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During Computed Tomography (CT) scans of the Thorax-Abdomen-Pelvis (TAP) the patient's arms should be positioned above the head to obtain optimal image quality and expose the patient to the lowest possible radiation dose. This may be impossible with patients with shoulder problems leading to arms being positioned in other ways. This study aimed to investigate differences in objective image quality and estimated effective dose (E), when positioning the arms below shoulder level in CT-TAP. An anthropomorphic phantom with cadaver arms was used. Four arm positions were tested: Along the torso (A), on the pelvis (B), on a pillow on the pelvis (C), and one arm on pillow on the pelvis and the other arm on the pelvis (D). A Siemens SOMATOM Definition Flash CT-scanner with CareDose 4D was used. The dose length product was read to estimate E. Image quality was assessed objectively by measuring noise within the region of interest in the liver and urinary bladder. Significant differences in E between all arm positions were seen (p = 0.005). The lowest E was obtained in position C, reducing E by 8.42%. Position A provided the best image quality but the highest E. This study showed no unequivocal optimal positioning of arms in CT-TAP. Position A provided the best object image quality, while position C yielded the lowest E. These results may impact the planning of diagnostic CT where positioning of arms may influence optimal image quality and radiation dose. This study illustrates tendencies for objective image quality and E when arms are positioned below shoulder level. Further research is needed to assess the clinical relevance with the diagnostic potential. [ABSTRACT FROM AUTHOR]

Abstract Objective To compare the image quality of normal anatomical structures and radiation dose on low-dose (LDCT) and standard-dose (SDCT) temporal bone CT in children. Methods The study included 45 LDCT (80 kV and 130 mAs) and 45 SDCT (120 kV and 170 mAs) scans in children, 1–15 years of age. LDCT and SDCT scans were analyzed on H60s and H70h reconstruction kernels, respectively. Two readers assessed the image quality for 25 anatomical structures, using a 5-point scale. A score of 3 and above was considered “sufficient” and 2 and below was considered “insufficient” image quality. Image noise, contrast, age and size-specific effective doses were calculated. Results Despite an increase in image noise on LDCT, image quality remained sufficient for most structures owing to increased image contrast. The median effective dose on LDCT, calculated with age-specific conversion factor, decreased by 72.9% and that calculated with size-specific conversion factor decreased by 81.8% compared to the dose on SDCT. Conclusion LDCT provides comparable image quality for evaluation of temporal bone with significant reduction in radiation dose in children.

Contrast resolution is an important index for evaluating the signal detectability of computed tomographic (CT) images. Recently, various noise reduction algorithms, such as iterative reconstruction (IR) and deep learning reconstruction (DLR), have been proposed to reduce the image noise in CT images. However, these algorithms cause changes in the image noise texture and blurred image signals in CT images. Furthermore, the contrast-to-noise ratio (CNR) cannot be accurately evaluated in CT images reconstructed using noise reduction methods. Therefore, in this study, we devised a new method, namely, "effective CNR analysis," for evaluating the contrast resolution of CT images. We verified whether the proposed algorithm could evaluate the effective contrast resolution based on the signal detectability of CT images. The findings showed that the effective CNR values obtained using the proposed method correlated well with the subjective visual impressions of CT images. To investigate whether signal detectability was appropriately evaluated using effective CNR analysis, the conventional CNR analysis method was compared with the proposed method. The CNRs of the IR and DLR images calculated using conventional CNR analysis were 13.2 and 10.7, respectively. By contrast, those calculated using the effective CNR analysis were estimated to be 0.7 and 1.1, respectively. Considering that the signal visibility of DLR images was superior to that of IR images, our proposed effective CNR analysis was shown to be appropriate for evaluating the contrast resolution of CT images. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the operation principles of the automatic tube current modulation (ATCM) of a modern GE healthcare CT scanner, and the impact of related settings on image quality and patient dose. Material & Methods: A dedicated phantom (Mercury 4.0) was scanned using two of the most frequently used clinical scanning protocols (chest and abdomen‐pelvis). The preset protocol settings were used as starting points (reference conditions). Scan direction, scan mode (helical vs. axial), total beam width, tube potential (kVp), and ATCM settings were then modified individually to understand their impact on radiation dose and image quality. Regarding the ATCM settings, the SmartmA minimum and maximum mA limits, and the noise index (NI) values were varied. As surrogates of patient dose, the CTDIvol and DLP values of each scan were used. As surrogates of image quality were used the image noise and the detectability index (d') of five different materials (air, solid water, polystyrene, iodine, and bone) embedded in the Mercury phantom calculated with the ImQuest software. Results: The scanning direction did not have any effect on ATCM curves, unlike what has been observed in CT scanners from other manufacturers. Total beam width does matter, however, the SmartmA limit settings and kVp selection had the greatest impact on image quality and dose. It was seen that improper minimum mA limit settings practically invalidated the ATCM operation. In contrast, when full modulation was allowed without restrictions, noise standard deviation, and detectability index became much more consistent across the wide range of phantom diameters. For lower kVp settings an impressive dose reduction was observed that requires further investigation. Conclusion: SmartmA is a tool that if not properly used may increase the patient doses considerably. Therefore, its settings should be carefully adjusted for each preset different clinical protocol. [ABSTRACT FROM AUTHOR]

Prior research on vegetation indices (VIs) to estimate species diversity in forest and grassland ecosystems has shown limitations when applied to wetland ecosystems due to their complex structure. Consequently, the predictive capacity of various VIs for wetland plant species diversity and their susceptibility to image noise remain largely unknown. To address these gaps, we utilized high-resolution multispectral images from Unmanned Aerial Vehicles (UAV) and field survey data in marsh wetlands. Various VIs and species diversity indices were computed, and univariate and multiple linear regression models were employed to assess predictive ability of the mean, standard deviation, and coefficient of variation of VIs for wetland plant species diversity. Results revealed that MTCI and NDREI exhibited the highest predictive ability for plant species diversity both before and after masking image noise. While most VIs generally improved in predictive ability after masking image noise, their susceptibility to it varied. MTCI, NDREI, SAVI, MSAVI, and MSAVI2 were less affected by image noise, with minimal changes in predictive ability before and after masking image noise. Conversely, CTVI, NDVI, and other VIs showed high susceptibility to image noise, with significant improvement in predictive ability after masking. ANOVA results indicated that integrating the mean and standard deviation of VIs into models significantly enhanced estimates of species diversity, highlighting their complementarity in predicting species diversity. These findings provide valuable insights into the predictive ability of different VIs for wetland plant species diversity, guiding selection of optimal VIs for predicting plant species diversity at broader scales in the future.

