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Computed tomography (CT), known for its exceptionally high accuracy, is associated with a substantial dose of ionizing radiation. Low-dose protocols have been devised to address this issue; however, a reduction in the radiation dose can lead to a deficiency in the number of photons, resulting in quantum noise. Thus, the aim of this study was to optimize the smoothing parameter (σ-value) of the block matching and 3D filtering (BM3D) algorithm to effectively reduce noise in low-dose chest and abdominal CT images. Acquired images were subsequently analyze using quantitative evaluation metrics, including contrast to noise ratio (CNR), coefficient of variation (CV), and naturalness image quality evaluator (NIQE). Quantitative evaluation results demonstrated that the optimal σ-value for CNR, CV, and NIQE were 0.10, 0.11, and 0.09 in low-dose chest CT images respectively, whereas those in abdominal images were 0.12, 0.11, and 0.09, respectively. The average of the optimal σ-values, which produced the most improved results, was 0.10, considering both visual and quantitative evaluations. In conclusion, we demonstrated that the optimized BM3D algorithm with σ-value is effective for noise reduction in low-dose chest and abdominal CT images indicating its feasibility of in the clinical field., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

This paper presents a new design algorithm for a withdrawal-weighted surface acoustic wave (SAW) transversal filter. The proposed algorithm is based on the effective transmission loss theory and a delta function model of a SAW transversal filter. The design process consists of three steps, which eventually determine eight geometrical design parameters for the filter in order to satisfy given performance specifications. First, the number of fingers in the input and output interdigital transducers (IDTs), plus their geometrical sizes is determined using the insertion loss specification. Second, the number and positions of the polarity reverses in the output IDT are determined using the bandwidth and ripple specifications. Third, the number and position for withdrawing and switching specific fingers in the output IDT and attached electrode area are determined to achieve the desired sidelobe level. The efficiency of the technique is illustrated using a sample design of an IF filter consisting of a uniform input IDT and withdrawal-weighted output IDT. The proposed algorithm is distinct from conventional techniques in that it can optimize the structural geometry of a withdrawal-weighted SAW filter in a direct manner by considering all the performance specifications simultaneously.

The aim of this study was to evaluate the application feasibility of the nonlocal means (NLM) noise reduction algorithm in low-dose computed tomography (CT) images using an advanced modeled iterative reconstruction (ADMIRE) iterative reconstruction technique-based tin filter with various applied parameters. Low-dose CT images were based on high pitch and tin filters and acquired using slices of the aortic arch, the four chambers of the heart, and the end of the heart. Intensities A2 and A3 as well as kernels B40 and B59 were used as the parameters for the ADMIRE technique. The NLM denoising algorithm was modeled based on the principle of weighting between pixels; the contrast-to-noise ratio (CNR), edge rise distance (ERD), and blind/referenceless image spatial quality evaluator (BRISQUE) were used as image quality evaluation parameters. The CNR result was the highest, with an average of 43.51 in three slices when the proposed NLM denoising algorithm was applied to CT images acquired using the ADMIRE intensity 2 and B59 kernel. The ERD results were similar to those obtained using the ADMIRE intensity 2 and B59 kernel in the CT image acquired using the proposed method. In addition, BRISQUE, which can evaluate the overall image quality, showed a similar trend to the ERD results. In conclusion, the NLM noise reduction algorithm is expected to maximize image quality by preserving efficient edge information while improving noise characteristics in low-dose CT examinations. [ABSTRACT FROM AUTHOR]

