Noise reduction filters based on pointwise MAP for CT images

2011 ◽  
Author(s):  
Rafael Jose Geraldo ◽  
Nelson D. A. Mascarenhas
Keyword(s):  
Noise Reduction ◽  
Ct Images

scholarly journals Image quality assessment of pediatric chest and abdomen CT by deep learning reconstruction

2021 ◽  
Author(s):  
Haesung Yoon ◽  
Jisoo Kim ◽  
Hyun Ji Lim ◽  
Mi-Jung Lee
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Noise Reduction ◽  
Iterative Reconstruction ◽  
Ct Images ◽  
Chest Ct ◽  
Abdomen Ct ◽  
Contrast Enhanced ◽  
Pediatric Chest ◽  
Body Ct

Abstract Background Efforts to reduce the radiation dose have continued steadily, with new reconstruction techniques. Recently, image denoising algorithms using artificial neural networks, termed deep learning reconstruction (DLR), have been applied to CT image reconstruction to overcome the drawbacks of iterative reconstruction (IR). The purpose of our study was to compare objective and subjective image quality of DLR and IR on pediatric abdomen and chest CT images.Methods This retrospective study included pediatric body CT images from February 2020 to October 2020, performed on 51 patients (34 boys and 17 girls; age 1–18 years). Non-contrast chest CT (n = 16), contrast-enhanced chest CT (n = 12), and contrast-enhanced abdomen CT (n = 23) images were included. Standard 50% adaptive statistical iterative reconstruction V (ASIR-V) images were compared to images with 100% ASIR-V and DLR at medium and high strengths. Attenuation, noise, contrast to noise ratio (CNR), and signal to noise (SNR) measurements were performed. Overall image quality, artifacts, and noise were subjectively assessed by two radiologists using a four-point scale (superior, average, suboptimal, and unacceptable). Quantitative and qualitative parameters were compared using repeated measures analysis of variance (ANOVA) with Bonferroni correction and Wilcoxon signed-rank tests.Results DLR had better CNR and SNR than 50% ASIR-V in both pediatric chest and abdomen CT images. When compared with 50% ASIR-V, high strength DLR was associated with noise reduction in non-contrast chest CT (33.0%), contrast-enhanced chest CT (39.6%), and contrast-enhanced abdomen CT (38.7%) with increases in CNR at 149.1%, 105.8% and 53.1% respectively. The subjective assessment of overall image quality and noise was also better on DLR images (p < 0.001). However, there was no significant difference in artifacts between reconstruction methods.Conclusion Compared with 50% ASIR-V, DLR improved pediatric body CT images with significant noise reduction. However, artifacts were not improved by DLR, regardless of strength.

Self-Adaptive Iterative Step Approach to Noise Reduction in Low-Dose CT Images

2017 ◽  
Vol 7 (1) ◽  
pp. 194-196
Author(s):  
Wei Zhang ◽  
Baolin Mao ◽  
Xiaozhao Chen ◽  
Luyang Wang ◽  
Shengyu Fan ◽  
...  
Keyword(s):  
Noise Reduction ◽  
Low Dose ◽  
Ct Images ◽  
Low Dose Ct ◽  
Self Adaptive

Wavelet Based Noise Reduction in CT-Images Using Correlation Analysis

2008 ◽  
Vol 27 (12) ◽  
pp. 1685-1703 ◽  
Author(s):  
A. Borsdorf ◽  
R. Raupach ◽  
T. Flohr ◽  
J. Hornegger
Keyword(s):  
Correlation Analysis ◽  
Noise Reduction ◽  
Ct Images

scholarly journals Noise Level and Similarity Analysis for Computed Tomographic Thoracic Image with Fast Non-Local Means Denoising Algorithm

2020 ◽  
Vol 10 (21) ◽  
pp. 7455
Author(s):  
Bae-Guen Kim ◽  
Seong-Hyeon Kang ◽  
Chan Rok Park ◽  
Hyun-Woo Jeong ◽  
Youngjin Lee
Keyword(s):  
Noise Reduction ◽  
Simulation Study ◽  
Signal To Noise Ratio ◽  
Ct Images ◽  
Male Adult ◽  
Computed Tomographic ◽  
Local Means ◽  
Thoracic Computed Tomography ◽  
Non Local ◽  
Noise Ratio