Today, the development of tools for analyzing the spatio-temporal properties of optical radiation is a hot topic because of the increased interest in multimode optical technologies. The determination of mode composition and of intermode phases of optical beams is particularly important in the context of studies of new nonlinear effects in multimode waveguides. However, the mode decomposition methods being developed have some limitations that affect their accuracy. This paper is devoted to the study of the impact of recording camera noise in the process of mode decomposition of optical radiation by the correlation filtering method. A significant impact of intensity noise on determining the position of the center of correlation response, a key step in mode decomposition. In order to improve the accuracy of the method, it is proposed to use the Jensen-Shannon metric that allows to effectively determine the center of correlation response of noisy signal. The proposed approach is resistant to a noise level of around 7%. [ABSTRACT FROM AUTHOR]

This exploratory retrospective study aims to quantitatively compare the image quality of unenhanced brain computed tomography (CT) reconstructed with an iterative (AIDR-3D) and a deep learning-based (AiCE) reconstruction algorithm. After a preliminary phantom study, AIDR-3D and AiCE reconstructions (0.5 mm thickness) of 100 consecutive brain CTs acquired in the emergency setting on the same 320-detector row CT scanner were retrospectively analyzed, calculating image noise reduction attributable to the AiCE algorithm, artifact indexes in the posterior cranial fossa, and contrast-to-noise ratios (CNRs) at the cortical and thalamic levels. In the phantom study, the spatial resolution of the two datasets proved to be comparable; conversely, AIDR-3D reconstructions showed a broader noise pattern. In the human study, median image noise was lower with AiCE compared to AIDR-3D (4.7 vs. 5.3, p < 0.001, median 19.6% noise reduction), whereas AIDR-3D yielded a lower artifact index than AiCE (7.5 vs. 8.4, p < 0.001). AiCE also showed higher median CNRs at the cortical (2.5 vs. 1.8, p < 0.001) and thalamic levels (2.8 vs. 1.7, p < 0.001). These results highlight how image quality improvements granted by deep learning-based (AiCE) and iterative (AIDR-3D) image reconstruction algorithms vary according to different brain areas. [ABSTRACT FROM AUTHOR]

This paper first classifies the image noise and evaluates the image quality by means of the correlation function, mean square error value, and fidelity. Secondly, an image adaptive threshold denoising system based on wavelet transform is constructed, and the image processing is realized by using the wavelet transform principle and the selection of threshold value. Finally, the image is optimized by using the modulo-maximum denoising method and the threshold denoising method for empirical analysis. The results show that the signal-to-noise ratio of the noisy signal is 6.2315dB, and the signal-to-noise ratio of the modulo-maximum processing is 12.7024 dB. The peak signal-to-noise ratio of the noisy image is 20.1258dB, the peak signal-to-noise ratio of the soft threshold denoising method is 26.4831dB, and the peak signal-to-noise ratio of the hard threshold denoising method is 22.5864dB. This shows that the wavelet transform and adaptive thresholding can effectively denoise and ensure image quality. Image quality.

With the advancement of artificial intelligence and deep neural networks, the ease of image generation and editing has increased significantly.Consequently, the occurrence of malicious tampering and forgery using image generation tools is on the rise, posing a significant threat to multimedia security and social stability.Therefore, it is crucial to research detection methods for forged faces.Face tampering and forgery can occur through various means and tools, leaving different levels of forgery traces during the tampering process.These traces can be partly reflected in the image noise.From the perspective of image noise, the noise components reflecting tampering traces of forged faces were extracted through a noise removal module.Furthermore, noise attention was generated to guide the backbone network in the detection of forged faces.The training of the noise removal module was supervised using SRM filters.In order to strengthen the guidance of the noise removal module, the noise obtained by the noise removal module was added back to the real face image, forming a pair of supervised training samples in a self-supervised manner.The experimental results illustrate that the noise features obtained by the noise removal module have a good degree of discrimination.Experiments were also conducted on several public datasets, and the proposed method achieves an accuracy of 98.32% on the Celeb-DF dataset, 92.61% on the DFDC dataset, and more than 94% on the FaceForensics++ dataset, thus proving the effectiveness of the proposed method.

This exploratory retrospective study aims to quantitatively compare the image quality of unenhanced brain computed tomography (CT) reconstructed with an iterative (AIDR-3D) and a deep learning-based (AiCE) reconstruction algorithm. After a preliminary phantom study, AIDR-3D and AiCE reconstructions (0.5 mm thickness) of 100 consecutive brain CTs acquired in the emergency setting on the same 320-detector row CT scanner were retrospectively analyzed, calculating image noise reduction attributable to the AiCE algorithm, artifact indexes in the posterior cranial fossa, and contrast-to-noise ratios (CNRs) at the cortical and thalamic levels. In the phantom study, the spatial resolution of the two datasets proved to be comparable; conversely, AIDR-3D reconstructions showed a broader noise pattern. In the human study, median image noise was lower with AiCE compared to AIDR-3D (4.7 vs. 5.3, p < 0.001, median 19.6% noise reduction), whereas AIDR-3D yielded a lower artifact index than AiCE (7.5 vs. 8.4, p < 0.001). AiCE also showed higher median CNRs at the cortical (2.5 vs. 1.8, p < 0.001) and thalamic levels (2.8 vs. 1.7, p < 0.001). These results highlight how image quality improvements granted by deep learning-based (AiCE) and iterative (AIDR-3D) image reconstruction algorithms vary according to different brain areas.

