Field Imaging Research Articles

Generation and discrimination of autism MRI images based on autoencoder.

This study aims to explore an autoencoder-based method for generating brain MRI images of patients with Autism Spectrum Disorder (ASD) and non-ASD individuals, and to discriminate ASD based on the generated images. Initially, we introduce the research background of ASD and related work, as well as the application of deep learning in the field of medical imaging. Subsequently, we detail the architecture and training process of the proposed autoencoder model, and present the results of generating MRI images for ASD and non-ASD patients. Following this, we designed an ASD classifier based on the generated images and elucidated its structure and training methods. Finally, through analysis and discussion of experimental results, we validated the effectiveness of the proposed method and explored future research directions and potential clinical applications. This research offers new insights and methodologies for addressing challenges in ASD studies using deep learning technology, potentially contributing to the automated diagnosis and research of ASD.

Semi‐supervised contextual cognitive augmentation‐based cross‐teaching network for multiclass medical image segmentation

AbstractThe application of medical image segmentation technology enables accurate localization of human tissues, providing doctors with a reliable foundation for diagnosis. While deep learning methods have proven effective in this task, most current approaches rely on a single prediction framework, which overlooks Edge semantic features and results in flawed texture features. Moreover, existing supervised methods face challenges due to limited availability of high‐quality annotations in the field of medical imaging. In this article, a Semi‐supervised Contextual Cognitive Augmentation‐based Cross‐teaching Network is proposed. A Contextual Cognitive Enhancement Module is introduced consisting of two components: data augmentation and information extraction. The data augmentation component provides multi‐level data distribution by incorporating diverse perturbation strategies such as Discrete Cosine Transform and Gaussian noise. The information extraction component employs the Comprehensive Information Extraction module, which consists of Global Perception Information Extraction module and Multi‐channel Information Extraction module to extract perceptual information from images and enhance interaction between image channels, respectively. Additionally, a cross‐teaching strategy is adopted and a hybrid loss function is utilized to encourage knowledge sharing among the networks, leveraging the advantages of dual networks for improved performance. Experimental results demonstrate significant enhancements in multiclass medical image segmentation compared to several state‐of‐the‐art single‐framework networks.

Progression in Near-Infrared Fluorescence Imaging Technology for Lung Cancer Management.

Lung cancer is a major threat to human health and a leading cause of death. Accurate localization of tumors in vivo is crucial for subsequent treatment. In recent years, fluorescent imaging technology has become a focal point in tumor diagnosis and treatment due to its high sensitivity, strong selectivity, non-invasiveness, and multifunctionality. Molecular probes-based fluorescent imaging not only enables real-time in vivo imaging through fluorescence signals but also integrates therapeutic functions, drug screening, and efficacy monitoring to facilitate comprehensive diagnosis and treatment. Among them, near-infrared (NIR) fluorescence imaging is particularly prominent due to its improved in vivo imaging effect. This trend toward multifunctionality is a significant aspect of the future advancement of fluorescent imaging technology. In the past years, great progress has been made in the field of NIR fluorescence imaging for lung cancer management, as well as the emergence of new problems and challenges. This paper generally summarizes the application of NIR fluorescence imaging technology in these areas in the past five years, including the design, detection principles, and clinical applications, with the aim of advancing more efficient NIR fluorescence imaging technologies to enhance the accuracy of tumor diagnosis and treatment.

Improving rail transit security with enhanced YOLOv5 obstacle detection

Abstract As the economy advances and rail transit technology progresses, rail transit plays an important role in alleviating urban traffic pressure and promoting regional economic development. However, railway safety faces potential hazards such as foreign object intrusion in the trackside environment, seriously threatening the safety of trains and passengers. Therefore, to achieve high-performance recognition of obstacle target images in rail transit, a small sample object detection algorithm YOLOv5-RTO, which using EVC (Explicit Visual Center) attention mechanism and lightweight up-sampling operator CARAFE (Content-Aware ReAssembly of FEatures) to improve YOLOv5, has been proposed. Based on the self-collected image data of rail transit obstacles, a dense and time-varying dataset was created within the rail transit system, and data augmentation methods were used to reduce the impact of small sample datasets on the detection performance of subsequent algorithm. Using this dataset, target detection tests were conducted on rail transit obstacle images to analyze the actual performance of the training model. The experiment results indicate that the enhanced YOLOv5-RTO algorithm integrates the advantages of EVC and CARAFE while preserving the lightweight and accurate characteristics of YOLOv5. The improved algorithm scheme has an average inference time of 1.3 ms per frame in the field of rail transit images, achieving an overall mAP of 96.5%, an increase of 1.8% compared to the original algorithm. It has a certain engineering application value and provides reference for further optimizing the safety system of urban rail transit.

