Hu
Keyword(s):  
Magnetic Resonance Imaging ◽  
Cerebral Aneurysm ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Low Rank ◽  
Resonance Imaging ◽  
Rank Matrix ◽  
Before And After ◽  
Low Rank Matrix

This study was to explore the effect of a low-rank matrix denoising (LRMD) algorithm based on the Gaussian mixture model (GMM) on magnetic resonance imaging (MRI) images of patients with cerebral aneurysm and to evaluate the practical value of the LRMD algorithm in the clinical diagnosis of cerebral aneurysm. In this study, the intracranial MRI data of 40 patients with cerebral aneurysm were selected to study the denoising effect of the low-rank matrix denoising algorithm based on the Gaussian mixture model on MRI images of cerebral aneurysm under the influence of Rice noise, to evaluate the PSNR value, SSIM value, and clarity of MRI images before and after denoising. The diagnostic accuracy of MRI images of cerebral aneurysms before and after denoising was compared. The results showed that after the low-rank matrix denoising algorithm based on the Gaussian mixture model, the PSNR, SSIM, and sharpness values of intracranial MRI images of 10 patients were significantly improved ( P < 0.05 ), and the diagnostic accuracy of MRI images of cerebral aneurysm increased from 76.2 ± 5.6 % to 93.1 ± 7.9 % , which could diagnose cerebral aneurysm more accurately and quickly. In conclusion, the MRI images processed based on the low-rank matrix denoising algorithm under the Gaussian mixture model can effectively remove the interference of noise, improve the quality of MRI images, optimize the accuracy of MRI image diagnosis of patients with cerebral aneurysm, and shorten the average diagnosis time, which is worth promoting in the clinical diagnosis of patients with cerebral aneurysm.

EXACT LOW-RANK MATRIX RECOVERY VIA NONCONVEX SCHATTEN p-MINIMIZATION

2013 ◽  
Vol 30 (03) ◽  
pp. 1340010 ◽  
Author(s):  
LINGCHEN KONG ◽  
NAIHUA XIU
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Low Rank ◽  
Magnetic Resonance Imaging System ◽  
Resonance Imaging ◽  
Matrix Recovery ◽  
Rank Matrix ◽  
Restricted Isometry Constant ◽  
Low Rank Matrix Recovery ◽  
Low Rank Matrix

The low-rank matrix recovery (LMR) arises in many fields such as signal and image processing, quantum state tomography, magnetic resonance imaging, system identification and control, and it is generally NP-hard. Recently, Majumdar and Ward [Majumdar, A and RK Ward (2011). An algorithm for sparse MRI reconstruction by Schatten p-norm minimization. Magnetic Resonance Imaging, 29, 408–417]. had successfully applied nonconvex Schatten p-minimization relaxation of LMR in magnetic resonance imaging. In this paper, our main aim is to establish RIP theoretical result for exact LMR via nonconvex Schatten p-minimization. Carefully speaking, letting [Formula: see text] be a linear transformation from ℝm×n into ℝs and r be the rank of recovered matrix X ∈ ℝm×n, and if [Formula: see text] satisfies the RIP condition [Formula: see text] for a given positive integer k ∈ {1, 2, …, m – r}, then r-rank matrix can be exactly recovered. In particular, we obtain a uniform bound on restricted isometry constant [Formula: see text] for any p ∈ (0, 1] for LMR via Schatten p-minimization.

scholarly journals Evaluating the Effect of Intensity Standardisation on Longitudinal Whole Brain Atrophy Quantification in Brain Magnetic Resonance Imaging

2020 ◽  
Author(s):  
Emily Esmeralda Carvajal-Camelo ◽  
Jose Bernal ◽  
Arnau Oliver ◽  
Xavier Lladó ◽  
Maria Trujillo
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Brain Magnetic Resonance Imaging ◽  
Kernel Density ◽  
Gaussian Mixture ◽  
Resonance Imaging ◽  
Fuzzy C Means ◽  
Skull Stripping

