High-accuracy Image Research Articles

Predictive coding compressive sensing optical coherence tomography hardware implementation

Compressed sensing (CS) is an approach that enables comprehensive imaging by reducing both imaging time and data density, and is a theory that enables undersampling far below the Nyquist sampling rate and guarantees high-accuracy image recovery. Prior efforts in the literature have focused on demonstrations of synthetic undersampling and reconstructions enabled by compressed sensing. In this paper, we demonstrate the first physical, hardware-based sub-Nyquist sampling with a galvanometer-based OCT system with subsequent reconstruction enabled by compressed sensing. Acquired images of a variety of samples, with volume scanning time reduced by 89% (12.5% compression rate), were successfully reconstructed with relative error (RE) of less than 20% and mean square error (MSE) of around 1%.

Virtual source total focusing method for surface defects using leaky Rayleigh waves

Leaky Rayleigh waves are sensitive to surface defects and beneficial for automated detection due to their non-contact characteristics. However, the amplitude of the leaky Rayleigh waves is low due to the attenuation of waveform conversion and acoustic waves propagation, which limits the imaging quality for long-distance detection. This study introduces a novel method combining leaky Rayleigh waves detection with virtual source total focusing method. The virtual source (VS) technology enhances the emission energy of leaky Rayleigh waves. Then, the total focusing method (TFM) is applied to acquire high-accuracy images of defects. To reduce noise and artifacts, a weighting function based on the coherence factor (CF) is developed to weight the TFM superimposed signals, thereby achieving high-quality image reconstruction. Experimental results show that compared with the conventional TFM method, the proposed method can effectively improve the amplitude of imaging signals while reducing system noise and imaging artifacts. The lateral accuracy of defects is improved, with the average lateral error of defect size being 0.178 mm. The signal-to-noise ratio (SNR) of ultrasound images is increased by 27.59 dB, and the array performance index (API) of ultrasound images is decreased by 33.78 %. The proposed method provides a new and effective approach for quantitatively assessing the surface defects of metal components using leaky Rayleigh waves.

Convolutional neural network for high-performance reservoir computing using dynamic memristors

In the rapidly advancing field of neuromorphic computing, W/ZnO/TiN resistive random-access memory (RRAM) devices have emerged as a next-generation computational building block. Our findings reveal the significant role played by the thickness of the ZnO layer in determining the electrical properties essential for data storage and neuromorphic applications. The short-term memory (STM) capabilities, which are critical for processing temporal information, are closely examined alongside their potential to simulate biological synaptic functions through multilevel conductance states and synaptic behaviors such as paired-pulse facilitation. Integrating these devices into reservoir computing systems enhances pattern recognition and accelerates learning, which demonstrates their utility in sequential data processing. In addition, conductance modulation via pulse width adjustment is a novel strategy to optimize memory device performance. By showcasing the effectiveness of W/ZnO/TiN devices in neuromorphic computing through high-accuracy image recognition tasks, our study highlights their foundational role in advancing neuromorphic computing technologies. The adaptability, learning capabilities, and efficiency of these devices underscore their potential for developing hardware-based neuromorphic systems that are capable of complex data processing.

Sensitivity Matrix Update Method for Electrical Resistance Tomography Based on Error-Constrained Cross Fusion Residual Attention Network

Abstract Electrical resistance tomography (ERT) is an imaging technique of conductivity distribution. The image reconstruction algorithm based on the empty field sensitivity matrix is widely used because it provides an approximate solution to the complex ERT inverse problems. However, due to the soft field effect, the sensitivity matrix changes with the media distribution, leading to significant errors in image reconstruction. To address this issue, an error-constrained deep learning scheme for bubble flow, namely cross fusion residual attention network (CFRA-Net) is designed to update the sensitivity matrix during gas-liquid-solid fluidized bed operation. CFRA-Net employs a multi-head self-attention mechanism to enhance bubble features in boundary measurements, a multilevel cross fusion module to fuse empty field strength and boundary measurement information, and a novel serial-channel residual spatial attention block to boost the feature extraction capability of the model. A weighted loss function is designed to give more weight to the gas phase distribution region. Additionally, this paper establishes a dataset for gas-liquid-solid three-phase flow, considering background field conductivity variations. Validation and reliability of the proposed method are assessed through ablation, comparison, and noise experiments. The experimental results demonstrate that CFRA-Net achieves rapid updates of the sensitivity matrix, high imaging accuracy, and robust noise immunity.