Although conventional denoising filters have been developed for noise reduction from digital images, these filters simultaneously cause blurring in the images. To address this problem, we proposed the fast non-local means (FNLM) denoising algorithm which would preserve the edge information of objects better than conventional denoising filters. In this study, we obtained thoracic computed tomography (CT) images from a male adult mesh (MASH) phantom modeled by computer and a five-year-old phantom to perform both the simulation study and the practical study. Subsequently, the FNLM denoising algorithm and conventional denoising filters, such as the Gaussian, median, and Wiener filters, were applied to the MASH phantom image adding Gaussian noise with a standard deviation of 0.002 and practical CT images. Finally, the results were compared quantitatively in terms of the coefficient of variation (COV), contrast-to-noise ratio (CNR), peak signal-to-noise ratio (PSNR), and correlation coefficient (CC). The results showed that the FNLM denoising algorithm was more efficient than the conventional denoising filters. In conclusion, through the simulation study and the practical study, this study demonstrated the feasibility of the FNLM denoising algorithm for noise reduction from thoracic CT images.

scholarly journals Evaluating a Convolutional Neural Network Noise Reduction Method When Applied to CT Images Reconstructed Differently Than Training Data

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Nathan R. Huber ◽  
Andrew D. Missert ◽  
Lifeng Yu ◽  
Shuai Leng ◽  
Cynthia H. McCollough
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Noise Reduction ◽  
Reduction Method ◽  
Ct Images ◽  
Training Data

scholarly journals Advanced Noise-Optimized Dual-Energy Virtual Monochromatic Imaging vs. Conventional 120-kVp CT Imaging: Image Quality Assessment

2021 ◽  
Author(s):  
Adnan Honardari ◽  
Ahmad Bitarafan-Rajabi ◽  
Razieh Solgi ◽  
Mahsa Shakeri ◽  
Kiara Rezaei-Kalantari ◽  
...  
Keyword(s):  
Image Quality ◽  
Noise Reduction ◽  
Contrast Material ◽  
Ct Images ◽  
Dual Energy ◽  
Image Noise ◽  
Dual Source Ct ◽  
Ct Number ◽  
Dual Source ◽  
Improvement Ratio

Purpose: This study aimed at evaluating the image quality characteristics of advanced noise-optimized and traditional virtual monochromatic images compared with conventional 120-kVp images from second-generation Dual-Source CT. Materials and Methods: For spiral scans six syringes filled with diluted iodine contrast material (1, 2, 5, 10, 15, 20 mg I/ml) were inserted into the test phantom and scanned with a second-generation dual-source CT in both single-energy (120-kVp) and dual-energy modes. Images set contain conventional single-energy 120-kVp, and virtual monochromatic were reconstructed with energies ranging from 40 to 190-keV in 1-keV steps. An energy-domain noise reduction algorithm was applied and the mean CT number, image noise, and iodine CNR were calculated. Results: The iodine CT number of conventional 120-kVp images compared with monochromatic of 40-, 50-, 60- and 70-keV images showed increase. The improvement ratio of image noise on Advanced Virtual Monochromatic Images (AVMIs) compared with the Traditional Virtual Monochromatic Images (TVMIs) at energies of 40-, 50-, 60, 70-keV was 52.9%, 35.7%, 8.1%, 2.1%, respectively. At AVMIs from 75- to 190-keV, the image noise value was less than conventional 120-kVp images. CNR improvement ratio at 20 mg/ml of iodinated contrast material for TVMIs and AVMIs compared to 120-kVp CT images and AVMIs compared to TVMI was 18.3% and 56.3%, 32.1% respectively. Conclusion: Both TVMIs (in energies ranging from 54 to 71-keV) and AVMIs (in energies ranging from 40 to 74-keV) represent improvement in the iodine contrast-to-noise ratio than conventional 120-kVp CT images for the same radiation dose. Also, AVMIs compared to TVMIs have been obtained considerable noise reduction and CNR improvement for low-energy virtual monochromatic images. In the present study, we show that virtual monochromatic image and its Advanced version (AVMI) may boost the dual-energy CT advantages by providing higher CNR images in the same exposure value compared to routinely acquired single-energy CT images.

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