Photon-counting detector computed tomography : Paradigm shift in cardiac CT imaging

The introduction of photon-counting detector computed tomography (PCD-CT) heralds anew generation of cardiac imaging. This review discusses the current scientific literature to determine the incremental value of PCD-CT in cardiac imaging. Discussion of currently available literature regarding cardiac PCD-CT from aradiological perspective. Since its market introduction in 2021, numerous studies have explored the advantages of this new technology in the field of cardiac imaging, including improved image quality through superior spatial resolution, ahigher contrast-to-noise ratio, reduced artifacts, and lower radiation dose. While preliminary studies have been promising, it remains to be seen how the advantages of PCD-CT will affect clinical guidelines for cardiac CT.

Study on Photon Beam Imaging Properties of CsPbBr3-PP Scintillator: Monte Carlo Simulation Approach

Perovskite scintillators have garnered significant interest in the realm of gamma-ray imaging in the past few years. Here, a comprehensive investigation into the gamma-ray imaging properties of CsPbBr3-PP composite scintillators is presented, wherein the Monte Carlo simulation approach offered by Geant4 is utilized. The primary focus is on the point spread function and modulation transfer function of the material, elucidating the nuanced interactions between gamma-ray energy, scintillator thickness, and their resultant imaging capabilities. A key aspect of this study is the exploration of the nonlinear and inverse effect of scintillator thickness and the energy of the photon beam on the imaging quality, highlighting the trade-offs between energy deposition and image resolution. This research underscores the importance of optimizing the scintillator design to balance these factors, catering to specific applications in high-energy detection and imaging. This work not only contributes significantly to the field of material sciences and radiographic imaging, but also provides practical insights for the development of more effective scintillator-based detectors. The findings of this study have broad implications for the design and application of perovskite scintillators in various high-tech industries and scientific research.

Application Exploration of the Photon Chip Technology in Real time Processing of Biomedical Images

In the biomedical field, photonic chip technology is gradually becoming the key to improving the speed and accuracy of medical imaging. The development of this technology has given us new hope in early disease diagnosis and cell monitoring. Recent research has not only sorted out the hotspots and future trends of biomedical photonics, but also paid special attention to the application of photon technology in these fields. Specifically, photonic chips have demonstrated their unique capabilities in multi-color two-photon excitation fluorescence microscopy imaging, two-photon fluorescence lifetime imaging, and two-photon fiber endoscopic imaging. These applications not only improve the clarity of imaging, but also accelerate processing speed, which is crucial for rapid diagnosis and treatment. At the end of the article, the author summarized the contributions of photonic chip technology in the field of biomedical imaging and provided prospects for future development directions.

Operando Spectroelectrochemical Imaging of Concentration Fields and Tafel Kinetics in Microfluidic Electrochemical Devices.

With the development of microtechnologies for energy conversion and storage, mass transfer and micromolar concentration variations need to be measured at the microscale. These advances need to be accompanied by novel imaging techniques with the capability of achieving high spatial resolution while detecting very small signal variations (less than 0.1%). Thus, in this study, a new microscopy technique is proposed based on a combination of electrochemical impedance spectroscopy (EIS) and visible imaging spectroscopy to measure the concentration fields at the micromolar scale in operando microfluidic fuel cells (MFCs). This technique exploits EIS modulation and Fourier analysis to reduce the noise during concentration field imaging. A mass transfer model in the periodic regime is derived to validate the measurements and to estimate the Tafel kinetics and mass diffusivities during potassium permanganate reduction from only one potential measurement. The proposed imaging method and mathematical framework presented in this study can be used to study binary electrochemical reactions without gas production.

Evaluating Explainable Artificial Intelligence (XAI) techniques in chest radiology imaging through a human-centered Lens.