Atrophy quantification is fundamental for understanding brain development and diagnosing and monitoring brain diseases. FSL-SIENA is a well-known fully-automated method that has been widely used in brain magnetic resonance imaging studies. However, intensity variations arising during image acquisition that may compromise evaluation, analysis and even diagnosis. In this work, we study whether intensity standardisation can improve longitudinal atrophy quantification. We considered seven methods comprising z-score, fuzzy c-means, Gaussian mixture model, kernel density, histogram matching, white stripe, and removal of artificial voxel effects by linear regression (RAVEL). We used a total of 330 scans from two publicly-available datasets, ADNI and OASIS. In scan-rescan assessments, that measures robustness to subtle imaging variations, intensity standardisation did not compromise the robustness of FSL-SIENA significantly (p&gt;0.1). In power analysis assessments, that measures the ability to discern between two groups of subjects, three methods led to consistent improvements in both datasets with respect to the original: fuzzy c-means, Gaussian mixture model, and kernel density estimation. Reduction in sample size using these three methods ranged from 17% to 95%. The performance of the other four methods was affected by spatial normalisation, skull stripping errors, presence of periventricular white matter hyperintensities, or tissue proportion variations over time. Our work evinces the relevance of appropriate intensity standardisation in longitudinal cerebral atrophy assessments using FSL-SIENA.

scholarly journals Low-Rank Matrix Denoising Algorithm-Based Magnetic Resonance Imaging Combined with Computed Tomography Images in the Diagnosis of Cerebral Aneurysm

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Daigui Zhang ◽  
Lihua Zhou ◽  
Tingdi Zhang ◽  
Shuai Wang ◽  
Yue Li
Keyword(s):  
Magnetic Resonance Imaging ◽  
Computed Tomography ◽  
Magnetic Resonance ◽  
Low Rank ◽  
Resonance Imaging ◽  
Ct Diagnosis ◽  
Rank Matrix ◽  
Mri Diagnosis ◽  
Low Rank Matrix ◽  
Better Than

This study was to analyze the diagnostic effects of computed tomography (CT) and magnetic resonance imaging (MRI) in patients with cerebrovascular diseases (CVDs) based on low-rank matrix denoising (LRMD) algorithm. The LRMD algorithm was adopted for MRI diagnosis and CT diagnosis for comparative analysis. 129 CVD patients were selected as the research objects, 43 cases were diagnosed by CT, 43 cases were diagnosed by MRI under LRMD, and the other 43 cases were diagnosed by CT + MRI. The results showed that the diagnostic compliance rates (DCRs) of CT group in the cerebral hemorrhage (CH), cerebral infarction (CI), and cerebral aneurysm (CA) were 95.1%, 94.7%, and 70%, respectively, while those in the MRI group were 99.01%, 97.71%, and 100%, respectively. Thus, it was obtained that MRI diagnosis was much better than CT diagnosis, and CT + MRI showed the best diagnosis efficacy, showing statistical differences ( P < 0.05 ). The accuracy, sensitivity, and specificity of MRI diagnosis under the LRMD algorithm were 96.28%, 88.76%, and 90.62%, respectively, which were superior to those of CT diagnosis (92.71%, 84.94%, and 80.71%, respectively). The diagnosis cost per case (DC/C) (799.73 ± 100.02 yuan) and the total diagnosis cost (TDC) (58,521.67 ± 301.62 yuan) in the MRI group were higher than those in the CT group (601.42 ± 83.61 yuan and 39,819.2 ± 198.72, respectively) ( P < 0.05 ). In conclusion, CT + MRI under the LRMD algorithm showed good potential in diagnosis of CVD; MRI based on the LRMD algorithm showed a higher positive rate in the diagnosis of CA and was better than CT diagnosis, and CT + MRI showed the best diagnosis effect and could improve the clinical diagnosis rate.

scholarly journals Hyperspectral Unmixing with Gaussian Mixture Model and Low-Rank Representation

