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Soil amendments (e.g., compost) require uniform incorporation in the soil profile to benefit plants. However, machines may not mix them uniformly throughout the upper soil layer commonly explored by plant roots. The study focuses on using image texture analysis to determine the level of mixing uniformity in the soil following the passage of two kinds of harrows. A 12.3-megapixel DX-format digital camera acquired images of soil/expanded polystyrene (in the laboratory) and soil/compost mixtures (in field conditions). In the laboratory, pictures captured the soil before and during the simulated progressive mixing of expanded polystyrene particles. In field conditions, images captured the exposed superficial horizons of compost-amended soil after the passage of a combined spike-tooth–disc harrow and a disc harrow. Image texture analysis based on the gray-level co-occurrence matrix calculated the sums of dissimilarity, contrast, entropy, and uniformity metrics. In the laboratory conditions, the progressive mixing resulted in increased image dissimilarity (from 1.15 ± 0.74 × 106 to 1.65 ± 0.52 × 106) and contrast values (from 2.69 ± 2.06 × 106 to 5.67 ± × 1.93 106), almost constant entropy (3.50 ± 0.25 × 106), and decreased image uniformity (from 6.65 ± 0.31 × 105 to 4.49 ± 1.36 × 105). Using a tooth-disc harrow in the open field resulted in higher dissimilarity, contrast, entropy (+73.3%, +62.8%, +16.3%), and lower image uniformity (−50.6%) than the disc harrow, suggesting enhanced mixing in the superficial layer.

Objective To assess quantitatively the effect of metallic materials on MR image uniformity using a standardized method. Methods Six types of 1 cm cubic metallic materials (i.e., Au, Ag, Al, Au–Ag–Pd alloy, Ti, and Co–Cr alloy) embedded in a glass phantom filled were examined and compared with no metal condition inserted as a reference. The phantom was scanned five times under each condition using a 1.5-T MR superconducting magnet scanner with an 8-channel phased-array brain coil and head and neck coil. For each examination, the phantom was scanned in three planes: axial, coronal, and sagittal using T1-weighted spin echo (SE) and gradient echo (GRE) sequences in accordance with the American Society for Testing and Materials (ASTM) F2119-07 standard. Image uniformity was assessed using the non-uniformity index (NUI), which was developed by the National Electrical Manufacturers Association (NEMA), as an appropriate standardized measure for investigating magnetic field uniformity. Results T1-GRE images with Co–Cr typically elicited the lowest uniformity, followed by T1-GRE images with Ti, while all other metallic materials did not affect image uniformity. In particular, T1-GRE images with Co–Cr showed significantly higher NUI values as far as 6.6 cm at maximum equivalent to 11 slices centering around it in comparison with the measurement uncertainty from images without metallic materials. Conclusion We found that MR image uniformity was influenced by the scanning sequence and coil type when Co–Cr and Ti were present. It is assumed that the image non-uniformity in Co–Cr and Ti is caused by their high magnetic susceptibility.

Purpose Real‐time magnetic resonance guided radiation therapy (MRgRT) uses 2D cine imaging for target tracking. This work evaluates the percent image uniformity (PIU) and spatial integrity of cine images in the presence of multileaf collimator (MLC) and gantry motion in order to simulate sliding window and volumetric modulated arc therapy (VMAT) conditions. Methods Percent image uniformity and spatial integrity of cine images were measured (1) during MLC motion, (2) as a function of static gantry position, and (3) during gantry rotation. PIU was calculated according to the ACR MRI Quality Control Manual. Spatial integrity was evaluated by measuring the geometric distortion of 16 measured marker positions (10 cm or 15.225 cm from isocenter). Results The PIU of cine images did not vary by more than 1% from static linac conditions during MLC motion and did not vary by more than 3% during gantry rotation. Banding artifacts were present during gantry rotation. The geometric distortion in the cine images was less than 0.88 mm for all points measured throughout MLC motion. For all static gantry positions, the geometric distortion was less than 0.88 mm at 10 cm from isocenter and less than 1.4 mm at 15.225 cm from isocenter. During gantry rotation, the geometric distortion remained less than 0.92 mm at 10 cm from isocenter and less than 1.60 mm at 15.225 cm from isocenter. Conclusion During MLC motion, cine images maintained adequate PIU, and the geometric distortion of points within 15.225 cm from isocenter was less than the 1 mm threshold necessary for real‐time target tracking and gating. During gantry rotation, PIU was negatively affected by banding artifacts, and spatial integrity was only maintained within 10 cm from isocenter. Future work should investigate the effects imaging artifacts have on real‐time target tracking during MRgRT.