The resulting image obtained from an X-ray imaging system depends significantly on the characteristics of the detector. In particular, when an X-ray image is acquired by thinning the detector, a relatively large amount of noise inevitably occurs. In addition, when a thick detector is used to reduce noise in X-ray images, blurring increases and the ability to distinguish target areas deteriorates. In this study, we aimed to derive the optimal X-ray image quality by deriving the optimal noise reduction parameters based on the non-local means (NLM) algorithm. The detectors used were of two thicknesses (96 and 140 μm), and images were acquired based on the IEC 62220-1-1:2015 RQA-5 protocol. The optimal parameters were derived by calculating the edge preservation index and signal-to-noise ratio according to the sigma value of the NLM algorithm. As a result, a sigma value of the optimized NLM algorithm (0.01) was derived, and this algorithm was applied to a relatively thin X-ray detector system to obtain appropriate noise level and spatial resolution data. The no-reference-based blind/referenceless image spatial quality evaluator value, which analyzes the overall image quality, was best when using the proposed method. In conclusion, we propose an optimized NLM algorithm based on a new method that can overcome the noise amplification problem in thin X-ray detector systems and is expected to be applied in various photon imaging fields in the future. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The non-local means (NLM) noise reduction algorithm is well known as an excellent technique for removing noise from a magnetic resonance (MR) image to improve the diagnostic accuracy. In this study, we undertook a systematic review to determine the effectiveness of the NLM noise reduction algorithm in MR imaging. A systematic literature search was conducted of three databases of publications dating from January 2000 to March 2020; of the 82 publications reviewed, 25 were included in this study. The subjects were categorized into four major frameworks and analyzed for each research result. Research in NLM noise reduction for MR images has been increasing worldwide; however, it was found to have slightly decreased since 2016. It was found that the NLM technique was most frequently used on brain images taken using the general MR imaging technique; these were most frequently performed during simultaneous real and simulated experimental studies. In particular, comparison parameters were frequently used to evaluate the effectiveness of the algorithm on MR images. The ultimate goal is to provide an accurate method for the diagnosis of disease, and our conclusion is that the NLM noise reduction algorithm is a promising method of achieving this goal. [ABSTRACT FROM AUTHOR]

The confocal laser scanning microscopy (CLSM) system has been widely used to analyze early carious lesions with fluorescent ligands in dental imaging. This system can be used to examine the physiological condition of cellular colonization in the tooth structure. However, the undesirable noise in CLSM images hinders accurate activity assessment of early carious lesions. To address this limitation, a total variation (TV)-based noise reduction algorithm with good edge preservation was developed, and its applicability to medical tooth specimen images obtained with CLSM was verified. To evaluate the imaging performance, the proposed algorithm was compared with conventional filtering methods in terms of the normalized noise power spectrum, contrast-to-noise ratio, and coefficient of variation. The results indicate that the proposed algorithm achieved better noise performance and fine-detail preservation, in comparison with the conventional methods. [ABSTRACT FROM AUTHOR]

The filters used in X-ray systems affect the image quality and radiation dose. Although using filters has advantages in terms of image quality and dose, image noise increases because the amount of X-rays in the energy range of the entire area is reduced. Thus, a newly improved non-local means (INLM) denoising algorithm was modeled and applied to X-ray images based on the thickness of the aluminum (Al) filter, which is most commonly used in X-ray imaging systems, to prove its potential use. For X-ray image acquisition, a high-performance cadmium telluride material-based detector and a tube containing Al filters (1-, 3-, and 5-mm thickness) were designed. The proposed INLM denoising algorithm was modeled by including the improved weights for the gradient information for each pixel in the conventional NLM-based equation. When the proposed INLM denoising algorithm was applied to the acquired photon-counting X-ray images, results showed superior performance for both noise characteristics and no-reference-based image evaluation. In particular, we confirmed that when the INLM was applied to X-ray images using 5 mm Al thickness, noise characteristics and no-reference-based evaluation results were improved by approximately 1.36 times and 1.06 times, respectively, compared to the conventional NLM. The study proved that choosing the INLM in photon-counting X-ray images using 5 mm-thick Al filtration is vital for the success of image processing applications. [ABSTRACT FROM AUTHOR]