Clear and reliable visual information is the premise and basis of work for nuclear robots. However, the ubiquitous γ rays in the nuclear environment will produce radiation effects on CMOS cameras and bring in complex visual noise. In this paper, combining the mechanism and characteristics of γ radiation noise, a method for restoring γ-radiation scene images based on spatial axial gradient discrimination is proposed. Firstly, interframe difference is used to determine the position of radiated noise on the image. Secondly, the gray gradients of different axes at noise pixels are calculated, and two axes with lager gray gradients are selected. Then, the adaptive medians are selected on the two axes, respectively and are weighted according to the gradient as the new value of the noise pixel. Finally, the Wallis sharpening filter is applied to enhance the detailed information and deblur the image. Plenty of experiments have been carried out on images collected in real γ radiation scenes, and image quality has been significantly improved, with Peak Signal to Noise ratio (PSNR) reaching 30.587 dB and Structural Similarity Index Mean (SSIM) reaching 0.82. It is obvious that this method has advanced performance in improving the quality of γ-radiation images. It can provide method guidance and technical support for the software module design of the anti-nuclear radiation camera. [ABSTRACT FROM AUTHOR]

Background: Whereas filtered back projection algorithms for voxel‐based CT image reconstruction have noise properties defined by the filter, iterative algorithms must stop at some point in their convergence and do not necessarily produce consistent noise properties for images with different degrees of heterogeneity. Purpose: A least‐squares iterative algorithm for proton CT (pCT) image reconstruction converges toward a unique solution for relative stopping power (RSP) that optimally fits the protons. We present a stopping criterion that delivers solutions with the property that correlations of RSP noise between voxels are relatively low. This provides a method to produce pCT images with consistent noise properties useful for proton therapy treatment planning, which relies on summing RSP along lines of voxels. Consistent noise properties will also be useful for future studies of image quality using metrics such as contrast to noise ratio, and to compare RSP noise and dose of pCT with other modalities such as dual‐energy CT. Methods: With simulated and real images with varying heterogeneity from a prototype clinical proton imaging system, we calculate average RSP correlations between voxel pairs in uniform regions‐of‐interest versus distance between voxels. We define a parameter r, the remaining distance to the unique solution relative to estimated RSP noise, and our stopping criterion is based on r falling below a chosen value. Results: We find large correlations between voxels for larger values of r, and anticorrelations for smaller values. For r in the range of 0.5–1, voxels are relatively uncorrelated, and compared to smaller values of r have lower noise with only slight loss of spatial resolution. Conclusions: Iterative algorithms not using a specific metric or rationale for stopping iterations may produce images with an unknown and arbitrary level of convergence or smoothing. We resolve this issue by stopping iterations of a least‐squares iterative algorithm when r reaches the range of 0.5–1. This defines a pCT image reconstruction method with consistent statistical properties optimal for clinical use, including for treatment planning with pCT images. [ABSTRACT FROM AUTHOR]

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Background: Deep learning technology has been widely applied to medical image analysis. But due to the limitations of its own imaging principle, ultrasound image has the disadvantages of low resolution and high Speckle Noise density, which not only hinder the diagnosis of patients' conditions but also affect the extraction of ultrasound image features by computer technology. Objective: In this study, we investigate the robustness of deep convolutional neural network (CNN) for classification, segmentation, and target detection of breast ultrasound image through random Salt & Pepper Noise and Gaussian Noise. Methods: We trained and validated 9 CNN architectures in 8617 breast ultrasound images, but tested the models with noisy test set. Then, we trained and validated 9 CNN architectures with different levels of noise in these breast ultrasound images, and tested the models with noisy test set. Diseases of each breast ultrasound image in our dataset were annotated and voted by three sonographers based on their malignancy suspiciousness. we use evaluation indexes to evaluate the robustness of the neural network algorithm respectively. Results: There is a moderate to high impact (The accuracy of the model decreased by about 5%-40%) on model accuracy when Salt and Pepper Noise, Speckle Noise, or Gaussian Noise is introduced to the images respectively. Consequently, DenseNet, UNet++ and Yolov5 were selected as the most robust model based on the selected index. When any two of these three kinds of noise are introduced into the image at the same time, the accuracy of the model will be greatly affected. Conclusions: Our experimental results reveal new insights: The variation trend of accuracy with the noise level in Each network used for classification tasks and object detection tasks has some unique characteristics. This finding provides us with a method to reveal the black-box architecture of computer-aided diagnosis (CAD) systems. On the other hand, the purpose of this study is to explore the impact of adding noise directly to the image on the performance of neural networks, which is different from the existing articles on robustness in the field of medical image processing. Consequently, it provides a new way to evaluate the robustness of CAD systems in the future. [ABSTRACT FROM AUTHOR]

High-quality and detailed CT scan images are crucial for accurate diagnosis. Factors such as image noise and slice thickness affect image quality. This study aimed to determine the optimal slice thickness that minimized image noise while maintaining sufficient diagnostic information using the single-source computed tomography head protocol. Single-source CT images were examined using the Linux Operating system Ge Revolution 64-slice CT scanner, and a combination of statical analysis and DICOM CT image analysis was employed. The single-source energy head CT protocol was used to investigate the effect of slice thickness on noise and visibility in images. Different values of slice thickness 0.625, 1.25, 2.5, 3.75, 5, 7.5, and 10 were prepared, and then quantitative analysis was performed. Thinner slice thickness decreased image noise, increased visibility, and improved detection. Therefore, the balance between changing the thickness of the slice with the diagnostic content and image noise must be considered. Maximum slice thickness enhances CT image detail and structure despite more noise. Based on the results, a slice thickness of 1.25mm was identified as the optimal choice for reducing image noise and achieving better and more accurate detection using the single-source computed tomography head protocol. The study revealed that image noise tends to increase with greater slice thickness according to the Linux operating system. These findings can serve as a valuable guide for quality control methods in CT centers, emphasizing the need to determine the appropriate slice thickness to ensure an accurate diagnosis. [ABSTRACT FROM AUTHOR]

Deep convolutional networks are vulnerable to image translation or shift, partly due to common down-sampling layers, e.g., max-pooling and strided convolution. These operations violate the Nyquist sampling rate and cause aliasing. The textbook solution is low-pass filtering (blurring) before down-sampling, which can benefit deep networks as well. Even so, non-linearity units, such as ReLU, often re-introduce the problem, suggesting that blurring alone may not suffice. In this work, first, we analyse deep features with Fourier transform and show that Depth Adaptive Blurring is more effective, as opposed to monotonic blurring. To this end, we propose a novel Depth Adaptive Blur-pool (DAB-pool) module to replace existing down-sampling methods. Second, we introduce a novel activation function – with a built-in low pass filter, as an additional measure, to keep the problem from reappearing. From experiments, we observe generalisation on other forms of transformations and corruptions as well, e.g., rotation, scale, and noise. We evaluate our method under three challenging settings: (1) a variety of image translations; (2) adversarial attacks – both ℓ p bounded and unbounded; and (3) data corruptions and perturbations. In each setting, our method achieves state-of-the-art results and improves clean accuracy on various benchmark datasets. [ABSTRACT FROM AUTHOR]

