MRI denoising by NeighShrink based on chi-square unbiased risk estimation

MRI Evaluation of Indomethacin Suppositories in the Prevention of Complications of Pancreatitis and Hyperamylasemia after Choledocholithiasis ERCP Based on Image Denoising Algorithm

Objective

To explore the value of MRI evaluation of indomethacin suppositories in the prevention of pancreatitis and hyperamylasemia in patients with common bile duct calculi after endoscopic retrograde cholangiopancreatography (ERCP) based on image denoising algorithm.

Methods

A retrospective analysis in August 2020 to December 2021. Because of the common bile duct calculi hospitalized parallel ERCP operation, 89 cases of patients, according to the different postoperative treatments, were divided into group A (n = 44) and group B (n = 45), in which A set of separate application inhibits the pancreatic enzyme secretion after surgery drug treatment, and B group on the basis of group A linked with indole beauty Xinshuan treatment. The incidence of postoperative pancreatitis and hyperamylasemia was compared between the two groups. The levels of serum amylase were compared between the two groups. Patients in group B were diagnosed with pancreatitis by conventional MRI and MRI with denoising algorithm, respectively, and the imaging characteristics and diagnosis rate differences of the two methods were observed. ROC curve was drawn to evaluate the diagnostic efficacy of MRI denoising algorithm for postoperative pancreatitis and serum amylase level detection for hyperamylasemia.

Results

The incidence of postoperative pancreatitis and hyperamylasemia in group B was significantly lower than that in group A (P < 0.05). There were 6 cases of postoperative pancreatitis in group B, 2 cases (33.33%) were diagnosed by conventional MRI, and 5 cases (83.33%) were diagnosed by MRI based on denoising algorithm. Although there was no significant difference in diagnosis rate between the two methods, the number of cases of pancreatitis diagnosed by MRI based on denoising algorithm was slightly higher than that by conventional MRI. Compared with conventional MRI images, MRI images with denoising algorithm showed that the number of cases with pancreatic swelling, the number of cases with pancreatic duct/bile duct dilation, and the number of cases with abdominal effusion were all high (all P < 0.05). ROC results showed that the area under the curve of MRI with denoising algorithm for the diagnosis of postoperative pancreatitis was 0.855, and the sensitivity was 89.40%. The specificity was 83.20%, and the area under the curve of serum amylase for postoperative hyperamylasemia was 0.893, the sensitivity was 89.80%, and the specificity was 85.20%, all of which had high diagnostic efficacy.

Conclusion

MRI results of denoising algorithm suggest that indomethacin suppositories can effectively reduce the incidence of postoperative pancreatitis and hyperamylasemia after ERCP, which is worthy of clinical application.

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

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.

Deep Adaptive Blending Network for 3D Magnetic Resonance Image Denoising

The visual quality of magnetic resonance images (MRIs) is crucial for clinical diagnosis and scientific research. The main source of quality degradation is the noise generated during MRI acquisition. Although denoising MRI by deep learning methods shows great superiority compared with traditional methods, the deep learning methods reported to date in the literature cannot simultaneously leverage long-range and hierarchical information, and cannot adequately utilize the similarity in 3D MRI. In this paper, we address the two issues by proposing a deep adaptive blending network (DABN) characterized by a large receptive field residual dense block and an adaptive blending method. We first propose the large receptive field residual dense block that can capture long-range information and fuse hierarchical features simultaneously. Then we propose the adaptive blending method that produces denoised pixels by adaptively filtering 3D MRI, which explicitly utilizes the similarity in 3D MRI. Residual is also considered as a compensating item after adaptive filtering. The blending adaptive filter and residual are predicted by a network consisting of several large receptive field residual dense blocks. Experimental results show that the proposed DABN outperforms state-of-the-art denoising methods in both clinical and simulated MRI data.

An assessment of noise variance estimations in Bayes threshold denoising under stationary wavelet domain on brain lesions and tumor MRIs

Purpose

A major key success factor regarding proficient Bayes threshold denoising refers to noise variance estimation. This paper focuses on assessing different noise variance estimations in three Bayes threshold models on two different characteristic brain lesions/tumor magnetic resonance imaging (MRIs).

Design/methodology/approach

Here, three Bayes threshold denoising models based on different noise variance estimations under the stationary wavelet transforms (SWT) domain are mainly assessed, compared to state-of-the-art non-local means (NLMs). Each of those three models, namely D1, GB and DR models, respectively, depends on the most detail wavelet subband at the first resolution level, on the entirely global detail subbands and on the detail subband in each direction/resolution. Explicit and implicit denoising performance are consecutively assessed by threshold denoising and segmentation identification results.

Findings

Implicit performance assessment points the first–second best accuracy, 0.9181 and 0.9048 Dice similarity coefficient (Dice), sequentially yielded by GB and DR; reliability is indicated by 45.66% Dice dropping of DR, compared against 53.38, 61.03 and 35.48% of D1 GB and NLMs, when increasing 0.2 to 0.9 noise level on brain lesions MRI. For brain tumor MRI under 0.2 noise level, it denotes the best accuracy of 0.9592 Dice, resulted by DR; however, 8.09% Dice dropping of DR, relative to 6.72%, 8.85 and 39.36% of D1, GB and NLMs is denoted. The lowest explicit and implicit denoising performances of NLMs are obviously pointed.

Research limitations/implications

A future improvement of denoising performance possibly refers to creating a semi-supervised denoising conjunction model. Such model utilizes the denoised MRIs, resulted by DR and D1 thresholding model as uncorrupted image version along with the noisy MRIs, representing corrupted version ones during autoencoder training phase, to reconstruct the original clean image.

Practical implications

This paper should be of interest to readers in the areas of technologies of computing and information science, including data science and applications, computational health informatics, especially applied as a decision support tool for medical image processing.

Originality/value

In most cases, DR and D1 provide the first–second best implicit performances in terms of accuracy and reliability on both simulated, low-detail small-size region-of-interest (ROI) brain lesions and realistic, high-detail large-size ROI brain tumor MRIs.