scholarly journals Low-Dose Computed Tomography Filtering Using Geodesic Distances

2020 ◽  
Author(s):  
Daniel A. Góes ◽  
Nelson D. A. Mascarenhas
Keyword(s):  
Computed Tomography ◽  
Low Dose ◽  
Convex Sets ◽  
Projection Onto Convex Sets ◽  
Local Means ◽  
Gamma Distributions ◽  
Non Local ◽  
Geodesic Distances ◽  
Low Dose Computed Tomography

Due to the concerns related to patient exposure to X-ray, the dosage used in computed tomography must be reduced (Low-dose Computed Tomography - LDCT). One of the effects of LDCT is the degradation in the quality of the final reconstructed image. In this work, we propose a method of filtering LDCT sinograms that are subject to signal-dependent Poisson noise. To filter this type of noise, we use a Bayesian approach, changing the Non-local Means (NLM) algorithm to use geodesic stochastic distances for Gamma distribution, the conjugate prior to Poisson, as a similarity metric between each projection point. Among the geodesic distances evaluated, we found a closed solution for the Shannon entropy for Gamma distributions. We compare our method with the following methods based on NLM: PoissonNLM, Stochastic Poisson NLM, Stochastic Gamma NLM and the original NLM after Anscombe transform. We also compare with BM3D after Anscombe transform. Comparisons are made on the final images reconstructed by the Filtered-Back Projection (FBP) and Projection onto Convex Sets (POCS) methods using the metrics PSNR and SSIM.

scholarly journals SR-NLM: A sinogram restoration induced non-local means image filtering for low-dose computed tomography

2013 ◽  
Vol 37 (4) ◽  
pp. 293-303 ◽  
Author(s):  
Zhaoying Bian ◽  
Jianhua Ma ◽  
Jing Huang ◽  
Hua Zhang ◽  
Shanzhou Niu ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Low Dose ◽  
Image Filtering ◽  
Local Means ◽  
Non Local ◽  
Sinogram Restoration ◽  
Low Dose Computed Tomography

scholarly journals Low-dose computed tomography image denoising based on joint wavelet and sparse representation.

2021 ◽  
Author(s):  
Samira Ghadrdan
Keyword(s):  
Computed Tomography ◽  
Sparse Representation ◽  
Image Denoising ◽  
Low Dose ◽  
Diagnostic Purpose ◽  
Noise Level Estimation ◽  
Tomography Image ◽  
Computed Tomography Image ◽  
Low Dose Computed Tomography

One of the most challenging issues in low dose computed tomography (CT) imaging is image denoising and signal enhancement. Sparse representational methods have shown initial promise for these applications. In this thesis we present a wavelet based sparse representation denoising technique utilizing dictionary learning and clustering. By using wavelets we extract the most suitable features in the images to obtain accurate dictionary atoms for the denoising algorithm. To achieve improved results we also lower the number of clusters which reduces computational complexity. In addition, a single image noise level estimation is developed to update the cluster centers in higher PSNRs. A new image enhancement technique is developed for low-dose CT images to improve the quality of image for diagnostic purpose and reduce the blurring artifacts. The accuracy along with the computational efficiency of the proposed algorithm are then compared with recent approaches and clearly demonstrate the improvement of the proposed algorithm proposed in this thesis.

scholarly journals Low-dose computed tomography image denoising based on joint wavelet and sparse representation.

2021 ◽  
Author(s):  
Samira Ghadrdan
Keyword(s):  
Computed Tomography ◽  
Sparse Representation ◽  
Image Denoising ◽  
Low Dose ◽  
Diagnostic Purpose ◽  
Noise Level Estimation ◽  
Tomography Image ◽  
Computed Tomography Image ◽  
Low Dose Computed Tomography

One of the most challenging issues in low dose computed tomography (CT) imaging is image denoising and signal enhancement. Sparse representational methods have shown initial promise for these applications. In this thesis we present a wavelet based sparse representation denoising technique utilizing dictionary learning and clustering. By using wavelets we extract the most suitable features in the images to obtain accurate dictionary atoms for the denoising algorithm. To achieve improved results we also lower the number of clusters which reduces computational complexity. In addition, a single image noise level estimation is developed to update the cluster centers in higher PSNRs. A new image enhancement technique is developed for low-dose CT images to improve the quality of image for diagnostic purpose and reduce the blurring artifacts. The accuracy along with the computational efficiency of the proposed algorithm are then compared with recent approaches and clearly demonstrate the improvement of the proposed algorithm proposed in this thesis.

Liver density in routine and low-dose computed tomography: the effect of image noise on measurement accuracy

2020 ◽  
Vol 24 (1) ◽  
pp. 39-47
Author(s):  
A. P. Gonchar ◽  
V. A. Gombolevskij ◽  
A. B. Elizarov ◽  
N. S. Kulberg ◽  
V. G. Klyashtorny ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Measurement Accuracy ◽  
Low Dose ◽  
Image Noise ◽  
Low Dose Computed Tomography

LOW-DOSE COMPUTED TOMOGRAPHY IN MOSCOW FOR LUNG CANCER SCREENING (LDCT-MLCS): BASELINE RESULTS

2019 ◽  
Vol 65 (2) ◽  
pp. 224-233
Author(s):  
Sergey Morozov ◽  
Viktor Gombolevskiy ◽  
Anton Vladzimirskiy ◽  
Albina Laypan ◽  
Pavel Kononets ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Cancer Screening ◽  
Low Dose ◽  
Early Stage ◽  
Lung Cancer Screening ◽  
Stage I ◽  
Malignant Neoplasms ◽  
Number Needed To Screen ◽  
Low Dose Computed Tomography

Study aim. To justify selective lung cancer screening via low-dose computed tomography and evaluate its effectiveness. Materials and methods. In 2017 we have concluded the baseline stage of “Lowdose computed tomography in Moscow for lung cancer screening (LDCT-MLCS)” trial. The trial included 10 outpatient clinics with 64-detector CT units (Toshiba Aquilion 64 and Toshiba CLX). Special low-dose protocols have been developed for each unit with maximum effective dose of 1 mSv (in accordance with the requirements of paragraph 2.2.1, Sanitary Regulations 2.6.1.1192-03). The study involved 5,310 patients (53% men, 47% women) aged 18-92 years (mean age 62 years). Diagnosis verification was carried out in the specialized medical organizations via consultations, additional instrumental, laboratory as well as pathohistological studies. The results were then entered into the “National Cancer Registry”. Results. 5310 patients (53% men, 47% women) aged 18 to 92 years (an average of 62 years) participated in the LDCT-MLCS. The final cohort was comprised of 4762 (89.6%) patients. We have detected 291 (6.1%) Lung-RADS 3 lesions, 228 (4.8%) Lung- RADS 4A lesions and 196 (4.1%) Lung-RADS 4B/4X lesions. All 4B and 4X lesions were routed in accordance with the project's methodology and legislative documents. Malignant neoplasms were verified in 84 cases (1.76% of the cohort). Stage I-II lung cancer was actively detected in 40.3% of these individuals. For the first time in the Russian Federation we have calculated the number needed to screen (NNS) to identify one lung cancer (NNS=57) and to detect one Stage I lung cancer (NNS=207). Conclusions. Based on the global experience and our own practices, we argue that selective LDCT is the most systematic solution to the problem of early-stage lung cancer screening.

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