Near- and remote-field pulsed eddy current integrated testing for enhancement in imaging of buried flaws in layered conductive structures

ABSTRACT In a bid to simultaneously exploit the advantages of Pulsed Eddy Current testing (PEC) and Pulsed Remote-field Eddy Current Testing (PRFEC) for evaluation of buried defects in layered conductors, in this paper the Near- and Remote-field Pulsed Eddy Current integrated testing (NRPEC) alongside an axi-symmetric probe has been proposed. Simulations are conducted to reveal the field characteristics underlying the proposed method and analyse the signal response to hidden flaws. In parallel, an NRPEC system has been built up. Based on the images of near-field and remote-field signal features, a fusion algorithm is proposed to fuse the signal features of near-field and remote-field signals for defect testing with the enhanced signal-to-noise ratio. Results from simulations and experiments indicate that based on the fused signal feature, high-accuracy imaging of hidden defects in a double-layer conductor can be achieved via NRPEC with the improved signal-to-noise ratio.

Image-domain least-squares imaging in an elastic vertically transversely isotropic medium: Multiparameter point-spread function deconvolution

By solving a linear inverse problem, least-squares migration (LSM) can provide higher-precision images of complex subsurface structures than traditional adjoint-operator-based migration. LSM can be conducted in the data domain and the image domain (IDLSM). IDLSM in acoustic media has been extensively studied, commonly using single-parameter point-spread function (PSF) deconvolution. For high-accuracy imaging of complex media, we develop an image-domain elastic least-squares imaging method using multiparameter PSF deconvolution for vertically transversely isotropic (VTI) media. The multiparameter PSFs, including P-P, P-S, S-P, and S-S components, are calculated on a sparse grid using elastic VTI wave equation Born modeling and migration. The traditional migration result is then decomposed into individual local results, each corresponding to the size of a single PSF, through unit partitioning. Finally, a set of filters is derived from four elastic VTI PSF components to approximate the Hessian inverse, which are subsequently assembled to reconstruct an entire deconvolved image. The numerical experiments confirm that our method can significantly decrease multiparameter crosstalk artifacts and improve spatial resolution and amplitude fidelity.

Handwritten Digital Signatures Accuracy Enhancement Comparison on Android-Based Mobile Application Systems

The research reported in this paper aimed to improve the quality, size ratio, and storage size of Digital Signatures (DS) used in the e-form Fumida app, a mobile application employed by PT Fumida Pestindo Jaya (Fumida) pest control, termite control, and fumigation services for data collection and verification. The application was developed using MADLC method for research and development of mobile application and utilized as a replacement for paper media used to fill in data related to work with the DS feature (e-signature), from which verification/authentication of the work results carried out by Fumida to its clients is given. The original DS, while replacing paper forms, suffered quality and accuracy loss when resized for printing, prompting the investigation of three methods to enhance their integrity. This process was subsequently examined using a questionnaire with the help of 22 respondents and calculation of questionnaire data. The results of our study showed that our model 3 emerged as the most effective solution, maintaining high image quality and accuracy even when resized.

Multitasking optimization for the imaging problem in electrical capacitance tomography

Electrical capacitance tomography excels in measuring multiphase flows, but it faces challenges in reconstructing high-accuracy images. To tap into the potential of this measurement technique, we model the image reconstruction problem as a bilevel multitasking optimization problem, aiming to enhance imaging quality via effective knowledge transfer and integration across various tasks. This new imaging model consists of a target optimization problem and an auxiliary optimization problem, facilitating knowledge transfer and fusion between tasks. We design a new algorithm to solve the target optimization problem and integrate it with an autoencoder used for the cross-task knowledge transfer, forming a new multitasking optimizer to solve this proposed bilevel multitasking optimization imaging model. The extreme learning machine is developed for the prediction of learnable prior images by introducing a novel bilevel optimization framework that enables concurrent parameter selection and model training. A novel nested algorithm, designed to efficiently address optimization problems structured hierarchically, is developed to solve the training model. Evaluation results show that the new imaging algorithm outperforms popular imaging methods in terms of reconstruction accuracy and robustness, while also demonstrating stable performance. This proposed imaging algorithm achieves cross-task knowledge transfer and fusion, integrates measurement physics and machine learning, mines and integrates effective image priors, adaptively learns model parameters, alleviates the ill-posed property of the image reconstruction problem, increase the automation of the model, and improves imaging quality, robustness and performance stability, which provides a comprehensive solution to mitigate challenges in imaging.