The statistical properties in various medical images demonstrate uncorrelated noise fluctuations. The signal noise fluctuations are generally due to physical imaging processes and have nothing to do with the tissue textures. Adding the noise types (e.g., quantization, electronics, photon) usually degrade medical images. The noise variation is usually assumed to be additive with zero‐mean, constant variance Gaussian distribution. However, close consideration of different medical images indicates the need for better model representation to minimise the noise that can be vital in decision‐making. This research proposed a probabilistic method to represent all real‐type noise in general medical images. The method aims to cover most classical statistical models such as Gaussian, lognormal, Rayleigh, Weibull, and Nakagami without a prior examination to test for fitness. The proposed model was applied to actual clinical images to test the performance of the noise originating from the physical processes. The noise is assumed to be additive white Gaussian type with a zero mean and constant variance. The theoretical literature indicates that a nonlinear function can better represent noise. This research helps to form a relationship between the image intensity and the noise variance that yields the fitting parameters in the introduced nonlinear function. The validity of the proposed method was proved mathematically and tested using the well know Kolmogorov–Smirnov (K‐S) and Akaike Information Criteria (AIC) tests. The method was successfully applied to various clinical images such as magnetic resonance, x‐ray, and panoramic images. The model's performance is compared with the classical models using root mean squared error (RMSE), relative error (RE), and R2 as the evaluation matrices. The presented model has outperformed all classic models. [ABSTRACT FROM AUTHOR]

This study evaluated the effect of pitch on 256-slice helical computed tomography (CT) scans. Cylindrical water phantoms (CWP) were measured using axial and helical scans with various pitch values. The surface dose distributions of CWP were measured, and reconstructed images were obtained using filtered back-projection (FBP) and iterative model reconstruction (IMR). The image noise in each reconstructed image was decomposed into a baseline component and another component that varied along the z-axis. The baseline component of the image noise was highest at the center of the reconstructed image and decreased toward the edges. The normalized 2D power spectra for each pitch were almost identically distributed. Furthermore, the ratios of the 2D power spectra for IMR and FBP at different pitch values were obtained. The magnitudes of the components varying along the z-axis were smallest at the center of the reconstructed image and increased toward the edge. The ratios of the 3D power spectra on the fx axis for IMR and FBP at different pitch values were obtained. The results showed that the effect of the pitch was related to the component that varied along the z-axis. Furthermore, the pitch had a smaller effect on IMR than on FBP. [ABSTRACT FROM AUTHOR]

To provide our oncology-specific adult abdominal-pelvic CT reference levels for image noise and radiation dose from a high-volume, oncologic, tertiary referral center. The portal venous phase abdomen-pelvis acquisition was assessed for image noise and radiation dose in 13,320 contrast-enhanced CT examinations. Patient size (effective diameter) and radiation dose (CTDI vol) were recorded using a commercial software system, and image noise (Global Noise metric) was quantified using a custom processing system. The reference level and range for dose and noise were calculated for the full dataset, and for examinations grouped by CT scanner model. Dose and noise reference levels were also calculated for exams grouped by five different patient size categories. The noise reference level was 11.25 HU with a reference range of 10.25–12.25 HU. The dose reference level at a median effective diameter of 30.7 cm was 26.7 mGy with a reference range of 19.6–37.0 mGy. Dose increased with patient size; however, image noise remained approximately constant within the noise reference range. The doses were 2.1–2.5 times than the doses in the ACR DIR registry for corresponding patient sizes. The image noise was 0.63–0.75 times the previously published reference level in abdominal-pelvic CT examinations. Our oncology-specific abdominal-pelvic CT dose reference levels are higher than in the ACR dose index registry and our oncology-specific image noise reference levels are lower than previously proposed image noise reference levels. This study reports reference image noise and radiation dose levels appropriate for the indication of abdomen-pelvis CT examination for cancer diagnosis and staging. The difference in these reference levels from non-oncology-specific CT examinations highlight a need for indication-specific, dose index and image quality reference registries. • Oncology-specific adult abdominal-pelvic CT image noise reference levels are lower than previously reported reference levels. • Oncology-specific adult abdominal-pelvic CT dose reference levels are higher than those in the ACR dose index registry. • There is a need for indication-specific, multi-center dose index and image quality reference level registries. [ABSTRACT FROM AUTHOR]

The noise of the image will lead to large errors in the image processing of high-voltage insulator damage. In order to reduce the impact of noise, a denoising method for high-voltage insulator damage image is designed based on U-Net neural network. The specific method is to define the perception features of the real domain of the HV insulator damage image, to obtain the input and output values in the convolution layer, to construct the excitation function of the convolution layer, to calculate the average value in the selected region, and to obtain the network image feature mapping results. Based on this, displacement changes were acquired by calculating the mean and variance of image data, and multi-scale image features were extracted combined with U-Net neural network. Thus, the image denoising model of high voltage insulator damage is established. Through the continuous optimization algorithm, the value of loss function reaches the predetermined standard, and the denoising of high voltage insulator damage image is finally realized. The experimental results show that as the noise level increases, the peak signal-to-noise(PSNR) ratio obtained by this denoising method shows a decreasing trend. When the noise level is 5 dB and 75 dB, the PSNR values of this denoising method on the color dataset are 27.6 dB and 26.8 dB, respectively. In the grayscale dataset, when the noise level is 75 dB, the PSNR value is 26.9 dB. From the experimental data, it can be seen that the proposed U-Net neural network denoising method is better. [ABSTRACT FROM AUTHOR]

