Image Noise Research Articles

A robust proportional filtered integral controller based on backpropagation neural network

This paper introduces a novel design of a proportional filtered-integral (P-FI) controller whose parameters are auto-tuned using the backpropagation neural networks (BPNN) algorithm. The proposed controller is designed to address the main challenges posed by a class of complex systems characterized by uncertain high-gain and pure integrator dynamics, including high-frequency noise amplification and poor robustness against parameter variations. Designing such a controller involves three main steps: First, a proportional–integral–derivative (PID) controller is designed, with its parameters auto-tuned online using the BPNN algorithm, resulting in the primary (BPNN-PID) controller. Second, the obtained parameters of the previous controller are utilized to compute those of a low-pass filter offline. This filter is then cascaded with the integral parts of a PID controller, forming a P-FI controller structure. This configuration introduces a phase lead within a specific frequency range without amplifying high-frequency noise, overcoming the primary disadvantage of the derivative term. Finally, the parameters of the resulting P-FI controller are again auto-tuned online using the BPNN algorithm, resulting in the final robust (BPNN-P-FI) controller. This novel controller structure and its parameters tuning procedure, based on a two-stage BPNN learning approach, constitute the main contributions of this paper. Simulation results demonstrate the superiority of the proposed controller in terms of time-domain performance, sensor noise attenuation, and closed-loop robustness compared to those obtained with the BPNN-PID and optimally tuned PID controllers.

Phantom test procedures for a new neuro-oncological amino acid PET tracer: [18F]fluciclovine.

Amino acid positron emission tomography (PET) examinations using anti-1-amino-3-[18F]-fluorocyclobutane-1-carboxylic acid ([18F]FACBC) were allowed for routine clinical use in July 2024. However, phantom test procedures for [18F]FACBC reconstruction parameters have not yet been established. The present study aimed to establish new phantom test procedures for [18F]FACBC brain PET imaging to determine optimal reconstruction parameters. Background (BG) activity as well as hot sphere and target-to-background ratios (TBRs) of [18F]FACBC were estimated based on brain activity and tumor-to-normal tissue ratios (TNR) in a Japanese clinical trial of [18F]FACBC. Phantom experiments proceeded under [18F]FACBC or L-[methyl-11C]-methionine ([11C]MET) conditions. The number of iterations and the Gaussian filter parameters were determined from the reconstruction parameters %contrastmean and coefficients of variation (CVs) in ordered subset expectation maximization (OSEM) and time-of-flight (TOF) with or without point-spread-function (PSF) correction. The amounts of activity in the hot spheres and BG were 1.1 and 5.5kBq/mL, respectively, and the TBR was 5.0 at the start of acquisition. The %contrastmean of all hot spheres was higher with [18F]FACBC than [11C]MET, and %contrastmean converged between 4 and 6 iterations in hot spheres with diameters < 10mm. We used four iterations for OSEM + TOF and five for OSEM + TOF + PSF correction for [18F]FACBC and [11C]MET images. The CV was higher for [18F]FACBC than [11C]MET. The optimal sizes of Gaussian filters for OSEM + TOF and OSEM + TOF + PSF correction of image reconstruction were 5mm for [18F]FACBC, and 4 and 3mm, respectively, for [11C]MET images. We estimated phantom activity and TBR based on brain activity in a Japanese clinical trial and established new phantom test procedures for [18F]FACBC. We recommend that the optimal reconstruction parameters for [18F]FACBC should be set to the same number of iterations as [11C]MET and that the FWHM of Gaussian filter should have a few mm higher than [11C]MET to reduce image noise from brain normal tissue.

Assessment of interstitial lung disease in a systemic sclerosis patient cohort using photon-counting detector CT with ultra-high resolution and a 1024-pixel image matrix.

