Objective.To quantify differences in quantum noise fraction (QNF) between direct and indirect mammography detectors across spatial frequency bands, assessing the ability of QNF to reveal performance degradation at high frequencies.Approach.QNF of various mammography detectors were analyzed by selectively restricting the analysis to medium and high spatial frequency bands. The study included detectors from different manufacturers, both direct and indirect types. Noise components were determined from the sub-bands of a wavelet packet decomposition applied to uniform images acquired at different entrance kerma levels. The noise components were obtained from the terms of a second-degree polynomial fitted to the total noise variance of the sub-bands as a function of entrance kerma to the detector. The results obtained were compared with detective quantum efficiency calculations and contrast-detail curves of the detectors studied.Main results.The QNF results obtained in the different frequency bands studied differ significantly from those calculated in the image domain. In all cases, the QNF decreases with increasing frequency across the entire range of kerma values. Moreover, this decrease is much more pronounced for indirect detectors than for direct ones. Despite showing similar QNF values in the spatial domain, the differences between direct and indirect detectors become substantial in the mid-frequency bands and increase even further in the high-frequency bands. Among the image quality metrics evaluated, the largest differences between detectors were observed in the frequency-dependent evolution of the QNF.Significance.Decreasing values of the QNF reflect the loss in signal-to-noise ratio transfer from input to output in digital detectors, which is directly associated with a loss in detectability. The greater reduction in quantum noise observed in the mid- and high-frequency components of indirect detectors may indicate a decline in their performance for tasks involving the detectability of small objects. QNF exhibited the greatest sensitivity to scintillator-induced signal blurring, making it the metric that best distinguishes between direct and indirect detector designs.

Novel surface-attached resonant acoustic metamaterials for vibration and noise reduction in the natural gas pipeline

Recently, with the rapid development of autonomous vehicles, intelligent robots, and mobile electronics, retina-inspired neuromorphic photosensors have attracted growing interest as color image processors for machine vision systems. These devices typically mimic essential functions of the human retina, such as multicolor detection and the processing of raw visual information. However, most neuromorphic photosensors have been implemented with heterojunction channel structures or complex circuit architectures, resulting in system complexity, low resolution, and inefficient energy consumption. Here, we propose a neuromorphic phototransistor with a homogeneous channel structure using subgap-engineered metal-oxide (MO) semiconductors. Despite the absence of conventional heterojunction channel architectures, subgap-engineered MO phototransistors, achieved through intentional doping with alkali metal ions, demonstrate broad spectral responsivity and analog conductance modulation in a compact device structure. Particularly, when doped with a small amount of alkali metal ions (Li 5 at%-MO), the device exhibits in-sensor color image processing capabilities, including full-color detection, enhanced analog conductivity, and distinct sensing performance based on the input color. By applying a 7 × 7 neuromorphic phototransistor array using the Li 5 at%-MO semiconductor as the frontend device, innovative refinement tasks of raw color images such as color character sharpness, noise reduction, and contrast enhancement were successfully achieved, significantly contributing to the overall performance enhancement of the machine vision system.

Artificial intelligence in 4D flow MRI: Review of technological aspects and clinical applications.

Experimental and numerical optimization of diesel injection timing for performance enhancement, combustion mode control, and noise reduction in an ammonia-diesel dual-fuel engine

A numerical investigation of noise reduction of ground lattices for rotors in ground effect

Engine nacelle acoustic liners are key components in modern aircraft engine noise control. The high-bypass-ratio turbofan engines possessing outstanding performance impose stringent constraints on the noise reduction design, thereby presenting...

Research on optimization of exhaust structure and exhaust noise reduction for an aviation piston two-stroke engine

A head computed tomography angiography (CTA) is widely used for diagnosing cerebrovascular diseases and preoperative vascular assessment. A photon-counting detector CT (PCD-CT)enables virtual monochromatic image (VMI)reconstruction regardless of tube voltage. In PCD-CT, both a low-voltage polyenergetic reconstruction (Poly) and a VMI reconstruction (Mono) can be selected to enhance contrast and reduce contrast medium usage. However, Mono increases noise at lower energy levels, necessitating noise reduction techniques that may degrade spatial resolution. This study aimed to compare the image quality between 70 kVp-Poly and 140 kVp-50 keV in PCD-CT head CTA to determine the optimal scanning method. The task transfer function (TTF) and the noise power spectrum were evaluated using a cylindrical water phantom with an attached bone ring. A vascular phantom with 1 mm branches was scanned for full-width at half-maximum (FWHM) and contrast-to-noise ratio (CNR) measurements. Visual assessment was performed by 10 radiological technologists using a five-point scale. No significant difference was observed in TTF or FWHM. However, 70 kVp-Poly had a significantly higher CNR than 140 kVp-50 keV. Visual scores were also significantly higher for 70 kVp-Poly. In PCD-CT head CTA, 70 kVp-Poly provides superior image quality over 140 kVp-50 keV, making it a preferable choice for enhanced vascular depiction while minimizing image degradation.

Statistical characterization of low-frequency seabed ambient noise on the southwest Indian ridge based on passive acoustic observations.

Spherical metacage for low-frequency noise reduction

An improved u-net method for denoising ultrasonic echo signals in carbon fiber composites.