Abstract Introduction Reducing tube voltage is an effective dose saving method in computed tomography (CT) assuming tube current is not concurrently increased. Recent innovations in scanner technology now enable CT tube voltage reduction to 70 kV thereby increasing opportunities for dose reduction in paediatric patients, but it is unclear if the increased image noise associated with 70 kV impacts on ability to visualise renal stones accurately. The purpose was to assess detectability of nephrolithiasis using a bespoke paediatric phantom and low kV, non‐contrast CT and to assess inter‐observer agreement. Methods Forty‐two renal stones of different size and chemical composition were inserted into porcine kidneys and positioned in a bespoke, water‐filled phantom mimicking a 9‐year‐old child weighing approximately 33kg. The phantom was scanned using 120 and 70 kV CT protocols, and the detectability of the stones was assessed by three radiologists. Absolute agreement and Fleiss’ kappa regarding detectability were assessed. Results The mean diameter of renal stones as measured physically was 4.24 mm ranging from 1 to 11 mm. Four stones were missed by at least one observer. One observer had a sensitivity of 93 and 95% at 70 and 120 kV, respectively, while the sensitivity for observers 2 and 3 was 98% at both kV levels. Specificity was 100% across readers and kV levels. Absolute agreement between the readers at 70 kV was 92% (kappa = 0.86) and 98% (kappa = 0.96) at 120 kV indicating a strong agreement at both kV levels. Conclusions The results suggest that lowering the kV does not affect the detection rate of renal stones and may be a useful dose reduction strategy for assessment of nephrolithiasis in children.

Purpose: To investigate the operation principles of the automatic tube current modulation (ATCM) of a CT scanner, using a dedicated phantom and the CT dosimetry index (CTDI) phantom. Material and methods: The Mercury 4.0 phantom and three different configurations of the CTDI dosimetry phantom were employed. A frequently used clinical scanning protocol was employed as a basis for the acquisitions performed with all phantoms, using both scanning directions. Additional acquisitions with different pitch and examination protocols were performed with Mercury phantom, to further explore their effect on ATCM and the resulting image quality. Different software named DICOM Info Extractor, ImageJ, and imQuest, were used to derive CTDIvol and table position, image noise, and water equivalent diameter (WED) of each phantom CT image, respectively. ImQuest was also used to derive the detectability index (d') of five different materials (air, solid water, polystyrene, iodine, and bone) embedded in the Mercury phantom. Results: It was exhibited with all four phantoms that the scanning direction greatly affects the modulation curves. The fitting of the dose modulations curves suggested that for each table position what determines the CTDIvol value is the WED values of the phantom structures laying ahead towards the scanning direction, for a length equal to the effective width of the X‐ray beam. Furthermore, it was also exhibited that ATCM does not fully compensate for larger thicknesses, since images of larger WED phantom sections present more noise (larger SD) in all four phantoms and in Mercury 4.0 phantom smaller detectability (d'). Conclusion: Mercury 4.0 is a dedicated phantom for a complete and in‐depth evaluation of the ATCM operation and the resulting image quality. However, in its absence, different CTDI configurations can be used as an alternative to investigate and comprehend some basic operation principles of the CT scanners' ATCM systems. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: This study investigates the dose burden of photon-counting detector (PCD) lung CT with ultra-high-resolution (UHR) and standard mode using organ-based tube current modulation (OBTCM)., Materials and Methods: An anthropomorphic Alderson-Rando phantom was scanned in UHR and standard mode with and without OBTCM on three dose levels (IQ 5, 20, 50). Effective radiation dose was determined by thermoluminescent dosimetry in 13 measurement sites and compared with the calculated effective dose derived from the dose-length product. Image quality was evaluated subjectively by six radiologists using an equidistant 7-point scale and objectively by means of modulation transfer function analysis., Results: Measured effective radiation exposure was lower in UHR and OBTCM studies than in standard mode (IQ 5: 0.34-0.36, IQ 20: 1.57-1.70, IQ 50: 3.76-3.99 mSv). Compared with the calculated effective dose, the radiation exposure measured with thermoluminescence dosimetry was 131-170% higher. Noise in UHR mode was rated lower than in standard (all p ≤ 0.042) and OBTCM images (all p ≤ 0.028) for all dose levels, while image sharpness was deemed highest for UHR protocols (all p ≤ 0.042). The use of OBTCM had no significant effect on either dimension of subjective image quality (all p ≥ 0.999). Modulation transfer function analysis confirmed the highest spatial frequency in UHR datasets (all p ≤ 0.016)., Conclusion: In PCD-CT of the lung, full field-of-view UHR imaging entails no dose disadvantage over standard mode despite superior image quality. OBTCM possesses moderate dose saving potential. Thermoluminescence dosimetry yielded considerably higher effective doses than those calculated from dose-length products., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Jan-Peter Grunz [grant number Z-3BC/02] and Philipp Gruschwitz [Z-02CSP/18] are financially supported by the Interdisciplinary Center of Clinical Research Würzburg. Andreas Kunz, Thorsten Bley, and Jan-Peter Grunz report a relationship with Siemens Healthineers that includes consulting or advisory and paid expert testimony. The Department of Diagnostic and Interventional Radiology receives ongoing research funding by Siemens Healthineers outside of the presented work., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Image denoising remains a classic and crucial issue in the field of image processing, significantly impacting the outcomes of subsequent image processing tasks. For instance, the denoising network depends on "noise-clean" image pairs to train network effectively. However, it is often hampered by issues such as useful information loss, low training efficiency, and poor blind denoising. To address these challenges, this study proposes a novel image denoising network that integrates the complementary strengths of model-based and learning-based approaches, specifically leveraging the capabilities of both SDDW-GAN and CHRNet. Firstly, SDDW-GAN is designed to estimate the noise distribution on the input noisy images, and a fast-smoothing noisy block sampling algorithm is proposed to extract the noise blocks in noisy images in SDDW-GAN. Secondly, a network with dual generators and dual discriminators based on W-GAN is designed to estimate the noise distribution on the input noisy images and generate noise sample pairs with the same noise distribution, which solves the problem of relying on "noise-clean" image pairs. Thirdly, CHRNet is designed to compute the mapping relationship between the double-noise samples and the single-noise samples. In order to further improve the denoising effect, the dual-channel residual attention module is proposed for fusion learning of global and local features. Experimental results show that the proposed method has a better denoising effect in complex environments and outperforms existing denoising methods. Specifically, in comparison with the stand-alone denoising methods BM3D, DnCNN, Noise2Noise, and Blind2Unblind, the proposed method improves the average peak signal-to-noise ratio (PSNR) by 0.23 dB to 0.78 dB on two benchmarking datasets crossing different noise levels. Its denoising effect is also greater than other competitive stand-alone and combination methods. The proposed method can also extend to low-light image enhancement, deblurring, and super-resolution. [ABSTRACT FROM AUTHOR]