Removing Artifacts in Transcranial Photoacoustic Imaging With Polarized Self-Attention Dense-UNet

ObjectivePhotoacoustic imaging (PAI) is a promising transcranial imaging technique. However, the distortion of photoacoustic signals induced by the skull significantly influences its imaging quality. We aimed to use deep learning for removing artifacts in PAI. MethodsIn this study, we propose a polarized self-attention dense U-Net, termed PSAD-UNet, to correct the distortion and accurately recover imaged objects beneath bone plates. To evaluate the performance of the proposed method, a series of experiments was performed using a custom-built PAI system. ResultsThe experimental results showed that the proposed PSAD-UNet method could effectively implement transcranial PAI through a one- or two-layer bone plate. Compared with the conventional delay-and-sum and classical U-Net methods, PSAD-UNet can diminish the influence of bone plates and provide high-quality PAI results in terms of structural similarity and peak signal-to-noise ratio. The 3-D experimental results further confirm the feasibility of PSAD-UNet in 3-D transcranial imaging. ConclusionPSAD-UNet paves the way for implementing transcranial PAI with high imaging accuracy, which reveals broad application prospects in preclinical and clinical fields.

Structured-Light 3D Imaging Based on Vector Iterative Fourier Transform Algorithm.

Quasi-continuous-phase metasurfaces overcome the side effects imposed by high-order diffraction on imaging and can impart optical parameters such as amplitude, phase, polarization, and frequency to incident light at sub-wavelength scales with high efficiency. Structured-light three-dimensional (3D) imaging is a hot topic in the field of 3D imaging because of its advantages of low computation cost, high imaging accuracy, fast imaging speed, and cost-effectiveness. Structured-light 3D imaging requires uniform diffractive optical elements (DOEs), which could be realized by quasi-continuous-phase metasurfaces. In this paper, we design a quasi-continuous-phase metasurface beam splitter through a vector iterative Fourier transform algorithm and utilize this device to realize structured-light 3D imaging of a target object with subsequent target reconstruction. A structured-light 3D imaging system is then experimentally implemented by combining the fabricated quasi-continuous-phase metasurface illuminated by the vertical-cavity surface-emitting laser and a binocular recognition system, which eventually provides a new technological path for the 3D imaging field.

High-Accuracy Image Segmentation Based on Hybrid Attention Mechanism for Sandstone Analysis

Mineral image segmentation based on computer vision is vital to realize automatic mineral analysis. However, current image segmentation methods still cannot effectively solve the problem of sandstone grains that are adjoined and concealed by leaching processes, and the segmentation performance of small and irregular grains still needs to be improved. This investigation explores and designs a Mask R-CNN-based sandstone image segmentation model, including a hybrid attention mechanism, loss function construction, and receptive field enlargement. Simultaneously, we propose a high-quality sandstone dataset with abundant labels named SMISD to facilitate comprehensive training of the model. The experimental results show that the proposed segmentation model has excellent segmentation performance, effectively solving adhesion and overlap between adjacent grains without affecting the classification accuracy. The model has comparable performance to other models on the COCO dataset, and performs better on SMISD than others.