This study assessed the potential of ultra-high-resolution (UHR) and a 1024-matrix in photon-counting-detector CT (PCD-CT) for evaluating interstitial lung disease (ILD) in systemic sclerosis (SSc) patients. Sixty-six SSc patients who underwent ILD-CT screening on a first-generation PCD-CT were retrospectively included. Scans were performed in UHR mode at 100 kVp with two different matrix sizes (512x512 and 1024x1024) and reconstructed at slice thicknesses of 1.5 mm and 0.2 mm. Image noise, subjective image quality, and ILD changes (1) were evaluated on a 5-point Likert-scale by two independent readers. Interreader agreement for subjective image quality ranged from fair to almost perfect (Krippendorff-Alpha: 0.258-0.862). Overall image quality was highest for 1.5 mm/1024matrix images ((reader 1: 4 (4.4), reader 2: 5 (4.5)). Image sharpness was rated significantly better in 0.2 mm images (p < 0.001). Regarding ILD changes, 0.2 mm slice thickness outperformed 1.5 mm slice thickness significantly (p < 0.001), while there was no significant difference between the two matrix sizes. 1024 matrix size demonstrated superiority in evaluating coarse reticulations compared to 512matrix size. UHR mode with a 0.2 mm slice thickness showed enhanced image sharpness and improved visibility of ILD changes compared to standard reconstructions. This has the potential to enable the early detection of subtle disease manifestations. With the invention of PCD-CT different reconstruction algorithms need to be evaluated for specific pathologies. In our study ILD UHR mode with 0.2 mm slice thickness showed to be beneficial in the detection of parenchymal changes in patients with scleroderma.

The current status of the development of photomemoryristors in the field of neuromorphic vision

As a new type of device, photoelectric memristors have the potential to mimic the human visual system. Researchers have been able to realize image enhancement, noise reduction, recognition and classification functions through optoelectronic modulation, which has a wide range of applications in the fields of neuromorphic computing and image processing. This paper discusses and analyzes the overview of amnesia, the research progress of photoelectric amnesia, as well as the application and prospect of photoelectric amnesia in artificial vision systems. Through the discussion, this paper concludes that photoelectric memristors have important application potentials in visual perception and recognition, intelligent surveillance systems, and medical image analysis. Photoelectric memristors can simulate part of the functions of the human visual system, provide more efficient and accurate image processing solutions, and are expected to bring technological breakthroughs in a number of application areas. By improving the accuracy and efficiency of image recognition and categorization, it can promote the development of intelligent systems. This is not only an important impetus for basic scientific research, but also has a profound impact on practical applications and industrial development. The prospect of wide application of photoelectric memristor means that it has the potential to become a core component of the next generation of intelligent systems, which will drive the progress and change of the whole field of science and technology.

Collaborative masking based speckle disentanglement for self-supervised optical coherence tomography image despeckling

Optical coherence tomography (OCT) has proven to be an effective and safe diagnostic tool in clinical settings due to its unique advantages. Nonetheless, the OCT images are unavoidably corrupted with speckle noise. Despeckling has become a vital preliminary step in OCT based image processing and analysis tasks. The supervised deep learning (DL) based denoising algorithms have recently attracted much attention due to their promising performance, but the lack of the noise-free OCT images renders it difficult to provide the effective supervision for DL model training. This paper has presented a self-supervised OCT image despeckling approach which does not depend on the reference clean image and combines the disentangled representation strategy with speckle noise estimation for the effective denoising. The proposed algorithm firstly disentangles the corrupted image into the speckle noise and clean image. Then, the multiple generated clean images are multiplied by the disentangled speckle noise to synthesize the speckle-corrupted image for self-supervised learning. To make the generated clean image and extracted speckle noise more statistically realistic, a collaborative masking strategy and a noise calibration module are applied to explore the spatial correlation of the clean image and refine speckle noise. The proposed method has been validated on two public datasets. Experimental results demonstrate that our method has superior speckle reduction performance to some traditional and DL-based despeckling approaches in terms of visual evaluation and quantitative metrics. Particularly, our method provides contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) improvements of at least 0.35 dB and 14.37 dB over the compared methods, respectively. Additionally, the retinal image classification results provide further evidence for the exceptional despeckling performance of our approach against other compared methods. It is obvious that the developed method has significant advantages in keeping the fidelity of retinal structure and enhancing the speckle suppression level. Meanwhile, the presented method holds certain reference value for improving diagnostic efficiency in clinical.