This study evaluated the effect of pitch on 256-slice helical computed tomography (CT) scans. Cylindrical water phantoms (CWP) were measured using axial and helical scans with various pitch values. The surface dose distributions of CWP were measured, and reconstructed images were obtained using filtered back-projection (FBP) and iterative model reconstruction (IMR). The image noise in each reconstructed image was decomposed into a baseline component and another component that varied along the z-axis. The baseline component of the image noise was highest at the center of the reconstructed image and decreased toward the edges. The normalized 2D power spectra for each pitch were almost identically distributed. Furthermore, the ratios of the 2D power spectra for IMR and FBP at different pitch values were obtained. The magnitudes of the components varying along the z-axis were smallest at the center of the reconstructed image and increased toward the edge. The ratios of the 3D power spectra on the fx axis for IMR and FBP at different pitch values were obtained. The results showed that the effect of the pitch was related to the component that varied along the z-axis. Furthermore, the pitch had a smaller effect on IMR than on FBP.

The image segmentation is a process of segregating image into manageable partitions or segments. Image segmentation involves segmentation of foreground from background, based on similarities in features, color or shape. Segmentation is the foremost step in the scrutiny of the printed circuit boards. The segmentation technique basically partitions the PCB into small chunks containing primeval PCB sub-patterns. The conventional threshold segmentation technique considers the effect of image noise and grayscale in the threshold segmentation operation of the PCB (printed circuit board). In this paper, we have tried to overcome the problem of conventional segmentation for PCB such as grayscale overlapping and noise related issues in the segmented images. The basic formula of the optimal threshold operation is given to the threshold segmentation algorithm, the triangular image grid model is designed, the finite difference discrete method is exploited, the optimal threshold calculation formula is devised, and an attempt is made to get the best threshold for segmentation of PCB. Through experimental analysis for the PCB circuit image, two-dimensional OSTU threshold segmentation method, two-dimensional Fisher threshold segmentation method and the proposed research method are used to perform image grid threshold segmentation and to evaluate the performance of the proposed and the existing methods. The experimental results show that the threshold segmentation values obtained by the finite difference based threshold method has low image-noise effect, the gray scale overlap is also minimized, and segmentation accuracy is more as compare to other benchmarked threshold methods. [ABSTRACT FROM AUTHOR]

Digital Image Denoising (noise removal) is one of the fundamental steps taken for the restoration of an actual/true image from its corresponding contaminated version. Image quality and reliability are vital processes that aid investigations, decisions, and judgments across a wide range of disciplines concerning various application domains like a diagnosis in medicine, digital evidence in multimedia forensics, and court of law, among many others. In the last few decades, it is observed that researchers in this field of Image Processing and Computer Vision adapted the traditional methods or approaches to the removal of noise from images. In recent times, advances in artificial intelligence have led to the adoption and popularity of deep learning methods. The Wasserstein Generative Adversarial Network (WGAN), is one of such popular and better approaches that are used. However, the problem with GAN is that after denoising an image, it introduces another variant of noise that was not originally contained in the given (contaminated) image. Against this backdrop, this study, proffer a solution to image denoising, based on the least-squares generative adversarial networks (LSGAN), using a two-step framework. A Generator model using the SRResnet framework was trained to predict the noise distribution over the input noisy images to ease the vanishing gradient and loss saturation. The Least Squares Method was adapted as the loss function for the discriminator model. From the results of the study, it was discovered that the proposed model showed improved PSNR values when compared with the existing models.; the top three results (from the ten test images for the study) for the proposed model against the existing model gave a PSNR value of 34.40 dB against 32.50dB for the baby image, a PSNR value of 33.73 dB against 31.12dB for the woman image, and a PSNR value of 32.54 dB against 29.10dB for the Zebraimage. Hence, the proposed model successfully improved the quality of images affected by noise without the introduction of any other variant of noise artifact. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In digital volume correlation (DVC), random image noise in volumetric images leads to increased systematic error and random error in the displacements measured by subvoxel registration algorithms. Previous studies in DIC have shown that adopting low-pass pre-filtering to the images prior to the correlation analysis can effectively mitigate the systematic error associated with the classical forward additive Newton-Raphson (FA-NR) algorithm. However, the effect of low-pass pre-filtering on the state-of-the-art inverse compositional Gauss-Newton (ICGN) algorithm has not been investigated so far. In this work, we focus on the effect of low-pass pre-filtering on two mainstream subvoxel registration algorithms (i.e., 3D FA-NR algorithm and 3D IC-GN algorithm) used in DVC. Basic principles and theoretical error analyses of the two algorithms are described first. Then, based on numerical experiments with precisely controlled subvoxel displacements and noise levels, the influences of image noise on the displacements measured by two subvoxel algorithms are examined. Further, the effects of low-pass pre-filtering on these two subvoxel algorithms are examined for simulated image sets with different noise levels and deformation modes. The results show that the low-pass pre-filtering can effectively suppress the systematic errors for the 3D FA-NR algorithm, which is consistent with the previously drawn conclusion in DIC. On the contrary, different form the 3D FA-NR algorithm, the 3D IC-GN algorithm itself can reduce the influence of image noise, and the effect of low-pass pre-filtering on it is not so obvious as on 3D FA-NR algorithm.

