A Multifeature Extraction Method Using Deep Residual Network for MR Image Denoising

Yao, Li

doi:10.1155/2020/8823861

Cited by 5 publications

(4 citation statements)

References 31 publications

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“…Batch normalization (BN), a neural network optimization method, can reduce the difficulty of learning, thereby increasing the speed of model practice. This method refers to a step of preprocessing in advance when processing each layer to standardize the data [ 13 ]. In this study, this method was employed to improve the algorithm based on CNN.…”

Section: Methodsmentioning

confidence: 99%

Magnetic Resonance Imaging Image Feature Analysis Algorithm under Convolutional Neural Network in the Diagnosis and Risk Stratification of Prostate Cancer

Gao¹,

Zhang

Wang³

et al. 2021

Journal of Healthcare Engineering

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This work aimed to explore the accuracy of magnetic resonance imaging (MRI) images based on the convolutional neural network (CNN) algorithm in the diagnosis of prostate cancer patients and tumor risk grading. A total of 89 patients with prostate cancer and benign prostatic hyperplasia diagnosed by MRI examination and pathological examination in hospital were selected as the research objects in this study (they passed the exclusion criteria). The MRI images of these patients were collected in two groups and divided into two groups before and after treatment according to whether the CNN algorithm was used to process them. The number of diagnosed diseases and the number of cases of risk level inferred based on the tumor grading were compared to observe which group was closer to the diagnosis of pathological biopsy. Through comparative analysis, compared with the positive rate of pathological diagnosis (44%), the positive rate after the treatment of the CNN algorithm (42%) was more similar to that before the treatment (34%), and the comparison was statistically marked ( P < 0.05 ). In terms of risk stratification, the grading results after treatment (37 cases) were closer to the results of pathological grading (39 cases) than those before treatment (30 cases), and the comparison was statistically obvious ( P < 0.05 ). In addition, it was obvious that the MRT images would be clearer after treatment through the observation of the MRT images before and after treatment. In conclusion, MRI image segmentation algorithm based on CNN was more accurate in the diagnosis and risk stratification of prostate cancer than routine MRI. According to the evaluation of Dice similarity coefficient (DSC) and Hausdorff I distance (HD), the CNN segmentation method used in this study was more perfect than other segmentation methods.

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Section: Methodsmentioning

confidence: 99%

Magnetic Resonance Imaging Image Feature Analysis Algorithm under Convolutional Neural Network in the Diagnosis and Risk Stratification of Prostate Cancer

Gao¹,

Zhang

Wang³

et al. 2021

Journal of Healthcare Engineering

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“…The projection data were extracted from the scanner to add the synthetic noise using a known noise model, then the modified projection data were returned to the scanner to utilize weighted, filtered back-projection (FBP). The simulation target was 10% of the full-dose radiation dose for the chest scan [12,20].…”

Section: Noise Simulationmentioning

confidence: 99%

“…Adam performs better than Scholastic Gradient Descent (SGD), Root Mean Square Propagation (RMSProp), Adaptive Gradient (AdaGrad), and Adaptive Delta (AdaDelta) [35]. Two previous studies used the same optimization for CNN image denoising [36] and MRI image denoising using the feature extraction method [20]. Each block before contraction or expansion layer may have different convolutional layers, one and two are the most common.…”

Section: Selection Of Denoising Techniquementioning

confidence: 99%

Image Recovery from Synthetic Noise Artifacts in CT Scans Using Modified U-Net

Gunawan

Tran

Zheng

et al. 2022

Sensors

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Computed Tomography (CT) is commonly used for cancer screening as it utilizes low radiation for the scan. One problem with low-dose scans is the noise artifacts associated with low photon count that can lead to a reduced success rate of cancer detection during radiologist assessment. The noise had to be removed to restore detail clarity. We propose a noise removal method using a new model Convolutional Neural Network (CNN). Even though the network training time is long, the result is better than other CNN models in quality score and visual observation. The proposed CNN model uses a stacked modified U-Net with a specific number of feature maps per layer to improve the image quality, observable on an average PSNR quality score improvement out of 174 images. The next best model has 0.54 points lower in the average score. The score difference is less than 1 point, but the image result is closer to the full-dose scan image. We used separate testing data to clarify that the model can handle different noise densities. Besides comparing the CNN configuration, we discuss the denoising quality of CNN compared to classical denoising in which the noise characteristics affect quality.

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“…A CNN model is used to process undersampled images, and finally fully sampled MRI images are obtained. e calculation equation and structure are shown in Figure 1.where y i represents the undersampled MRI image, T 1 , T 2 , and T 3 represent the weight, a 1 , a 2 , and a 3 represent the offset, * represents the convolution operation, ReLU represents the activation function [13], and BM represents the regularization operation [14].…”

Section: Mrh � F(mrl)mentioning

confidence: 99%

Deep Learning-Based MRI in Diagnosis of Fracture of Tibial Plateau Combined with Meniscus Injury

Xie

Bai

et al. 2021

Scientific Programming

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This study aimed to explore the application value of magnetic resonance imaging (MRI) images based on deep learning algorithms in the diagnosis of tibial plateau fractures combined with meniscus injuries. The original MRI image was input into the deep learning convolutional neural network (CNN), and the knee joint undersampled and fully sampled MRI image data were used for training to obtain a neural network model that can effectively remove the noise and blur of the undersampled image. Then, the image was reconstructed by the Regridding model to obtain an image with less noise and clearer structure. At the same time, all subjects underwent knee MRI examinations, and algorithms were used to analyze the sensitivity, specificity, and accuracy of their images. It was found that of 160 menisci from 80 cases of tibial plateau fractures, 64 were normal meniscus and 88 were injured menisci. The sensitivity, specificity, and accuracy of optimized MRI in diagnosing fracture of tibial plateau combined with meniscus injury were 96.9%, 93.2%, and 95.3%, respectively. In conclusion, the restored MRI images have high sensitivity in the diagnosis of meniscus injury and high consistency with the intraoperative results. It suggests that the optimized MRI image is effective in the diagnosis of meniscus injury.

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A Multifeature Extraction Method Using Deep Residual Network for MR Image Denoising

Cited by 5 publications

References 31 publications

Magnetic Resonance Imaging Image Feature Analysis Algorithm under Convolutional Neural Network in the Diagnosis and Risk Stratification of Prostate Cancer

Magnetic Resonance Imaging Image Feature Analysis Algorithm under Convolutional Neural Network in the Diagnosis and Risk Stratification of Prostate Cancer

Image Recovery from Synthetic Noise Artifacts in CT Scans Using Modified U-Net

Deep Learning-Based MRI in Diagnosis of Fracture of Tibial Plateau Combined with Meniscus Injury

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