Objective: The purpose of this study was to develop an automated method for performing quality control (QC) tests in magnetic resonance imaging (MRI) systems, investigate the effect of different definitions of QC parameters and its sensitivity with respect to variations in regions of interest (ROI) positioning, and validate the reliability of the automated method by comparison with results from manual evaluations. Materials and Methods: Magnetic Resonance imaging MRI used for acceptance and routine QC tests from five MRI systems were selected. All QC tests were performed using the American College of Radiology (ACR) MRI accreditation phantom. The only selection criterion was that in the same QC test, images from two identical sequential sequences should be available. The study was focused on four QC parameters: percent signal ghosting (PSG), percent image uniformity (PIU), signal-to-noise ratio (SNR), and SNR uniformity (SNRU), whose values are calculated using the mean signal and the standard deviation of ROIs defined within the phantom image or in the background. The variability of manual ROIs placement was emulated by the software using random variables that follow appropriate normal distributions. Results: Twenty-one paired sequences were employed. The automated test results for PIU were in good agreement with manual results. However, the PSG values were found to vary depending on the selection of ROIs with respect to the phantom. The values of SNR and SNRU also vary significantly, depending on the combination of the two out of the four standard rectangular ROIs. Furthermore, the methodology used for SNR and SNRU calculation also had significant effect on the results. Conclusions: The automated method standardizes the position of ROIs with respect to the ACR phantom image and allows for reproducible QC results.

Assessment of radionuclide activity concentration on positron emission tomography-computedr tomography (PET-CT) image uniformity has been carried out quantitatively. Tomographic PET-CT images of cylindrical phantom containing F-18 fluorodeoxyglucose (FDG) activity concentration was acquired and used for the assessment. Activity concentrations were varied and PET-CT images were acquired at the constant acquisition parameters of time, matrix size, and reconstruction algorithm, respectively. Using midtransaxial image slices, quantitative index of nonuniformity (NU), and coefficient of uniformity variation were estimated for the different activity concentrations. Maximum NUs of 17.6%, 26.3%, 32.7%, 36.2%, and 38.5% were estimated for activity concentrations of 16.87 kBq/mL, 14.06 kBq/mL, 11.25 kBq/mL, 8.43 kBq/mL, and 5.62 kBq/mL, respectively. The coefficient of uniformity variation established an inverse quadratic relationship with activity concentration. Activity concentrations of 16.87 kBq/mL, 14.06 kBq/mL, 11.25 kBq/mL, 8.43 kBq/mL, and 5.62 kBq/mL produced uniformity variations of 1.47%, 2.52%, 4.23%, 5.12%, and 4.98%, respectively. Increasing activity concentration resulted in decreasing coefficient of uniformity and hence, an increase in image uniformity. The uniformity estimates compared well with the standards set internationally.

In the passive containment cooling system (PCCS), water distribution tests are essential to verify the water distribution devices performance. Without regular boundaries and homogeneous intensities, images of water film acquired from these tests are hard to be detected by conventional approaches. We propose an improved segmentation method to identify the water film areas from the complex background. Considering the gray distortion resulted from asymmetric illumination, the method combines the modified motion segmentation and optimal threshold method. Detection results show that this method is hardly affected by the illumination change, and also insensitive to noise.

In this paper, the edge detection for a medical image is performed based on Sobel operator, and the bounding box is obtained, by which the effective medical sub-image is extracted. Then, the centroid and the normalized central moments of the medical sub-image are calculated, and the rotation angle α is obtained by minimizing the second-order central moment based on its rotation invariance. Finally, the whole medical image is rotated around the centroid by −α to correct the tilted image. Furthermore, inspired by the uniformity degree of the image, the rotation angle α is revised, which achieves a better correction effect and performance. The experimental results show that the proposed algorithms are fairly reliable and accurate for the determination of tilt angles, and are practical and effective tilt correction techniques.