High-accuracy ultrasonic imaging of the defects in the complex-surface components by the search-vector imaging condition

Complex-surface components play more and more significant roles in engineering practice because of the development of manufacturing technology and requirements from various industries. Void and crack defects inside the components greatly deteriorate the mechanical properties, and ultrasonic full-matrix imaging is a promising method to detect the defects. However, the research on high-quality full-matrix imaging for complex-surface components is limited. In this paper, a full-matrix imaging method based on the search-vector imaging condition is proposed to detect the defects in complex-surface components with high accuracy. Herein, the search-vector imaging condition is established based on waveform inversion, which employs the first-order derivative vector and approximated Hessian matrix to obtain the high-accuracy images of defects. The proposed imaging condition does not make assumptions about the wavefield and is more rigorous than the conventional excitation-time imaging condition. Furthermore, the proposed method can highlight defects throughout the measurement area, so it is very convenient for locating defects in large measurement structures. In the simulation and experiments, defects in complex sound speed models are the measurement targets, and the proposed method has significantly improved the imaging accuracy, resolution and signal-to-noise ratio (SNR) in all cases compared with the conventional methods, where all the void and crack defects are highlighted clearly in the whole image. In addition, the proposed method is not sensitive to the influencing factors and has strong robustness. Therefore, the proposed method has great potential in defect detection for complex-surface components in various fields.

Multiply Complementary Priors for Image Compressive Sensing Reconstruction in Impulsive Noise

Impulsive noise is always present in real-world image Compressive Sensing (CS) acquisition systems, where existing CS reconstruction performance may seriously deteriorate. In this article, we propose a robust CS formulation for image reconstruction to suppress outliers in the presence of impulsive noise. To address this issue, we consider a novel truncated-Cauchy loss function as the metric of residual error to elevate the reconstruction robustness. Specifically, we design a complementary priors model to incorporate nonconvex nonlocal low-rank prior and deep denoiser prior for high-accuracy image reconstruction. By means of the half-quadratic optimization theory and generalized soft-thresholding technique, we also develop an alternative optimization algorithm for solving the induced nonconvex optimization problem. Numerical simulations demonstrate the robustness and accuracy of the proposed robust CS method compared to some recent CS methods for image reconstruction in impulsive noise.

Research on reconstruction of diffraction tomography based on prior information

Under coherent light illumination, several approaches need either angle scanning or diffuser rotating to reconstruct the image through opaque scattering media. We propose a linear model to restore the hidden object through the actual power spectrum with disturbance of the scattering layer. The experimental results confirm that, the algorithm quickly converge to the only correct reconstruction solution with the accuracy power spectrum pattern of Fourier transform, and the method can reconstruct the high accuracy image of the object hidden by the scattering media with one-shot power spectrum.

Broadband and large-aperture metasurface edge encoders for incoherent infrared radiation.

The prevalence of computer vision systems necessitates hardware-based approaches to relieve the high computational demand of deep neural networks in resource-limited applications. One solution would be to off-load low-level image feature extraction, such as edge detection, from the digital network to the analog imaging system. To that end, this work demonstrates incoherent, broadband, low-noise optical edge detection of real-world scenes by combining the wavefront shaping of a 24-mm aperture metasurface with a refractive lens. An inverse design approach is used to optimize the metasurface for Laplacian-based edge detection across the 7.5- to 13.5-μm LWIR imaging band, allowing for facile integration with uncooled microbolometer-based LWIR imagers to encode edge information. A polarization multiplexed approach leveraging a birefringent metasurface is also demonstrated as a single-aperture implementation. This work could be applied to improve computer vision capabilities of resource-constrained systems by leveraging optical preprocessing to alleviate the computational requirements for high-accuracy image segmentation and classification.

HPB P41 The incidence and diagnostic potential of Extra-biliary findings on Magnetic Resonance Cholangiopancreatography