The field of radiology imaging has experienced a remarkable increase in using of deep learning (DL) algorithms to support diagnostic and treatment decisions. This rise has led to the development of Explainable AI (XAI) system to improve the transparency and trust of complex DL methods. However, XAI systems face challenges in gaining acceptance within the healthcare sector, mainly due to technical hurdles in utilizing these systems in practice and the lack of human-centered evaluation/validation. In this study, we focus on visual XAI systems applied to DL-enabled diagnostic system in chest radiography. In particular, we conduct a user study to evaluate two prominent visual XAI techniques from the human perspective. To this end, we created two clinical scenarios for diagnosing pneumonia and COVID-19 using DL techniques applied to chest X-ray and CT scans. The achieved accuracy rates were 90% for pneumonia and 98% for COVID-19. Subsequently, we employed two well-known XAI methods, Grad-CAM (Gradient-weighted Class Activation Mapping) and LIME (Local Interpretable Model-agnostic Explanations), to generate visual explanations elucidating the AI decision-making process. The visual explainability results were shared through a user study, undergoing evaluation by medical professionals in terms of clinical relevance, coherency, and user trust. In general, participants expressed a positive perception of the use of XAI systems in chest radiography. However, there was a noticeable lack of awareness regarding their value and practical aspects. Regarding preferences, Grad-CAM showed superior performance over LIME in terms of coherency and trust, although concerns were raised about its clinical usability. Our findings highlight key user-driven explainability requirements, emphasizing the importance of multi-modal explainability and the necessity to increase awareness of XAI systems among medical practitioners. Inclusive design was also identified as a crucial need to ensure better alignment of these systems with user needs.

A deep learning-based spatial gradient reconstruction method for efficient damage identification in composite with high-sparsity Lamb wavefield

The structural integrity and safety of carbon fiber reinforced plastics (CFRP) are vulnerable to delamination, which is often imperceptible to the naked eye. Although the Scanning Laser Doppler Vibrometer (SLDV) has shown promise in damage quantification of CFRP, its time-consuming measurement process limits its application in engineering scenarios. To address this, we introduce a novel damage index, the spatial gradient, which captures the interaction between delamination and the wavefield. We have also developed a neural network capable of reconstructing the spatial gradient directly from high-sparsity Lamb wavefield data obtained at an extremely low spatial sampling rate, thereby significantly reducing measurement time. To enhance the network’s capability to detect wavefield anomalies, we employ the cross-attention technique, allowing for the direct injection of shallow features representing local wavefield distortions caused by damage into the decoder. Additionally, we integrate multiple reconstruction layers to guide the wavefield reconstruction process, ensuring meaningful information is captured at each stage. Our method achieves substantial improvements in reconstruction accuracy, increasing from 70 % to 92 % in single-damage scenario and from 14 % to 72 % in multi-damage scenario compared to the previous state-of-the-art techniques. By using the reconstructed spatial gradient field for damage imaging through spatial covariance analysis, our approach demonstrates its feasibility and generalizability across various damage locations. This suggests its potential as a reliable solution for fast and accurate damage characterization, reducing the measurement burden and enhancing practical applicability.

Three-dimensional polarimetric ptychography

A three-dimensional polarimetric ptychography (3D-PP) technique is proposed to reconstruct the residual stress distribution in optical elements in three dimensions by combining polarization imaging and a 3D ptychographic iterative engine (3D-PIE). 3D-PP mainly uses two orthogonal linear polarizers placed along the light direction to capture the photoelastic information within an optical element. Based on phase shift theory, a three-step phase shift method is proposed to record three dark field images, allowing for calculating the isometric line of residual stress within the optical element. This method enables the layer-by-layer measurement of residual stress within thick samples and assesses the distribution of residual stress along the detection axis, enhancing the precision of stress measurements.

In vivo volumetric analysis of retinal vascular hemodynamics in mice with spatio-temporal optical coherence tomography.

Microcirculation and neurovascular coupling are important parameters to study in neurological and neuro-ophthalmic conditions. As the retina shares many similarities with the cerebral cortex and is optically accessible, a special focus is directed to assessing the chorioretinal structure, microvasculature, and hemodynamics of mice, a vital animal model for vision and neuroscience research. We aim to introduce an optical imaging tool enabling in vivo volumetric mouse retinal monitoring of vascular hemodynamics with high temporal resolution. We translated the spatio-temporal optical coherence tomography (STOC-T) technique into the field of small animal imaging by designing a new optical system that could compensate for the mouse eye refractive error. We also developed post-processing algorithms, notably for the assessment of (i) localized hemodynamics from the analysis of pulse wave-induced Doppler artifact modulation and (ii) retinal tissue displacement from phase-sensitive measurements. We acquired high-quality, in vivo volumetric mouse retina images at a rate of 113Hz over a lateral field of view of . We presented high-resolution en face images of the retinal and choroidal structure and microvasculature from various layers, after digital aberration correction. We were able to measure the pulse wave velocity in capillaries of the outer plexiform layer with a mean speed of 0.35mm/s and identified venous and arterial pulsation frequency and phase delay. We quantified the modulation amplitudes of tissue displacement near major vessels (with peaks of 150nm), potentially carrying information about the biomechanical properties of the retinal layers involved. Last, we identified the delays between retinal displacements due to the passing of venous and arterial pulse waves. The developed STOC-T system provides insights into the hemodynamics of the mouse retina and choroid that could be beneficial in the study of neurovascular coupling and vasculature and flow speed anomalies in neurological and neuro-ophthalmic conditions.