Underwater image enhancement using contrast correction

AbstractLight‐induced degeneration of underwater images occurs by physical features of seawater. According to the wavelength of the colour spectrum, light reduces intensity significantly when it moves through water. The greatest wavelength of light that is visible gets absorbed first. Red and blue absorb the most and least, respectively. Because of the reducing consequences of the light spectrum, underwater images having poor contrast can be obtained. As a result, the crucial data contained inside these images will not be effectively retrieved for later analysis. The recent research suggests a novel approach to enhance the contrast while decreasing noise in underwater images. The recommended approach involves image histogram transformation using two significant colour spaces, Red‐Green‐Blue (RGB) and Hue‐Saturation‐Value (HSV). The histogram of the dominant colour channel (blue channel) in the RGB colour model is extended towards the lower level, containing a maximum limitation of 95%, while the inferior red colour channel has been extended towards the upper side, containing a minimum limitation of 5%. During the entire dynamic range, the green colour channel having the dominant and inferior colour channels expands in each direction. The Rayleigh distribution has been utilized for developing various stretching actions within the RGB colour space. The image has been converted to the HSV colour space, having the S and V elements adjusted within 1% of their minimum and maximum values. The suggested approach is examined in both qualitative and quantitative analysis. According to qualitative analysis, the recommended approach substantially boosts image contrast, lowers its blue and green effect, and minimizes over‐enhanced and under‐enhanced sections in the final resultant underwater image. The quantitative examination of 500 large scale underwater images dataset reveals that the suggested technique generates better results. The dataset images are grouped into small fish images, blue coral images, stone wall images, and coral branch images. The quantitative examination of all these four groups have been evaluated and shown. The average mean square error, peak signal to noise ratio, underwater image quality measurement, and underwater colour image quality evaluation values of dataset images are 76.69, 31.25, 3.85, and 0.64, respectively. These values of our proposed work outperform six other previous methods.

Robust Direction-of-Arrival Estimation in the Presence of Outliers and Noise Nonuniformity

In direction-of-arrival (DOA) estimation with sensor arrays, the background noise is usually modeled to be uncorrelated uniform white noise, such that the related algorithms can be greatly simplified by making use of the property of the noise covariance matrix being a diagonal matrix with identical diagonal entries. However, this model can be easily violated by the nonuniformity of sensor noise and the presence of outliers that may arise from unexpected impulsive noise. To tackle this problem, we first introduce an exploratory factor analysis (EFA) model for DOA estimation in nonuniform noise. Then, to deal with the outliers, a generalized extreme Studentized deviate (ESD) test is applied for outlier detection and trimming. Based on the trimmed data matrix, a modified EFA model, which belongs to weighted least-squares (WLS) fitting problems, is presented. Furthermore, a monotonic convergent iterative reweighted least-squares (IRLS) algorithm, called the iterative majorization approach, is introduced to solve the WLS problem. Simulation results show that the proposed algorithm offers improved robustness against nonuniform noise and observation outliers over traditional algorithms.

Discrete search-based determination of a local orbital frame in unknown environments

A local orbital frame is an important and often applied coordinate frame in attitude and orbit control. Usually, a navigation filter estimates the position and velocity vector, which are used to build up this reference frame. It is beneficial for many space applications, such as communication or Earth observation missions, or for conducting orbit control maneuvers to change the orbit plane. Furthermore, satellite formation flying can require an orbital frame to describe relative motion. This work investigates the feasibility of determining the local-vertical local horizontal (LVLH) frame without full knowledge of the orbit. This scenario often applies to non-characterized small bodies like asteroids or comets. A discrete search method is used to determine the complete LVLH frame in real-time, assuming that one axis, a line-of-sight vector to the body’s center of mass, is known. By minimizing an objective function, the remaining axes can be determined. The estimated parameters are the inclination and the right ascension of the ascending node of the osculating orbit plane. This study investigates the feasibility of this concept and then applies it to perturbed orbits and sensor noise. The proposed method demonstrates that a discrete search can determine the LVLH frame. The approach can be seen as a step toward increasing the autonomy of a spacecraft near small bodies with unknown environments.

Towards laryngeal cancer diagnosis using Dandelion Optimizer Algorithm with ensemble learning on biomedical throat region images