Abstract Background Magnetic Resonance Cholangiopancreatography (MRCP) is increasingly utilised to assess the biliary tree. This imaging modality can potentially reveal extra-biliary findings that will warrant further urgent investigations. The primary aim of this study was to assess the prevalence of extra-biliary findings on MRCPs. The secondary aim was to assess the need for subsequent investigations and subsequent outcomes. Methods This was a retrospective cohort study of all consecutive MRCPs performed on adult patients in a single centre form September 2021 to September 2022. Patient demographics, indication(s) for MRCP, MRCP result including extra-biliary findings were analysed from electronic case notes. Further analysis of necessary investigations into these extra-biliary findings and outcomes was also undertaken. This study was registered with Clinical Quality Project (LanQIP 14742). Results 495 MRCPs were performed in the study period; 305 (61.6%) were female; median age was 63 years (IQR 50-74). 311 (62.8%) were inpatient and 200 (40.4%) were urgent. 61.2% (n=303) had at least one extra-biliary finding and 8.1% (n=44) required urgent follow-up with 12 having a new diagnosis of malignancy. 19.4% (n=96) had renal cysts, 12.7% (n=63) had hepatic cysts, 5.6% (n=28) had pancreatic cysts, 2.8% (n=14) had liver cirrhosis, 2.8% (n=14) had peripancreatic collections, 3.4% (n=17) had a hiatus hernia, 7.3% (n=36) had pleural effusions, 5.3% (n=26) had splenomegaly and 1.8% (n=9) had identified diverticular disease. Conclusions Each of the extra-biliary findings on MRCP were confirmed with further investigation, which again highlights the high accuracy of Magnetic Resonance Imaging (MRI). It is undeniable that MRCPs are revealing extra-biliary findings which indicate that the diagnosis potential of MRCPs is underestimated. Although MRIs are widely used and has no ionising radiation unlike Computerised Tomography (CT) scans, they are still more costly. Furthermore, it was also noted in previous studies that incidental findings are largely dependent on the reporting radiologist and a structured proforma might be beneficial moving forward.

Optical properties reconstruction in nonhomogeneous participating medium based on an improved sequential quadratic programming

The reconstruction of optical parameters achieves functional imaging by providing characteristics closely related to the physiological state of biological tissues. With considerable research significance, this imaging technology has received increasing attention in the field of medical diagnosis and treatment. However, owing to the limited number of known measurement signals far less than the number of target parameters to be estimated, reconstruction requires a long ill-conditioned process, which is easily polluted by background noise so as to difficulties for achieving high imaging accuracy and efficiency. To conquer these deficiencies, an improved sequential quadratic programming (ISQP) is proposed in this work. In the ISQP, dual constraints of equality and inequality are added in the process of minimizing the objective functional, which avoids the time-consuming forward problem. Moreover, duality method, reduced Hessian matrix, and second-order correction are introduced in the subproblem. In addition, prior information of diagonal contiguous points is introduced to overcome the ill-posedness. The reconstruction performance of ISQP in different modes is discussed in depth. A time-frequency coupling model based on ISQP is constructed subsequently, of which the practical significance is demonstrated in the biological tissue-like medium model. The results show that in the established frequency domain model, ISQP significantly shortens the calculation time of the traditional method and improves the reconstruction quality of absorption parameters. The developed time-frequency coupling method effectively eliminates the influence of the background disturbance, and in the meantime improves the accuracy and efficiency when simultaneously estimating multi-parameters related to optics, with good robustness. The effectiveness of the ISQP combined with hybrid time-frequency model provides new referable ideas and application potential for the development and progress of medical, biology, photonics and other fields.

A rapid detection method of towed array seismic surface wave for leakage passage of dyke-dam

Rapid detection of leakage passage in dykes has become a committed step in flood prevention work. The seismic surface wave method can be given the S-wave velocity information of the near-surface (≤ 20 m), and qualitatively evaluate the transverse position, depth range, and direction of the leakage passage. The low acquisition efficiency of the cone geophone in the conventional point measurement mode does not meet the requirements of rapid detection. A single survey line has a small coverage area and lacks comparison with neighboring survey lines, so it is not possible to obtain the direction of extension of the leakage passage. In this paper, we propose a rapid detection method of the towed array seismic surface wave for leakage passage of a dyke-dam. Forty-eight gravity geophones are arranged in three columns, 16 in each column, to form the array geometry. Surface wave imaging methods are inversion and fast imaging. The main shortcomings of inversion calculation are (1) lacking shallow information, (2) low vertical resolution, and (3) multiple solutions. To overcome the above issues, the imaging calculation of the surface wave dispersion curve is implemented using fast imaging method. The proposed array geometry is used to collect seismic data. The simulated data and measured data show that fast imaging has the following advantages: (1) It can make full use of the dispersion curve of broadband seismic data, does not lose shallow information, and has high imaging accuracy. (2) It uses the dispersion curve directly for imaging, does not lose any details of velocity changes, and has a high vertical resolution. (3) The imaging results are unique. In contrast to the inversion results, the fast imaging results clearly distinguish the location of the leakage passage and highlight the lateral variability of the leakage passage. The results of the model data and measured data demonstrate the efficiency and high resolution of the towed array seismic geometry and the fast imaging method for leakage passage detection.