To explore the value of Computed Tomography (CT) three-dimensional imaging based on image reconstruction to improve the image quality of abdominal enhanced scan. From November 2019 to October 2020, 76 cases of patients who underwent enhanced abdominal CT scans in our hospital were selected as the research objects. All the cases were divided into the control group and reconstruction group with 38 cases in each group. And the control group were given routine scanning imaging, the reconstruction group were given CT three-dimensional imaging based on adaptive statistical iterative reconstruction (ASIR), and the imaging quality and noise of the two groups were recorded. The subjective quality scores of the two doctors in the reconstruction group were higher than those in the control group (P<0.05). The images of the reconstruction group were relatively delicate and soft, and could clearly show the sections of small blood vessels, the peripheral blood vessels were well displayed, and the blood vessel walls were smooth and soft. The CT values of the liver and pancreas in the arterial, portal and balance phase of the reconstruction group were higher than those of the control group (P<0.05), and the contrast to noise ratio (CNR) values of the liver and pancreas in the arterial, portal and balance phases were lower than those of the control group (P<0.05). The volume CT dose index (CTDIvol), Dose-Length product(DLP) and effective dose (ED) values of the reconstruction group were lower than those of the control group (P<0.05). CT three-dimensional imaging based on image reconstruction can improve the subjective and objective image quality of abdominal enhanced scanning, reduce image noise, and is more conducive to the display of abdominal diseases, thereby increase the correct diagnosis rate. [ABSTRACT FROM AUTHOR]

Introduction: Reducing tube voltage is an effective dose saving method in computed tomography (CT) assuming tube current is not concurrently increased. Recent innovations in scanner technology now enable CT tube voltage reduction to 70 kV thereby increasing opportunities for dose reduction in paediatric patients, but it is unclear if the increased image noise associated with 70 kV impacts on ability to visualise renal stones accurately. The purpose was to assess detectability of nephrolithiasis using a bespoke paediatric phantom and low kV, non‐contrast CT and to assess inter‐observer agreement. Methods: Forty‐two renal stones of different size and chemical composition were inserted into porcine kidneys and positioned in a bespoke, water‐filled phantom mimicking a 9‐year‐old child weighing approximately 33kg. The phantom was scanned using 120 and 70 kV CT protocols, and the detectability of the stones was assessed by three radiologists. Absolute agreement and Fleiss' kappa regarding detectability were assessed. Results: The mean diameter of renal stones as measured physically was 4.24 mm ranging from 1 to 11 mm. Four stones were missed by at least one observer. One observer had a sensitivity of 93 and 95% at 70 and 120 kV, respectively, while the sensitivity for observers 2 and 3 was 98% at both kV levels. Specificity was 100% across readers and kV levels. Absolute agreement between the readers at 70 kV was 92% (kappa = 0.86) and 98% (kappa = 0.96) at 120 kV indicating a strong agreement at both kV levels. Conclusions: The results suggest that lowering the kV does not affect the detection rate of renal stones and may be a useful dose reduction strategy for assessment of nephrolithiasis in children. [ABSTRACT FROM AUTHOR]

Cerebrovascular segmentation is important in various clinical applications, such as surgical planning and computer-aided diagnosis. In order to achieve high segmentation performance, three challenging problems should be taken into consideration: (1) large variations in vascular anatomies and voxel intensities; (2) severe class imbalance between foreground and background voxels; (3) image noise with different magnitudes. Limited accuracy was achieved without considering these challenges in deep learning-based methods for cerebrovascular segmentation. To overcome the limitations, we propose an end-to-end adversarial model called FiboNet-VANGAN. Specifically, our contributions can be summarized as follows: (1) to relieve the first problem mentioned above, a discriminator is proposed to regularize for voxel-wise distribution consistency between the segmentation results and the ground truth; (2) to mitigate the problem of class imbalance, we propose to use the addition of cross-entropy and Dice coefficient as the loss function of the generator. Focal loss is utilized as the loss function of the discriminator; (3) a new feature connection is proposed, based on which a generator called FiboNet is built. By incorporating Dice coefficient in the training of FiboNet, noise robustness can be improved by a large margin. We evaluate our method on a healthy magnetic resonance angiography (MRA) dataset to validate its effectiveness. A brain atrophy MRA dataset is also collected to test the performance of each method on abnormal cases. Results show that the three problems in cerebrovascular segmentation mentioned above can be alleviated and high segmentation accuracy can be achieved on both datasets using our method. [ABSTRACT FROM AUTHOR]

Despite the low spatial resolution of 2D-multisegment late gadolinium enhancement (2D-MSLGE) sequences, it may be useful in uncooperative patients instead of standard 2D single segmented inversion recovery gradient echo late gadolinium enhancement sequences (2D-SSLGE). The aim of the study is to assess the feasibility and comparison of 2D-MSLGE reconstructed with artificial intelligence reconstruction deep learning noise reduction (NR) algorithm compared to standard 2D-SSLGE in consecutive patients with ischemic cardiomyopathy (ICM). Fifty-seven patients with known ICM referred for a clinically indicated CMR were enrolled in this study. 2D-MSLGE were reconstructed using a growing level of NR (0%,25%,50%,75%and 100%). Subjective image quality, signal to noise ratio (SNR) and contrast to noise ratio (CNR) were evaluated in each dataset and compared to standard 2D-SSLGE. Moreover, diagnostic accuracy, LGE mass and scan time were compared between 2D-MSLGE with NR and 2D-SSLGE. The application of NR reconstruction ≥50% to 2D-MSLGE provided better subjective image quality, CNR and SNR compared to 2D-SSLGE (p < 0.01). The best compromise in terms of subjective and objective image quality was observed for values of 2D-MSLGE 75%, while no differences were found in terms of LGE quantification between 2D-MSLGE versus 2D-SSLGE, regardless the NR applied. The sensitivity, specificity, negative predictive value, positive predictive value and accuracy of 2D-MSLGE NR 75% were 87.77%,96.27%,96.13%,88.16% and 94.22%, respectively. Time of acquisition of 2D-MSLGE was significantly shorter compared to 2D-SSLGE (p < 0.01). When compared to standard 2D-SSLGE, the application of NR reconstruction to 2D-MSLGE provides superior image quality with similar diagnostic accuracy. • NR technique improves image quality of late gadolinium enhancement. • 2D-MSLGE reconstructed with NR 75% provide better image quality compared to 2D-SSLGE. • NR reconstruction on 2D-MSLGE has not impact in terms of diagnostic accuracy. [ABSTRACT FROM AUTHOR]

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Introduction: Recent studies of Computed Tomography (CT) conducted on patient dose reduction have recommended using an iterative reconstruction algorithm and mA (mili-Ampere) dose modulation. The current study aimed to evaluate Iterative Dose Reduction Algorithm (IDREAM) as an iterative reconstruction algorithm. Material and Methods: Two CT protocols (i.e., A: 120 KV /150 mA, FBP; B: 120KV/ (20-150) mAs, IDREAM) to scan water and acrylic phantoms. A number of 40 patients were assigned to two CT protocols (C: n=20, 120KV/160 ±10 mAs, FBP and D: n=20, 120 KV/ (30-150 mAs, IDREAM), the two groups (C and D) were then referred to abdomen and pelvis CT scan (Sinovision, insitum 16) with contrast. Image quality parameters, dose calculations were measured for all groups (i.e., A, B, C, and D). Results: Group B had a highly significant SNR with less significant noise (P0.05) in acrylic phantom, as compared to group A. CTDIvol (A: 13.94 mGy ; B: 6.91 mGy , PConclusion: The results of the present study were indicative of the feasibility of IDREAMas an iterative reconstruction algorithm.