2019 ◽  
Vol 11 (8) ◽  
pp. 911 ◽  
Author(s):  
Yong Ma ◽  
Qiwen Jin ◽  
Xiaoguang Mei ◽  
Xiaobing Dai ◽  
Fan Fan ◽  
...  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Spatial Data ◽  
Principal Component ◽  
Gaussian Mixture ◽  
Low Rank ◽  
Hyperspectral Unmixing ◽  
Low Rank Representation ◽  
Synthetic Datasets ◽  
Endmember Variability

Gaussian mixture model (GMM) has been one of the most representative models for hyperspectral unmixing while considering endmember variability. However, the GMM unmixing models only have proper smoothness and sparsity prior constraints on the abundances and thus do not take into account the possible local spatial correlation. When the pixels that lie on the boundaries of different materials or the inhomogeneous region, the abundances of the neighboring pixels do not have those prior constraints. Thus, we propose a novel GMM unmixing method based on superpixel segmentation (SS) and low-rank representation (LRR), which is called GMM-SS-LRR. we adopt the SS in the first principal component of HSI to get the homogeneous regions. Moreover, the HSI to be unmixed is partitioned into regions where the statistical property of the abundance coefficients have the underlying low-rank property. Then, to further exploit the spatial data structure, under the Bayesian framework, we use GMM to formulate the unmixing problem, and put the low-rank property into the objective function as a prior knowledge, using generalized expectation maximization to solve the objection function. Experiments on synthetic datasets and real HSIs demonstrated that the proposed GMM-SS-LRR is efficient compared with other current popular methods.

scholarly journals Low-Rank Matrix Denoising Algorithm-Based MRI Image Feature for Therapeutic Effect Evaluation of NCRT on Rectal Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Qin Hu ◽  
Jin Li ◽  
Jun Li
Keyword(s):  
Rectal Cancer ◽  
Cancer Patients ◽  
Image Feature ◽  
Conventional Group ◽  
Low Rank ◽  
Pathological Examination ◽  
Conventional Mri ◽  
Rank Matrix ◽  
Before And After ◽  
Low Rank Matrix

This study aimed to explore the therapeutic effects of neoadjuvant chemoradiotherapy (NCRT) on rectal cancer patients using the MRI based on low-rank matrix denoising algorithm, which was then compared with the postoperative pathological examination to evaluate its application value in tumor staging after NCRT treatment. 15 patients with rectal cancer who met the requirements of radiotherapy and chemotherapy after conventional MRI were selected as the research subjects. The conventional MRI images before and after NCRT treatment were divided in two groups. One group was not processed and set as the conventional group; the other group was processed with low-rank matrix denoising algorithm and set as the optimized group. The two groups of images were observed for the changes in the ADC value and length and thickness of the tumor before and after NCRT treatment. The two groups were compared with the pathological examination for the complete remission of pathology (pCR) after the NCRT treatment and the tumor stage results. The results showed that Root Mean Square Error (RMSE) and Peak Signal to Noise Ratio (PSNR) (18.9121 and 74.9911 dB) after introducing the low-rank matrix denoising algorithm were significantly better than those before (20.1234 and 70.1234 dB) ( P < 0.05 ); there were notable differences in the tumor index data within the two groups before and after NCRT treatment ( P < 0.05 ), indicating that the NCRT treatment was effective. The pathological examination results of pCR data of the two groups were not much different ( P > 0.05 ); the examination results between the two groups were different, but no notable difference was noted ( P < 0.05 ); in the optimized group, there was no notable difference between the MRI results and the pathological examination results ( P < 0.05 ), while in the conventional group, there were notable differences in the MRI results and pathological examination results ( P < 0.05 ). In conclusion, MRI images based on low-rank matrix denoising algorithm are clearer, which can improve the diagnosis rate of patients and better display the changes of the microenvironment after NCRT treatment. It also indicates that NCRT treatment has significant clinical effects in the treatment of rectal cancer patients, which is worth promoting.

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