The purpose of this study was to investigate the viability of the proposed method in preventing the loss of metallic components including the clip and coil in cerebral computed tomography angiography (CTA). Forty patients undergoing surgery for aneurysms carried metallic materials. The proposed method is based on conventional bone subtraction CTA (BS-CTA) system. Briefly, the position of metal components was determined using the threshold value and a region of interest (ROI). An appropriate threshold was used to separate the background from the target materials based on the Otsu method. A three-dimensional (3D) rendering was performed from the proposed BS-CTA data carrying the extracted target information. The accuracy of clip and coil region measured using the dice similarity coefficient (DSC) and bidirectional Hausdorff distance (HD) is reported. The metallic components of the proposed BS-CTA were significantly visualized in various patient cases. Quantitative evaluation using the proposed method is based on the mean DSC of 0.93 with a standard deviation (SD) of ±0.05 (e.g., maximum value = 0.99, minimum value = 0.75, 95% confidence interval (CI) = 0.91 to 0.95, and all p < 0.05). The mean HD was 1.50 voxels with an SD of ± 0.58 (e.g., maximum value = 5.95, minimum value = 0.12, 95% CI = 1.10 to 1.90, and all p < 0.05). The proposed method demonstrates effective segmentation of the metallic component and application to the existing conventional BS-CTA system. [ABSTRACT FROM AUTHOR]

In order to improve image quality in single-photon emission computed tomography (SPECT)/computed tomography (CT), various studies focusing on reconstruction methods and noise reduction algorithms have been conducted. Therefore, the purpose of this study was to evaluate the efficiency of the filtered back projection (FBP) and ordered subset expectation maximization (OSEM) reconstruction methods, and to evaluate image quality after applying the median modified Wiener filter (MMWF) as a complementary noise reduction filter using 3 × 3 and 5 × 5 mask sizes. The 3D Hoffman brain phantom was used, and the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were evaluated in regions of interest for quantitative analysis. Based on the results, the image quality obtained by the OSEM reconstruction method was 37.7% and 25.9% higher than that of the FBP reconstruction method regarding SNR and CNR, respectively. In addition, the average SNR and CNR values obtained by applying the MMWF algorithm were 35.9% and 17.1% higher than those without the MMWF algorithm. Therefore, our results indicate that the OSEM reconstruction method is more efficient than the FBP reconstruction method, and that the MMWF algorithm improves image quality in SPECT images, regardless of which reconstruction method is used. [ABSTRACT FROM AUTHOR]

Noise reduction is a fundamental early stage in X-ray image performance evaluation. In this study, the median-modified Wiener filter (MMWF) algorithm based on a nonlinear adaptive spatial filter is compared to two general noise-reduction techniques using median and Wiener filters to confirm an excellent denoising approach in X-ray images. To acquire images, a high-resolution complementary metal–oxide–semiconductor (CMOS) radio-magnetic X-ray imaging system (exposure conditions: 100, 400, and 700 μ A) and rat phantom were used. The performances of the denoising methods were evaluated in terms of contrast-to-noise ratio (CNR), coefficient of variation (COV), and no-reference image quality assessment using the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE). The average results for CNR, COV, and BRISQUE in the acquired X-ray image using the MMWF algorithm were 1.08, 1.10, and 1.03 times higher than those of the noisy image, respectively. On average, the MMWF algorithm provided better image restoration than general noise-reduction techniques and was found to be most effective in relatively lower exposure conditions. [ABSTRACT FROM AUTHOR]

Gamma images inherently contain noise distributions owing to the low photon count in a detector. Several methods using semiconductor detectors have been reported to overcome this challenge. Median modified Wiener filter (MMWF) algorithm is one of the effective noise reduction methods, which employs mask size as an essential parameter for optimization. In this study, we evaluated and optimized the acquired gamma image using the proposed MMWF algorithm based on the Monte Carlo simulation tool with various mask sizes (3 × 3, 5 × 5, and 7 × 7) in a cadmium telluride pixelated semiconductor detector. Our results demonstrated that the images obtained with 7 × 7 mask size showed the lowest values in the normalized noise power spectrum. The contrast-to-noise ratio and the coefficient of variation were observed to be 29.2–65.9% better for the images obtained with the 7 × 7 mask size compared with those obtained using other mask sizes. This study suggests that an excellent image quality of gamma images can be achieved with semiconductor detectors by optimizing the mask size in the MMWF algorithm. • Monte Carlo simulation study GATE designed cadmium telluride pixelated semiconductor detector and NEMA IEC body phantom. • Optimization of mask size parameter in median modified Wiener filter (MMWF) algorithm to reduce the noise distribution in the simulated gamma images. • The quantitative evaluation results using normalized noise power spectrum, contrast to noise ratio, and coefficient of variation confirm optimization parameter of gamma image characteristic. [ABSTRACT FROM AUTHOR]