Laryngeal cancer exhibits a notable global health burden, with later-stage detection contributing to a low mortality rate. Laryngeal cancer diagnosis on throat region images is a pivotal application of computer vision (CV) and medical image diagnoses in the medical sector. It includes detecting and analysing abnormal or cancerous tissue from the larynx, an integral part of the vocal and respiratory systems. The computer-aided system makes use of artificial intelligence (AI) through deep learning (DL) and machine learning (ML) models, including convolution neural networks (CNN), for automated disease diagnoses and detection. Various DL and ML approaches are executed to categorize the extraction feature as healthy and cancerous tissues. This article introduces an automated Laryngeal Cancer Diagnosis using the Dandelion Optimizer Algorithm with Ensemble Learning (LCD-DOAEL) method on Biomedical Throat Region Image. The LCD-DOAEL method aims to investigate the images of the throat region for the presence of laryngeal cancer. In the LCD-DOAEL method, the Gaussian filtering (GF) approach is applied to eliminate the noise in the biomedical images. Besides, the complex and intrinsic feature patterns can be extracted by the MobileNetv2 model. Meanwhile, the DOA model carries out the hyperparameter selection of MobileNetV2 architecture. Finally, the ensemble of three classifiers such as bidirectional long short-term memory (BiLSTM), regularized extreme learning machine (ELM), and backpropagation neural network (BPNN) models, are utilized for the classification process. A comprehensive set of simulations is conducted on the biomedical image dataset to highlight the efficient performance of the LCD-DOAEL technique. The comparison analysis of the LCD-DOAEL method exhibited a superior accuracy outcome of 97.54% over other existing techniques.

MSFE-UIENet: A Multi-Scale Feature Extraction Network for Marine Underwater Image Enhancement

Underwater optical images have outstanding advantages for short-range underwater target detection tasks. However, owing to the limitations of special underwater imaging environments, underwater images often have several problems, such as noise interference, blur texture, low contrast, and color distortion. Marine underwater image enhancement addresses degraded underwater image quality caused by light absorption and scattering. This study introduces MSFE-UIENet, a high-performance network designed to improve image feature extraction, resulting in deep-learning-based underwater image enhancement, addressing the limitations of single convolution and upsampling/downsampling techniques. This network is designed to enhance the image quality in underwater settings by employing an encoder–decoder architecture. In response to the underwhelming enhancement performance caused by the conventional networks’ sole downsampling method, this study introduces a pyramid downsampling module that captures more intricate image features through multi-scale downsampling. Additionally, to augment the feature extraction capabilities of the network, an advanced feature extraction module was proposed to capture detailed information from underwater images. Furthermore, to optimize the network’s gradient flow, forward and backward branches were introduced to accelerate its convergence rate and improve stability. Experimental validation using underwater image datasets indicated that the proposed network effectively enhances underwater image quality, effectively preserving image details and noise suppression across various underwater environments.

Comparison of adaptive imaging receiver coil and traditional coil for multiplexed sensitivity encoding diffusion-weighted imaging of the liver.

To compare the image quality and efficacy of the adaptive imaging receiver (AIR) coil (GE Healthcare) and the traditional coil for multiplexed sensitivity encoding diffusion-weighted imaging (MUSE-DWI) in the detection of focal liver lesions (FLLs). Two groups of MUSE-DWI were obtained. Image quality was qualitatively evaluated by 3 independent blinded radiologists on a 5-point scale, and quantitative parameters were calculated by measurements of the region of interest in the liver and FLLs. McNemar's test were used to compare the characteristics and detectability. Less image noise, sharper contours, milder susceptibility artefacts, and better liver lesion conspicuity were found by all radiologists in 60 livers with 140 FLLs with the AIR coil than with the traditional coil (reader average mean, 4.3-4.4 vs. 3.7-4.0, P < .001). The signal-to-noise ratio (SNR) of the liver was significantly higher with the AIR coil than with the traditional coil (right lobe: mean, 8.89 vs.7.76, P < .05; left lobe: mean, 7.14 vs.6.19, P < .001), and the SNR of FLLs (mean, 24.62 vs. 21.01, P < .001) and lesion-to-liver CNR (mean, 16.61 vs. 14.02, P < .001) exhibited significant differences between the AIR coil and the traditional coil. Besides, superior detection of FLLs was observed with the AIR coil compared to the traditional coil (95.7% [134/140] vs. 85.7% [120/140], P < .001). The AIR coil yields less noise, fewer distortions, better lesion detectability, higher SNR of the liver and FLLs, and improved lesion-to-liver CNR during liver MUSE-DWI. Thus, it is a feasible and effective scanning scheme in liver MRI. The AIR coil improves SNR and the quality of liver MR imaging compared with the traditional coil.

Reducing thermal noise in high-resolution quantitative magnetic resonance imaging rotating frame relaxation mapping of the human brain at 3 T.