P17.10.B VOLUMETRIC DIFFERENCES IN TARGET DEFINITON OF RECURRENT HIGH-GRADE GLIOMAS USING CONTRAST ENHANCED MRI VS.[18F] FDOPA PET IN RADIOSURGICAL PLANNING

Abstract BACKGROUND Radiosurgical treatment of high-grade gliomas usually bases target definition on contrast-enhanced T1-weighted MRI images. Despite advances in several MRI techniques, a number of limitations exist, primarily due to the disruption of the blood-brain barrier, making the target delineation less accurate, particularly when treating recurrences. Several studies support the use of amino acid PET/MRI in distinguishing progression from post-operative/post-radiation inflammation. However, no established guidelines define the biological target volume when using amino acid PET/MRI in radiosurgical treatment planning. We aimed to explore the volumetric differences in target volumes obtained with standard contrast-enhanced MRI versus [18F]FDOPA PET in patients with recurrent high-grade gliomas (rHGG). MATERIAL AND METHODS Ten patients obtained [18F]FDOPA PET/MRI within 1 week before radiosurgical treatment. We co-registered these images with the 3D MPRAGE-MRI typically used for stereotactic target definition and performed a follow-up PET/MRI scan 4-6 weeks after treatment. We reviewed and compared the method of tumor delineation, target volumes, and stereotactic accuracy in both modalities. RESULTS Radiosurgical treatment delivers high radiation doses to defined targets. The target volume and the proximity to eloquent structures limit the dose, thus depending on high-accuracy imaging. The invasive nature of malignant brain tumors makes anatomical delineation insufficient, particularly when treating recurrences. In such cases, amino acid PET supplies additional information. However, the spatial resolution afforded by [18F]FDOPA uptake alone is insufficient for stereotactic definition, which to a large degree is based on contrast-enhanced T1-weighted MRI images in order to achieve the sub-millimeter resolution needed for radiosurgical planning. Hybrid PET/MRI allows the combination of metabolic information of [18F]FDOPA-PET with the anatomical precision of stereotactic MRI. In the recurrent setting, [18F]FDOPA-PET supplies the necessary information for targeting the biologically active tumour, differentiating it from radiation necrosis, thus limiting the target volumes. This enables us to treat the recurrent tumours more efficiently. CONCLUSION [18F]FDOPA-PET is a useful supplement in radiosurgical planning and treatment of rHGG. It is helpful in the delineation of the biologically active tumor, thus limiting the target volume significantly. This enables higher radiation doses and potentially more efficient treatment. Further results will be presented.

Improved sparse domain super-resolution reconstruction algorithm based on CMUT.

A novel breast ultrasound tomography system based on a circular array of capacitive micromechanical ultrasound transducers (CMUT) has broad application prospects. However, the images produced by this system are not suitable as input for the training phase of the super-resolution (SR) reconstruction algorithm. To solve the problem, this paper proposes an improved medical image super-resolution (MeSR) method based on the sparse domain. First, we use the simultaneous algebraic reconstruction technique (SART) with high imaging accuracy to reconstruct the image into a training image in a sparse domain model. Secondly, we denoise and enhance the contrast of the SART images to obtain improved detail images before training the dictionary. Then, we use the original detail image as the guide image to further process the improved detail image. Therefore, a high-precision dictionary was obtained during the testing phase and applied to filtered back projection SR reconstruction. We compared the proposed algorithm with previously reported algorithms in the Shepp Logan model and the model based on the CMUT background. The results showed significant improvements in peak signal-to-noise ratio, entropy, and average gradient compared to previously reported algorithms. The experimental results demonstrated that the proposed MeSR method can use noisy reconstructed images as input for the training phase of the SR algorithm and produce excellent visual effects.