Imaging Technology Research Articles

Integration of monitoring and operation ion source system in the DECY-13 cyclotron

Abstract In nuclear medicine, cyclotrons play a vital role as cyclic accelerators, producing radioisotopes for Positron Emission Tomography (PET) technology. Currently, the Accelerator Technology Research Center, National Research and Innovation Agency of Indonesia, is developing the DECY-13 cyclotron, which comprises several crucial components, including the ion source. The ion source generates ions that is accelerated to a specific energy level before being directed at the target. The ion source system of the DECY-13 cyclotron includes various supporting components, such as a cooling system, vacuum system, Magnet Power Supply (MPS), gas flow system, and cathode power supply. These components are currently operated independently, with manual monitoring by operators. To simplify and improve the operation, an integration project needed for supporting component of the DECY-13 cyclotron’s ion source. This integration utilizes LabVIEW software, the NI-9870 module, NI-9425 module, and NI-cRIO as the process operation controller. The successful development of the integration program ensures that the displayed data on the interface aligns seamlessly with data from each individual device. Consequently, the integration program serves as an effective tool for monitoring the operation of the DECY-13 cyclotron’s ion source.

Clinical Application of the 4K-3D Exoscope System in Cochlear Implantation.

To evaluate a system for otomicrosurgery based on 4K three-dimensional (3D) exoscope technology and apply it to cochlear implantation. An open stereoscopic vision-based surgical system, which differs from traditional surgical microscopes, was created by utilizing 4K stereo imaging technology and combining it with low-latency 4K ultra-high-definition 3D display. The system underwent evaluation based on 57 cochlear implantation operations, three designed microscopic manipulations, and a questionnaire survey. The surgical images displayed by the 4K-3D exoscope system (4K-3D-ES) are stereoscopic, clear, and smooth. The use of 4K-3D-ES in cochlear implantation is not inferior to traditional microscopes in terms of intraoperative bleeding and surgical complications, and the surgical duration is not slower or may even be faster than when using traditional microscopes. The results of micromanipulation experiments conducted on 16 students also confirmed this and demonstrated that 4K-3D-ES can be easily adapted. Furthermore, additional advantages of 4K-3D-ES were gathered. Significantly enlarged and high-definition stereoscopic images contribute to the visualization of finer anatomical microstructures such as chordae tympani, ensuring safer surgery. Users feel more comfortable in their necks, shoulders, waists, and backs. Real-time shared stereoscopic view for multiple people, convenient for collaboration and teaching. The ear endoscope and 4K-3D-ES enable seamless switching on the same screen. High-definition 3D images and videos can be saved with just one click, making future publication and communication convenient. The feasibility and safety of 4K-3D-ES for cochlear implantation surgery have been demonstrated. The 4K-3D-ES also offers numerous unique advantages and holds clinical application and promotional value.

Suppression of Subpixel Jitter in Resonant Scanning Systems With Phase-locked Sampling.

Resonant scanning is critical to high speed and in vivo imaging in many applications of laser scanning microscopy. However, resonant scanning suffers from well-known image artifacts due to scanner jitter, limiting adoption of high-speed imaging technologies. Here, we introduce a real-time, inexpensive and all electrical method to suppress jitter more than an order of magnitude below the diffraction limit that can be applied to most existing microscope systems with no software changes. By phase-locking imaging to the resonant scanner period, we demonstrate an 86% reduction in pixel jitter, a 15% improvement in point spread function with resonant scanning and show that this approach enables two widely used models of resonant scanners to achieve comparable accuracy to galvanometer scanners running two orders of magnitude slower. Finally, we demonstrate the versatility of this method by retrofitting a commercial two photon microscope and show that this approach enables significant quantitative and qualitative improvements in biological imaging.

Experimental Study on Surface Segregation Law of Three-Dimensional Accumulation Body of Sand and Stone Particles by Binary Mechanism