A high comprehensive performance ECG noise reduction architecture based on conditional generative adversarial net

Experimental investigation on the application of serrated trailing edge propellers for drone noise reduction

Experimental and numerical investigations on the noise reduction performance and mechanism of the perforated sleeve in a natural gas station pressure reducing valve

Modified oversampling based Borderline Smote with Noise Reduction Techniques for IoT Smart Farming dataset

Research on aerodynamic noise analysis and noise reduction method of gas regulator

Purpose Traditional data analysis methods are plagued by problems such as the difficulty of extracting features from the data and the low accuracy of removing noise from bridge monitoring data. Noise reduction analysis is implemented on the experimental sample monitoring data by using the topology data analysis method. Design/methodology/approach The time-delayed embedding simple complex form and noise reduction methods for topology data analysis are stated. A topology data analysis experiment is designed using the displacement sample monitoring data of an in-service assembled simple girder bridge as the research object, and noise reduction analysis is implemented on the experimental sample monitoring data by using the topology data analysis method. Findings The time-delayed embedding simple complex shape and noise reduction algorithms of topological data analysis are more innovative to be used for the analysis of one-dimensional time-series data. The experimental design is more reasonable and repeatable, and the experimental results show that the topological data analysis method can be used to reduce the noise of bridge structure monitoring data. This study contributes to the scientific analysis and evaluation of the performance of in-service bridges by improving the accuracy of the analysis of bridge monitoring data. Originality/value In this paper, topological data analysis methods are employed to study the implied topological features in bridge monitoring data and implement improvements to further enhance the noise reduction. This study contributes to the scientific analysis and evaluation of the performance of in-service bridges by improving the accuracy of the analysis of bridge monitoring data.

Spatial filters are required to suppress the statistical noise of SPECT images but with an unavoidable smoothing effect that further decreases the SUV and contrast. This study assesses a deep-learning noise reduction (DLNR) algorithm, previously developed to further reduce bone SPECT recording time on a high-speed whole-body 360° CZT-camera, when used instead of, rather than in addition to, the conventional spatial filters (CSF) recommended for this camera. The SUVmax of bone lesions (114 definite arthritis or metastasis lesions) and the resolution recovery coefficients of small and medium phantom spheres, were higher for DLNR than CSF or the combination of CSF plus DLNR (CSF-DLNR) (all p < 0.001), whereas the relative noises were lower for DLNR or CSF-DLNR, as compared with CSF (p < 0.001). Consequently, contrast-to-noise ratio (CNR) was dramatically higher for DLNR, as compared with CSF, and also CSF-DLNR, especially for small- and medium-sized structures. Compared with CSF, DLNR provided an almost two-fold CNR increase for the sphere and lesions in the range of one cm3. This dramatic CNR improvement was still documented when DLNR was compared with the median, kernel, Butterworth, or Gaussian filters used alone and set to provide an equivalent image noise reduction to DLNR on the phantom. When used alone, this DLNR algorithm enhances the contrast-to-noise ratio and quantification of bone lesions, especially those of small or medium sizes. It outperforms conventional spatial filters and provides remarkable image quality for routine analysis of bone SPECT from the high-speed whole-body 360° CZT camera. However, further research and validation studies are still necessary before a widespread adoption in clinical practice. Question: How does a deep-learning noise reduction algorithm, previously developed to further reduce bone SPECT recording times on a high-speed whole-body 360° CZT-camera, work when used instead of, rather than in addition to, the conventional spatial filters recommended for this camera. Pertinent findings: When used alone, this deep-learning noise reduction algorithm provides a high level of image denoising and better preserves the activities of small- to medium-sized bone structures and lesions than conventional spatial filters do, leading to a dramatic increase in the corresponding contrast-to-noise ratios. Such a deep-learning noise reduction algorithm could be used not only to reduce SPECT recording time when added to conventional spatial filters, but also to improve image quality and resolution when used alone. clinicaltrials.gov, NCT06782438, Registered 27 February 2025,https://clinicaltrials.gov/search?id=NCT06782438.

&lt;b&gt;Introduction:&lt;/b&gt; Positron emission tomography (PET) has undergone transformative advancements, evolving from a research tool into a cornerstone of precision medicine. &lt;br&gt;&lt;b&gt;Objective:&lt;/b&gt; This review highlights key developments in PET imaging, including the introduction of specialized systems such as brain and breast-dedicated scanners, total-body PET, and hybrid PET/CT and PET/MRI technologies. &lt;br&gt;&lt;b&gt;Methods:&lt;/b&gt; These innovations have significantly enhanced diagnostic accuracy and patient management across oncology, neurology and cardiology. The emergence of novel radiotracers beyond fluorodeoxyglucose (FDG) has expanded PET's clinical applications by targeting specific molecular pathways, improving sensitivity and specificity in disease characterization. Notable tracers include those for tumor proliferation, hypoxia and receptor-specific imaging, which facilitate personalized treatment strategies. The integration of artificial intelligence (AI) has revolutionized PET imaging by improving image reconstruction, noise reduction, motion correction and lesion segmentation. AI-driven tools enhance diagnostic precision while reducing scan times and radiation exposure, making PET safer and more efficient. Furthermore, AI accelerates radiotracer development by optimizing molecular design and enabling personalized dosimetry planning for theranostic applications. Total-body PET scanners represent a technological milestone, offering unparalleled sensitivity, reduced radiation doses, faster scans, the ability to track systemic diseases comprehensively and to enhance diagnosis by novel imaging biomarkers. These advancements enable earlier disease detection, precise monitoring of treatment efficacy and deeper insights into disease mechanisms. &lt;br&gt;&lt;b&gt;Results:&lt;/b&gt; Collectively, these innovations underscore PET's transformative role in advancing precision medicine through early diagnosis, disease monitoring and tailored therapeutic interventions. &lt;br&gt;&lt;b&gt;Conclusions:&lt;/b&gt; This review concludes that ongoing technological progress will continue to redefine the capabilities of PET imaging in clinical practice and research.