The curvature of grain boundaries in polycrystalline materials is an important characteristic, since it plays a key role in phenomena like grain growth. However, most traditional tessellation models that are used for modeling the microstructure morphology of these materials, e.g., Voronoi or Laguerre tessellations, have flat faces and thus fail to incorporate the curvature of the latter. For this reason, we consider generalizations of Laguerre tessellations—variations of so-called generalized balanced power diagrams (GBPDs)—that exhibit non-convex cells. With as many as ten parameters for each cell, it is computationally demanding to fit GBPDs to three-dimensional image data containing hundreds of grains. We therefore propose a modification of the traditional definition of GBDPs that allows gradient-based optimization methods to be employed. The resulting reduction in runtime makes it feasible to find approximations to real experimental datasets. We demonstrate this on a three-dimensional x-ray diffraction (3DXRD) mapping of an AlCu alloy, but we also evaluate the modeling errors for simulated data. Furthermore, we investigate the effect of noisy image data and whether the smoothing of image data prior to the fitting step is advantageous.

Cerebrovascular segmentation is important in various clinical applications, such as surgical planning and computer-aided diagnosis. In order to achieve high segmentation performance, three challenging problems should be taken into consideration: (1) large variations in vascular anatomies and voxel intensities; (2) severe class imbalance between foreground and background voxels; (3) image noise with different magnitudes. Limited accuracy was achieved without considering these challenges in deep learning-based methods for cerebrovascular segmentation. To overcome the limitations, we propose an end-to-end adversarial model called FiboNet-VANGAN. Specifically, our contributions can be summarized as follows: (1) to relieve the first problem mentioned above, a discriminator is proposed to regularize for voxel-wise distribution consistency between the segmentation results and the ground truth; (2) to mitigate the problem of class imbalance, we propose to use the addition of cross-entropy and Dice coefficient as the loss function of the generator. Focal loss is utilized as the loss function of the discriminator; (3) a new feature connection is proposed, based on which a generator called FiboNet is built. By incorporating Dice coefficient in the training of FiboNet, noise robustness can be improved by a large margin. We evaluate our method on a healthy magnetic resonance angiography (MRA) dataset to validate its effectiveness. A brain atrophy MRA dataset is also collected to test the performance of each method on abnormal cases. Results show that the three problems in cerebrovascular segmentation mentioned above can be alleviated and high segmentation accuracy can be achieved on both datasets using our method.

Background: Dedicated cardiac SPECT cameras which employ multi-pinhole detectors have variable photon sensitivity within the camera's field-of-view such that a lower number of photon counts is typically detected from the base of the heart than from the apex. Consequently, the noise in a reconstructed image is expected to be higher at the base than at the apex of the heart. Methods: Patient emission images were resampled to create statistical replicates which were reconstructed with and without attenuation correction. Noise images were computed using one standard deviation of the replicated images. These were evaluated for 93 patients with normal study results, each imaged with both a dual-headed parallel-hole camera and a multi-pinhole camera. Statistics for a normal database (NDB) of images from the 93 patients were also calculated. Results: Image noise (1.7-fold) and NDB uncertainty (1.3-fold) increase significantly from the apex-to-the base of the heart in attenuation-corrected multi-pinhole SPECT images. The differences for non-attenuation-corrected images or those acquired with a parallel-hole camera were not significant. Conclusions: For best interpretation of attenuation-corrected images acquired with multi-pinhole cameras, knowledge of NDB uncertainty gradients should be taken into consideration. [ABSTRACT FROM AUTHOR]

The paper designs a hybrid method for image fusion technique. The proposed algorithm is a combination of Directional Discrete Cosine Transform (DDCT) and Principal Component Analysis (PCA) technique. The proposed algorithm gives better results compared to other traditional techniques like Discrete Wavelet Transform (DWT) and segmentation. The multiple problems in image fusion like image noise, image pixel quality, etc. are identified using image filter. The paper also uses impulse filter to reduce image noise and improve pixel quality. [ABSTRACT FROM AUTHOR]

Purpose: State regulations require that CT radiation output be measured using a dosimeter after major service. The most common major service is tube replacement. We hypothesized that historical QC data could be used instead to determine if output measurements are necessary, potentially reducing the need for output measurements that require an on‐site visit by a qualified medical physicist. Methods: Records of 65 original equipment manufacturer (OEM) tube replacements were reviewed to determine with what frequency output was outside the manufacturer's specifications. The previous 7 days of historical quality control (QC) data prior to a tube change was used to establish a baseline mean noise level and 95% inferential confidence intervals (ICIs) about the mean. This was compared to an ICI constructed using 7 days of QC data post‐tube change and the region of indifference. Different methods for acquiring samples of image noise were compared using a single factor analysis of variance (ANOVA). Results: None of the 65 tube replacements reviewed in this study resulted in an output change that exceeded the manufacturer's specifications. In all but one case, the results of the ICI analysis matched the measured output results. In the single case where results were discordant, the mean image noise was slightly higher after the tube change, which may have indicated the need for a larger sample size or service unrelated to the X‐ray tube, for example, system calibration. The method used to sample image noise did not significantly affect the calculated mean noise. Conclusions: This review of historical OEM tube replacement data indicated the likelihood of output falling outside manufacturer specifications is low. Considering this, it is likely that using QC data from programs required by regulation and the American College of Radiology (ACR), medical physicists can reliably verify radiation output stability remotely instead of making measurements using a dosimeter. [ABSTRACT FROM AUTHOR]