Sand and stone materials are one of the important raw materials for infrastructure construction. In recent years, natural sand resources have decreased rapidly. In order to protect rivers and DAMS and ecological balance, local governments have increased the control power of natural sand mining, and replacing natural sand with mechanically broken parent rock formation mechanism sand has become an inevitable trend of the industry development. During the crushing process of parent rock, sand particles of different particle size range are produced, and particle segregation occurs in the process of stacking in the yard, which brings great trouble to the calculation of particle grading and material sample detection, and hinders the large-scale application of machine-made sand. In this paper, three-dimensional stacking tests of irregular particles were carried out to identify the particle distribution on the surface of the stack by digital image technology, analyze the relationship between particle size composition of particle mixture and the particle distribution of the accumulation body, and study the distribution law and segregation characteristics of the accumulation body. The experimental results show that the mixture with different coarse particle content will produce the corresponding distribution on the surface of the accumulation body after the accumulation. On the surface of the accretion body, the content of coarse particles is vertically distributed along the height of the accretion body. The content of coarse particles in the lower part of the accretion body is greater than that in the upper part of the accretion body. The overall segregation coefficient on the surface of the pile decreases with the increase of coarse particle content, and the variation is the largest in the lower part of the pile surface with the initial coarse particle content. Based on the test results in this paper, it provides a basis for formulating the sampling and inspection method of machine-made sand, ensuring the quality of machine-made sand and improving the utilization rate of machine-made sand.

Image reconstruction of multispectral sparse sampling photoacoustic tomography based on deep algorithm unrolling

Photoacoustic tomography (PAT), as a novel medical imaging technology, provides structural, functional, and metabolism information of biological tissue in vivo. Sparse Sampling PAT, or SS-PAT, generates images with a smaller number of detectors, yet its image reconstruction is inherently ill-posed. Model-based methods are the state-of-the-art method for SS-PAT image reconstruction, but they require design of complex handcrafted prior. Owing to their ability to derive robust prior from labeled datasets, deep-learning-based methods have achieved great success in solving inverse problems, yet their interpretability is poor. Herein, we propose a novel SS-PAT image reconstruction method based on deep algorithm unrolling (DAU), which integrates the advantages of model-based and deep-learning-based methods. We firstly provide a thorough analysis of DAU for PAT reconstruction. Then, in order to incorporate the structural prior constraint, we propose a nested DAU framework based on plug-and-play Alternating Direction Method of Multipliers (PnP-ADMM) to deal with the sparse sampling problem. Experimental results on numerical simulation, in vivo animal imaging, and multispectral un-mixing demonstrate that the proposed DAU image reconstruction framework outperforms state-of-the-art model-based and deep-learning-based methods.

Navigating the Complexities of Brain Metastases Management.

The management of brain metastases, a potentially devastating complication of advanced cancers, has become increasingly complex with advancements in local and systemic therapies. Improved outcomes and extended survival for patients with metastatic solid tumors have led to a surge in the prevalence and possibly incidence of brain metastases, affecting up to 40% of individuals with solid tumors. Enhanced imaging technologies contribute to more accurate and early detection, shaping the understanding of the intricate landscape of this condition. Traditionally, surgery and radiation stood as the mainstays of treatment because of the limited efficacy of systemic therapies within the brain. However, emerging clinical data, particularly in melanoma, lung, and breast cancers, reveal promising results with novel systemic treatments such as immunotherapy and targeted therapies. Despite the historical exclusion of patients with active brain metastases from clinical trials, a shift is occurring toward a more inclusive approach. This chapter delves into the multifaceted challenges associated with managing brain metastases, with a focus on the evolving landscape of systemic approaches as well as the intricacies of shared decision making, providing a comprehensive overview of the current state and future directions in navigating the complexities of brain metastases management.

Comparison of Feature Histograms and Co-occurrence Matrix on Analysis of the Light Spectrum Effect for Identification of Surface Roughness with Speckle Images

Abstract Speckle imaging is a technique that can be used for digital image analysis. Speckle imaging technology essentially uses a laser to create a pattern of dots through the laser’s interference with an object. The advantage of speckle photography is that it is safe and does not require direct contact with the subject. In this study, speckle imaging techniques were used to analyze the surface roughness of objects. Speckle imaging techniques are used to investigate the influence of the choice of laser spectrum on the determination of the surface roughness of an object. For objects with rough surfaces, it comes in 80 sheets of coarse sandpaper and 2000 sheets of fine sandpaper. This study aims to compare the accuracy of identification results using Histogram Features and Co-occurrence Matrix Features when analyzing the influence of laser wavelength on object spot image identification. The research procedures included identifying rough objects, collecting point image data on rough surfaces using a series of experiments, pre-processing the point images, extracting features using histograms and co-occurrence matrices, and using a naive Bayes classifier. It includes determining surface roughness and performing comparisons between them. The resulting feature histogram and feature appearance matrix. This study shows that the use of Histogram Features and coexistence matrix features yields similar conclusions regarding the influence of the laser beam spectrum in determining the surface roughness of an object. The difference between Histogram Features and Co-occurrence Matrix Features lies in the pattern of detection results. Differences in the detection result patterns may be caused by differences in the characteristics of the Histogram Features and Co-occurrence Matrix Features of each speckle image used in the study. Although the detection result patterns of the Histogram Feature amount and the Co-occurrence Matrix feature amount are different, the accuracy of the detection result is equivalent. The similarity in the accuracy of the detection results may be caused by the similarity in the effectiveness of the two features in detecting the speckle image pattern.