This review explores the current landscape of neural network-based methods for digital image noise processing. Digital cameras have become ubiquitous in fields like forensics and medical diagnostics, and image noise remains a critical factor for ensuring image quality. Traditional noise suppression techniques are often limited by extensive parameter selection and inefficient handling of complex data. In contrast, neural networks, particularly convolutional neural networks, autoencoders, and generative adversarial networks, have shown significant promise for noise estimation, suppression, and analysis. These networks can handle complex noise patterns, leverage context-specific data, and adapt to evolving conditions with minimal manual intervention. This paper describes the basics of camera and image noise components and existing techniques for their evaluation. Main neural network-based methods for noise estimation are briefly presented. This paper discusses neural network application for noise suppression, classification, image source identification, and the extraction of unique camera fingerprints through photo response non-uniformity. Additionally, it highlights the challenges of generating reliable training datasets and separating image noise from photosensor noise, which remains a fundamental issue.

The growing demand for scalable solar‐blind image sensors with remarkable photosensitive properties has stimulated the research on more advanced solar‐blind photodetector (SBPD) arrays. In this work, the authors demonstrate ultrahigh‐performance metal‐semiconductor‐metal (MSM) SBPDs based on amorphous (a‐) Ga2O3 via a post‐annealing process. The post‐annealed MSM a‐Ga2O3 SBPDs exhibit superhigh sensitivity of 733 A/W and high response speed of 18 ms, giving a high gain‐bandwidth product over 104 at 5 V. The SBPDs also show ultrahigh photo‐to‐dark current ratio of 3.9 × 107. Additionally, the PDs demonstrate super‐high specific detectivity of 3.9 × 1016 Jones owing to the extremely low noise down to 3.5 fW Hz−1/2, suggesting high signal‐to‐noise ratio. Underlying mechanism for such superior photoelectric properties is revealed by Kelvin probe force microscopy and first principles calculation. Furthermore, for the first time, a large‐scale, high‐uniformity 32 × 32 image sensor array based on the post‐annealed a‐Ga2O3 SBPDs is fabricated. Clear image of target object with high contrast can be obtained thanks to the high sensitivity and uniformity of the array. These results demonstrate the feasibility and practicality of the Ga2O3 PDs for applications in solar‐blind imaging, environmental monitoring, artificial intelligence and machine vision. Ultraviolet imaging technology is widely used in meteorology, medical science, and military science. For the first time, a high‐uniformity 32 × 32 solar‐blind image sensor array with outstanding imaging capability is demonstrated based on high‐performance Ga2O3 photodetectors. Schottky barrier lowering effect is experimentally revealed to attribute to the internal gain mechanism.

We have developed an X‐ray scattering setup capable of capturing time‐resolved small‐angle X‐ray scattering (SAXS) and wide‐angle X‐ray scattering (WAXS) images spanning q = 0.02 to over 5.2 Å−1 on a single, large area Rayonix MX340‐HS detector with time resolution as short as 120 ps. A key feature of this setup is a 0.51 mm‐diameter partially transmissive beamstop that enables non‐invasive, image‐by‐image recording of direct beam position and intensity during acquisition. This beamstop attenuates 12 keV undulator radiation by approximately eight orders of magnitude while suppressing off‐axis second harmonic radiation. Continuous monitoring of the direct beam position facilitates long‐term beam alignment and allows datasets acquired at different times to be placed on a common absolute scale prior to differencing. The accuracy of the difference scattering curves is ultimately limited by the performance of the large area, fiber‐taper X‐ray detector used in this study. To address accuracy issues, we present a detailed statistical characterization of the detector readout noise and responsivity and introduce a variance‐per‐count statistic that enables identification of zinger‐free averages, generation of precise uniformity corrections, and statistically weighted conversion of two‐dimensional scattering images into near shot‐noise‐limited one‐dimensional scattering curves. The detector point‐spread function and its effects on resolution and scaling are examined using scattering data from a fused silica plate and from apoferritin solution in a capillary. The ability to acquire high accuracy, high precision scattering curves over a broad range of q and temperatures provides a robust foundation for time‐resolved studies of biomolecular structure and dynamics in solution. An X‐ray scattering setup designed to capture small‐ and wide‐angle X‐ray scattering on a single, large area detector features a partially transmissive beamstop that facilitates non‐invasive recording of X‐ray beam position and intensity during acquisition of X‐ray scattering images. The integrated transmitted intensity allows different datasets to be put on the same absolute scale and thereby achieve accurate differencing.