Potential Field Imaging of Salt and Basement Structures in the Southern Zagros Foreland Basin

Potential Field Imaging of Salt and Basement Structures in the Southern Zagros Foreland Basin

A review of deep learning approaches for multimodal image segmentation of liver cancer.

This review examines the recent developments in deep learning (DL) techniques applied to multimodal fusion image segmentation for liver cancer. Hepatocellular carcinoma is a highly dangerous malignant tumor that requires accurate image segmentation for effective treatment and disease monitoring. Multimodal image fusion has the potential to offer more comprehensive information and more precise segmentation, and DL techniques have achieved remarkable progress in this domain. This paper starts with an introduction to liver cancer, then explains the preprocessing and fusion methods for multimodal images, then explores the application of DL methods in this area. Various DL architectures such as convolutional neural networks (CNN) and U-Net are discussed and their benefits in multimodal image fusion segmentation. Furthermore, various evaluation metrics and datasets currently used to measure the performance of segmentation models are reviewed. While reviewing the progress, the challenges of current research, such as data imbalance, model generalization, and model interpretability, are emphasized and future research directions are suggested. The application of DL in multimodal image segmentation for liver cancer is transforming the field of medical imaging and is expected to further enhance the accuracy and efficiency of clinical decision making. This review provides useful insights and guidance for medical practitioners.

DC voltage switching-based octuple-electrode microdevice for cell rotation and area-specific membrane capacitance measurement

Single-cell electrorotation plays an important role in the field of single-cell imaging and electric parameter measurement. However, reported cell rotation technology often adopts a quadruple-electrode structure and is excited by an AC signal. The distribution of electric field strength in the enclosed area is not uniform, and the rotation speed of the cells is related to the location in the area, so it is difficult to achieve uniformity of electric field distribution and the stationarity of rotation. This work proposes a DC voltage switching-based octuple-electrode microdevice for cell rotation and area-specific membrane capacitance measurements. This design can switch the DC voltages on each electrode periodically to produce a uniformly distributed rotating electric field. The rotation direction of the electric field can be realized by simply controlling the switching order of the analog switches. According to the theoretical single-cell model, the area-specific membrane capacitance of cells are determined through rotation movements. Simultaneously, based on simulation results, the rotation area is normalized to enhance the accuracy of the measuring electrical parameters. This study demonstrates the potential application of the proposed octuple-electrode DC voltage-based electro-rotation device for rapid, convenient, and cost-effective manipulation and electrical parameter measurement of single cells.

1-arcsecond imaging of the ELAIS-N1 field at 144MHz using the LoTSS survey with the international LOFAR telescope

We present the first wide area ($2.5 deg ^2$) LOFAR high band antenna image at a resolution of $1.2 with a median noise of $ Jy beam $. It was made from an 8-hour International LOFAR Telescope (ILT) observation of the ELAIS-N1 field at frequencies ranging from 120 to 168\,MHz with the most up-to-date ILT imaging methods. This intermediate resolution falls between the highest possible resolution (0.3 achievable by using all ILT baselines and the standard 6-arcsecond resolution in the LOFAR Two-meter Sky Survey (LoTSS) image products utilising the LOFAR Dutch baselines only. This is the first demonstration of the feasibility of imaging using the ILT at a resolution of sim 1 which provides unique information on source morphology at scales that fall below the surface brightness limits at higher resolutions. The total calibration and imaging computational time is approximately 52,000 core hours, which is nearly five times more than required to produce a $6 resolution image. We also present a radio source catalogue containing 2263 sources detected over the $2.5 deg ^2$ image of the ELAIS-N1 field, with a peak intensity threshold of $5.5 The catalogue has been cross-matched with the LoTSS deep ELAIS-N1 field radio catalogue, and its flux density and positional accuracy have been investigated and corrected accordingly. We find that sim 80<!PCT!> of sources that we expect to be detectable based on their peak brightness in the LoTSS $6 resolution image are detected in this image, which is approximately a factor of two higher than for $0.3 resolution imaging in the Lockman Hole, implying there is a wealth of information on these intermediate scales.

Toward widespread use of virtual trials in medical imaging innovation and regulatory science.