Quantitative maps of rotating frame relaxation (RFR) time constants are sensitive and useful magnetic resonance imaging tools with which to evaluate tissue integrity in vivo. However, to date, only moderate image resolutions of 1.6 x 1.6 x 3.6 mm3 have been used for whole-brain coverage RFR mapping in humans at 3 T. For more precise morphometrical examinations, higher spatial resolutions are desirable. Towards achieving the long-term goal of increasing the spatial resolution of RFR mapping without increasing scan times, we explore the use of the recently introduced Transform domain NOise Reduction with DIstribution Corrected principal component analysis (T-NORDIC) algorithm for thermal noise reduction. RFR acquisitions at 3 T were obtained from eight healthy participants (seven males and one female) aged 52 ± 20 years, including adiabatic T1ρ, T2ρ, and nonadiabatic Relaxation Along a Fictitious Field (RAFF) in the rotating frame of rank n = 4 (RAFF4) with both 1.6 x 1.6 x 3.6 mm3 and 1.25 x 1.25 x 2 mm3 image resolutions. We compared RFR values and their confidence intervals (CIs) obtained from fitting the denoised versus nondenoised images, at both voxel and regional levels separately for each resolution and RFR metric. The comparison of metrics obtained from denoised versus nondenoised images was performed with a two-sample paired t-test and statistical significance was set at pless than 0.05 after Bonferroni correction for multiple comparisons. The use of T-NORDIC on the RFR images prior to the fitting procedure decreases the uncertainty of parameter estimation (lower CIs) at both spatial resolutions. The effect was particularly prominent at high-spatial resolution for RAFF4. Moreover, T-NORDIC did not degrade map quality, and it had minimal impact on the RFR values. Denoising RFR images with T-NORDIC improves parameter estimation while preserving the image quality and accuracy of all RFR maps, ultimately enabling high-resolution RFR mapping in scan times that are suitable for clinical settings.

محاكاة مخططات التصفية المطبقة لتحسين الصور الفوتوغرافية

In signal processing, filters generally play a pivotal role to remove unwanted parts of the signal, such as random noise, or to extract useful parts of the signal, such as the components lying within a certain frequency range to enhance the performance and denoising scenario. This paper presents the implementation of linear and non-linear filtering schemes for noise reduction and images enhancement. This is achieved by performing convolution of a grayscale image with a mask filter of multiple sizes using MATLAB software. The average and median filters are implemented on the same image of an injected salt and pepper noise. In accordance with the findings, the nonlinear filters show more promising performance with a better peak-signal-noise ratio (PSNR) compared to the linear filters.

Denoising method for X-ray images with poisson-Gaussian noise based on a new threshold function and shearlet transform

BackgroundX-ray imaging has been applied in various fields. However, the noise in X-ray images reduces the image quality and affects subsequent detection. AimsA denoising method is developed to solve the problem of the Poisson-Gaussian mixed noise caused by X-ray imaging. Methodology: A new threshold function and an improved threshold are proposed in this paper. Furthermore, an improved denoising method, namely the improved generalized Anscombe with shearlet transform (improved GA-ST), is developed based on the above proposed algorithms. After theoretical derivation, experiments and parameter analysis, the proposed method is applied to actual X-ray images. ResultsThe results show that the new threshold function is continuous, asymptotic, and has no inherent deviation, which solves the problems existing in traditional threshold functions. In addition, the improved GA-ST method can reduce Poisson-Gaussian mixed noise at different levels. As for actual X-ray images, the improved GA-ST method outperforms the other methods, and the BRISQUE descent ratios all exceed 25%. ConclusionsThe improved GA-ST method proposed in this paper can effectively reduce the noise in X-ray images and meet the requirements of actual applications based on MATLAB platform.

Optimal Spectral Performance on Pediatric Photon-Counting CT: Investigating Phantom-Based Size-Dependent kV Selection for Spectral Body Imaging.