Abstract Chest digital tomosynthesis (CDT) is developed to solve the problem of overlapping and absence of depth information as seen in general X-rays applied in chest radiography and high-exposure dose problem of computed tomography. However, in CDT, noise can reduce the quality of the image and increase the rate of false diagnosis. Thus, noise removal is an important issue in the clinical application of CDT, and a total variation (TV)-based noise reduction algorithm is an excellent algorithm because of its high noise reduction efficiency. In this study, we demonstrate the superiority of the TV noise reduction algorithm by quantitative comparisons of analyses with conventional algorithms. The quantitative evaluation of the image quality, coefficient of variation (COV), contrast-to-noise ratio (CNR), and intensity profile are evaluated. The COV and CNR results in the TV noise reduction algorithm are 1.88 times lower and 1.54 times higher than those of the original image, respectively; the smoothest graph is also obtained in the intensity profile. In conclusion, it is expected that the quality of the CDT image processed using the TV noise reduction algorithm is better than that processed using conventional noise reduction methods and the use of our algorithm will increase in the clinical field in the future. [ABSTRACT FROM AUTHOR]

In this study, we applied the fast non-local means (FNLM) algorithm to high-pitch low-dose computed tomography (CT) images using a tin-filter. This study was conducted via a simulation study using a Shepp-Logan phantom and an experimental approach using American association of physicists in medicine (AAPM) phantom images. To optimize the smoothing factor of the FNLM algorithm, the values were adjusted from 0.01 to 0.05 at intervals of 0.001 and applied to the CT image. The smoothing factor of the optimal FNLM algorithm, derived through various quantitative evaluations, was 0.03. According to the results, when the optimized FNLM algorithm was applied to the acquired CT image with a low-dose protocol based on a high pitch condition, the estimated coefficient of variation, contrast-to-noise ratio, and signal-to-noise ratio were improved by 8.7, 6.7, and 8.0 times, respectively, compared to the general CT scanning condition (none). In addition, compared with the noise filtering methods mainly used in the past, both simulation and real experimental results proved that the application efficiency of the FNLM algorithm was high in the obtained low-dose-based CT images. In summary, the applicability of the FNLM algorithm in CT images obtained under low-dose conditions with high pitch using a tin-filter was demonstrated, and the derived research results are expected to be actively utilized in the diagnostic medical field to help reduce exposure doses and improve image quality. • A denoising algorithm was applied on CT images using tin-filter. • The improvement of image quality of low-dose CT images based on high pitch was studied. • A fast non-local means (FNLM) algorithm was optimized for CT images. • The experiment was conducted using simulation tool and computed tomography. • The application of FNLM algorithm can improve the image performances. [ABSTRACT FROM AUTHOR]

Gamma imaging of the brain in nuclear medicine usually contains a noise distribution owing to attenuation and scattering effects. This study aimed at improving the quality of the image of the brain acquired by single photon emission computed tomography (SPECT) by applying the fast non-local means (FNLM) algorithm, which an excellent noise reduction technique, using a Hoffman 3D brain phantom filled with 99mTc. Gaussian noise was added to the acquired image using MATLAB and the image quality evaluated using conventional noise reduction filters and the proposed FNLM algorithm. The contrast to noise ratio (CNR), peak signal to noise ratio, and root mean square error were used for quantitative evaluation. Comparing the image qualities obtained using various filters and algorithms, the CNR results of the images with Gaussian and median filters, and FNLM algorithm were found to surpass those of the noise image by 1.06, 1.22, and 1.41 times, respectively. In addition, similarity analysis indicated that the image evaluated using the FNLM algorithm showed excellent quality. Thus, our results demonstrated that the FNLM algorithm provided the greatest improvement in image quality based on quantitative evaluation results in the SPECT system. [ABSTRACT FROM AUTHOR]