The rapid advancement in the field of medical imaging presents a challenge in keeping up to date with the necessary objective evaluations and optimizations for safe and effective use in clinical settings. These evaluations are traditionally done using clinical imaging trials, which while effective, pose several limitations including high costs, ethical considerations for repetitive experiments, time constraints, and lack of ground truth. To tackle these issues, virtual trials (aka in silico trials) have emerged as a promising alternative, using computational models of human subjects and imaging devices, and observer models/analysis to carry out experiments. To facilitate the widespread use of virtual trials within the medical imaging research community, a major need is to establish a common consensus framework that all can use. Based on the ongoing efforts of an AAPM Task Group (TG387), this article provides a comprehensive overview of the requirements for establishing virtual imaging trial frameworks, paving the way toward their widespread use within the medical imaging research community. These requirements include credibility, reproducibility, and accessibility. Credibility assessment involves verification, validation, uncertainty quantification, and sensitivity analysis, ensuring the accuracy and realism of computational models. A proper credibility assessment requires a clear context of use and the questions that the study is intended to objectively answer. For reproducibility and accessibility, this article highlights the need for detailed documentation, user-friendly software packages, and standard input/output formats. Challenges in data and software sharing, including proprietary data and inconsistent file formats, are discussed. Recommended solutions to enhance accessibility include containerized environments and data-sharing hubs, along with following standards such as CDISC (Clinical Data Interchange Standards Consortium). By addressing challenges associated with credibility, reproducibility, and accessibility, virtual imaging trials can be positioned as a powerful and inclusive resource, advancing medical imaging innovation and regulatory science.

Detection of Brain Tumor using Medical Images: A Comparative Study of Machine Learning Algorithms – A Systematic Literature Review

Abstract: Background: Brain tumors are a significant global health concern impacting both adults and children. Tumors are characterized by abnormal or excessive growth resulting from uncontrolled cell division. Diagnosing brain tumors poses various challenges, including limited funding, a shortage of qualified professionals, and insufficient access to medical facilities in remote regions. Different learning techniques for detecting brain tumors have been developed due to their ease of use, cost-effectiveness, and non-invasive nature, in contrast to other invasive methods. Methods: This research conducts a systematic literature review to explore modern trends and concepts of machine learning in healthcare, aiming to identify effective techniques for brain tumor detection. It also compares and analyzes the most efficient machine learning methods currently in use, focusing on aspects such as machine learning algorithms, image augmentation, evaluation metrics, and the sizes of datasets employed. Results: The findings indicate that non-invasive methods, such as machine learning algorithms for brain tumor detection, are cost-effective and provide quick results. Conclusions: This systematic literature review offers a technical overview, demonstrating the efficiency and effectiveness of machine learning techniques in making brain tumor detection feasible. The study utilizes deep learning and machine learning methods to comprehensively analyse diagnosis, imaging, and clinical evaluations in medical fields related to brain tumor detection.

Enhancing circular dichroism with anisotropic heterogeneous-structure based on MQBIC resonance

Metasurfaces based on quasi-bound states in the continuum (QBIC) typically require careful tuning of structural parameters to meet critical coupling condition, thereby maximizing circular dichroism (CD). In this study, we propose an innovative heterogeneous-structure comprising four pairs of symmetry-breaking silicon dielectric rods embedded in an anisotropic polymer. The resonance of magnetic QBIC (MQBIC) within the dielectric rods generates a CD response, while the polymer effectively modulates the chirality of the heterogeneous-structure and enhances the CD. This enhancement occurs because the resonance of the MQBIC in the diagonal direction of the heterogeneous-structure is confined, leading to a break in mirror symmetry. Further investigations reveal that even when critical coupling condition are not satisfied, CD can still be maximized through rotational modulation. Additionally, the critical coupling condition for the resonance of MQBIC in the heterogeneous-structure are more readily achievable, allowing the CD response to remain unconstrained by lattice constants. This research is anticipated to have a significant impact on the fields of biosensing and medical imaging in the future.

Imaging in France: 2024 Update.

Radiology in France has made major advances in recent years through innovations in research and clinical practice. French institutions have developed innovative imaging techniques and artificial intelligence applications in the field of diagnostic imaging and interventional radiology. These include, but are not limited to, a more precise diagnosis of cancer and other diseases, research in dual-energy and photon-counting computed tomography, new applications of artificial intelligence, and advanced treatments in the field of interventional radiology. This article aims to explore the major research initiatives and technological advances that are shaping the landscape of radiology in France. By highlighting key contributions in diagnostic imaging, artificial intelligence, and interventional radiology, we provide a comprehensive overview of how these innovations are improving patient outcomes, enhancing diagnostic accuracy, and expanding the possibilities for minimally invasive therapies. As the field continues to evolve, France's position at the forefront of radiological research ensures that these innovations will play a central role in addressing current healthcare challenges and improving patient care on a global scale.