The comprehensive evaluation of kV selection on photon-counting computed tomography (PCCT) has yet to be performed. The aim of the study is to evaluate and determine the optimal kV options for variable pediatric body sizes on the PCCT unit. In this study, 4 phantoms of variable sizes were utilized to represent abdomens of newborn, 5-year-old, 10-year-old, and adult-sized pediatric patients. One solid water and 4 solid iodine inserts with known concentrations (2, 5, 10, and 15 mg I/mL) were inserted into phantoms. Each phantom setting was scanned on a PCCT system (Siemens Alpha) with 4 kV options (70 and 90 kV under Quantum Mode, 120 and 140 kV under QuantumPlus Mode) and clinical dual-source (3.0 pitch) protocol. For each phantom setting, radiation dose (CTDIvol) was determined by clinical dose settings and matched for all kV acquisitions. Sixty percent clinical dose images were also acquired. Reconstruction was matched across all acquisitions using Qr40 kernel and QIR level 3. Virtual monoenergetic images (VMIs) between 40 and 80 keV with 10 keV interval were generated on the scanner. Low-energy and high-energy images were reconstructed from each scan and subsequently used to generate an iodine map (IM) using an image-based 2-material decomposition method. Image noise of VMIs from each kV acquisition was calculated and compared between kV options. Absolute percent error (APE) of iodine CT number accuracy in VMIs was calculated and compared. Root mean square error (RMSE) and bias of iodine quantification from IMs were compared across kV options. At the newborn size and 50 keV VMI, noise is lower at low kV acquisitions (70 kV: 10.5 HU, 90 kV: 10.4 HU), compared with high kV acquisitions (120 kV: 13.8 HU, 140 kV: 13.9 HU). At the newborn size and 70 keV VMI, the image noise from different kV options is comparable (9.4 HU for 70 kV, 8.9 HU for 90 kV, 9.7 HU for 120 kV, 10.2 HU for 140 kV). For APE of VMI, high kV (120 or 140 kV) performed overall better than low kV (70 or 90 kV). At the 5-year-old size, APE of 90 kV (median: 3.6%) is significantly higher (P < 0.001, Kruskal-Wallis rank sum test with Bonferroni correction) than 140 kV (median: 1.6%). At adult size, APE of 70 kV (median: 18.0%) is significantly higher (P < 0.0001, Kruskal-Wallis rank sum test with Bonferroni correction) than 120 kV (median: 1.4%) or 140 kV (median: 0.8%). The high kV also demonstrated lower RMSE and bias than the low kV across all controlled conditions. At 10-year-old size, RMSE and bias of 120 kV are 1.4 and 0.2 mg I/mL, whereas those from 70 kV are 1.9 and 0.8 mg I/mL. The high kV options (120 or 140 kV) on the PCCT unit demonstrated overall better performance than the low kV options (70 or 90 kV), in terms of image quality of VMIs and IMs. Our results recommend the use of high kV for general body imaging on the PCCT.

Hyperspectral image restoration using noise gradient and dual priors under mixed noise conditions

AbstractImages obtained from hyperspectral sensors provide information about the target area that extends beyond the visible portions of the electromagnetic spectrum. However, due to sensor limitations and imperfections during the image acquisition and transmission phases, noise is introduced into the acquired image, which can have a negative impact on downstream analyses such as classification, target tracking, and spectral unmixing. Noise in hyperspectral images (HSI) is modelled as a combination from several sources, including Gaussian/impulse noise, stripes, and deadlines. An HSI restoration method for such a mixed noise model is proposed. First, a joint optimisation framework is proposed for recovering hyperspectral data corrupted by mixed Gaussian‐impulse noise by estimating both the clean data as well as the sparse/impulse noise levels. Second, a hyper‐Laplacian prior is used along both the spatial and spectral dimensions to express sparsity in clean image gradients. Third, to model the sparse nature of impulse noise, an ℓ1 − norm over the impulse noise gradient is used. Because the proposed methodology employs two distinct priors, the authors refer to it as the hyperspectral dual prior (HySpDualP) denoiser. To the best of authors' knowledge, this joint optimisation framework is the first attempt in this direction. To handle the non‐smooth and non‐convex nature of the general ℓp − norm‐based regularisation term, a generalised shrinkage/thresholding (GST) solver is employed. Finally, an efficient split‐Bregman approach is used to solve the resulting optimisation problem. Experimental results on synthetic data and real HSI datacube obtained from hyperspectral sensors demonstrate that the authors’ proposed model outperforms state‐of‐the‐art methods, both visually and in terms of various image quality assessment metrics.

Neural implicit surface reconstruction of freehand 3D ultrasound volume with geometric constraints

Three-dimensional (3D) freehand ultrasound (US) is a widely used imaging modality that allows non-invasive imaging of medical anatomy without radiation exposure. Surface reconstruction of US volume is vital to acquire the accurate anatomical structures needed for modeling, registration, and visualization. However, traditional methods cannot produce a high-quality surface due to image noise. Despite improvements in smoothness, continuity, and resolution from deep learning approaches, research on surface reconstruction in freehand 3D US is still limited. This study introduces FUNSR, a self-supervised neural implicit surface reconstruction method to learn signed distance functions (SDFs) from US volumes. In particular, FUNSR iteratively learns the SDFs by moving the 3D queries sampled around volumetric point clouds to approximate the surface, guided by two novel geometric constraints: sign consistency constraint and on-surface constraint with adversarial learning. Our approach has been thoroughly evaluated across four datasets to demonstrate its adaptability to various anatomical structures, including a hip phantom dataset, two vascular datasets and one publicly available prostate dataset. We also show that smooth and continuous representations greatly enhance the visual appearance of US data. Furthermore, we highlight the potential of our method to improve segmentation performance, and its robustness to noise distribution and motion perturbation.

Accurate extraction method of multi-laser stripes for stereo-vision based handheld scanners in complex circumstances

A technique for extracting multiline lasers is proposed for handheld scanner. First, a robust matching method for multi-lasers is presented in this work to distinguish every laser stripe in the left and right images by using back-projection algorithm. Then, an improved DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering method is promoted to detect noise in the laser images caused by complex scenarios. This approach is focused on the features of the laser stripes in the images, such as normal vectors, stripe width and gray values. These parameters are used as the criteria to find the noise in the images. After the calibration results of stereo-vision and laser-matching results are combined, the 3D information of the noise parts in the laser stripes are recovered accurately. Finally, a subpixel interpolation method based on Shepard interpolation is used to achieve the precise subpixel centerline extraction of laser stripes. And the experiments further valide the effectiveness of the proposed method.

AI-driven pseudo-light source for achieving high coherence and low speckle noise simultaneously in dual-wavelength digital holographic microscopy

Digital holography, a promising technology for optical imaging, is limited by the fundamental challenge of speckle noise when based on coherent lasers. These approaches often compromise either coherence, affecting surface depth measurement capabilities, or introduce excessive noise, degrading image clarity. To eliminate this trade-off, this study introduces a novel solution based on an AI-driven pseudo-light source that simultaneously achieves high coherence and low speckle noise in holographic imaging. Consisting of conditional generative adversarial networks, the AI model was trained on paired holograms from both highly coherent laser light and partially coherent quantum dot (QD)-based light sources to generate holograms that mimicked the low-noise characteristics of QD-based light while retaining the high coherence length of laser. The effectiveness of the pseudo-light source was validated through holographic observations on a reflective 8.0-µm-deep specimen. Compared to the laser, the AI-driven pseudo-light source achieved substantial improvement in interference pattern clarity and reduced speckle noise contrast from 0.602 to 0.0873. Moreover, the standard deviation of the surface depth distribution was notably reduced from 215.3 nm to 44.7 nm. Quantitative phase evaluations further confirmed the preservation of accurate phase information in the generated holograms, verifying the successful reconstruction of the three-dimensional specimen structure.

Effects of Auditory and Visual White Noise on Oculomotor Inhibition in Children With Attention-Deficit/Hyperactivity Disorder: Protocol for a Crossover Study.

In attention-deficit/hyperactivity disorder (ADHD), poor inhibitory control is one of the main characteristics, with oculomotor inhibition impairments being considered a potential biomarker of the disorder. While auditory white noise has demonstrated the ability to enhance working memory in this group, visual white noise is still unexplored and so are the effects of both types of white noise stimulation on oculomotor inhibition. This crossover study aims to explore the impact of auditory and visual white noise on oculomotor inhibition in children with ADHD and typically developing (TD) children. The study will investigate the impact of different noise levels (25% and 50% visual, 78 dB auditory), and performance will be evaluated both with and without noise stimulation. We hypothesize that exposure to white noise will improve performance in children with ADHD and impair the performance for TD children. Memory-guided saccades and prolonged fixations, known for their sensitivity in detecting oculomotor disinhibition in ADHD, will be used to assess performance. Children diagnosed with ADHD, withdrawing from medication for 24 hours, and TD children without psychiatric disorders were recruited for the study. Data collection was initiated in October 2023 and ended in February 2024. A total of 97 participants were enrolled, and the first results are expected between September and November 2024. This study will examine whether cross-modal sensory stimulation can enhance executive function, specifically eye movement control, in children with ADHD. In addition, the study will explore potential differences between auditory and visual noise effects in both groups. Our goal is to identify implications for understanding how noise can be used to improve cognitive performance. ClinicalTrials.gov NCT06057441; https://clinicaltrials.gov/study/NCT06057441. DERR1-10.2196/56388.