Leveraging Vision Transformers for Detecting Porosity in Wind Energy Composites

The structural robustness and operational efficiency of wind turbine rotor blades are crucial for the overall effectiveness of wind energy systems, often constructed with fiber-reinforced polymers (FRPs) and adhesives. However, porosity within these materials poses a significant threat, weakening structural strength and effectiveness. Air pockets lead to stress concentration points, reducing load-carrying capacity and elevating the risk of blade failure, especially under dynamic wind loads. Manual detection of these air pockets is laborious, necessitating automated inspection techniques. Advanced imaging technologies, such as computed tomography (CT) scanning and deep learning, hold promise for identifying and quantifying porosity in FRPs and adhesives, reducing labor while enhancing accuracy. The study introduces a transformer-based model for porosity detection, departing from convolution-based methods, emphasizing the incorporation of global context throughout the network. Leveraging Vision Transformer (ViT) framework advances, the model is applied to porosity segmentation in wind energy blades, showing promising results with limited datasets. The prospect of using larger datasets suggests potential for a versatile solution in segmenting porosity or voids in various wind energy blade composites, including adhesives.

Mohs micrographic surgery challenges and new technologies to optimize care of cutaneous malignancies of the ear.

Cutaneous malignancies affecting the ear, exacerbated by extensive ultraviolet (UV) exposure, pose intricate challenges owing to the organ's complex anatomy. This article investigates how the anatomy contributes to late-stage diagnoses and ensuing complexities in surgical interventions. Mohs Micrographic Surgery (MMS), acknowledged as the gold standard for treating most cutaneous malignancies of the ear, ensures superior margin control and cure rates. However, the ear's intricacy necessitates careful consideration of tissue availability and aesthetic outcomes. The manuscript explores new technologies like Reflectance Confocal Microscopy (RCM), Optical Coherence Tomography (OCT), High-Frequency, High-Resolution Ultrasound (HFHRUS), and Raman spectroscopy (RS). These technologies hold the promise of enhancing diagnostic accuracy and providing real-time visualization of excised tissue, thereby improving tumor margin assessments. Dermoscopy continues to be a valuable non-invasive tool for identifying malignant lesions. Staining methods in Mohs surgery are discussed, emphasizing hematoxylin and eosin (H&E) as the gold standard for evaluating tumor margins. Toluidine blue is explored for potential applications in assessing basal cell carcinomas (BCC), and immunohistochemical staining is considered for detecting proteins associated with specific malignancies. As MMS and imaging technologies advance, a thorough evaluation of their practicality, cost-effectiveness, and benefits becomes essential for enhancing surgical outcomes and patient care. The potential synergy of artificial intelligence with these innovations holds promise in revolutionizing tumor detection and improving the efficacy of cutaneous malignancy treatments.

Enhancing mango quality control: A novel approach to spongy tissue inspection through image clustering and machine learning models via X‐ray imaging

AbstractThe globally renowned Alphonso mango is esteemed for its exceptional flavor, fragrance, texture, and extended shelf life. However, it faces a substantial challenge with spongy tissue (ST) disorder, affecting up to 30% of a single lot. This issue leads to the rejection of entire batches during export due to delayed detection, causing significant produce loss. Additionally, the current destructive assessment method is inadequate, as physically inspecting only a few mangoes within a batch does not ensure overall quality, emphasizing the need for a non‐destructive approach. To address these challenges, this study proposes a non‐destructive approach utilizing X‐ray imaging to identify ST in Alphonso mangoes, employing machine learning (ML) for binary classification. We captured and augmented 216 X‐ray images, utilizing various augmentation techniques. K‐means clustering effectively distinguished healthy and ST‐affected areas in mango fruits. For automated classification, ML models were fine‐tuned through grid search hyperparameter optimization within a 5‐fold cross‐validation framework. The CNN model achieved impressive accuracies of 99.20% and 96.82% at threshold values of .5 and .95, respectively, on the test dataset, while the SVM model attained overall accuracy rates of 95.23% and 87.35% for the same thresholds. Our study demonstrates the potential of machine learning in X‐ray imaging for detecting internal fruit issues, improving inspection for disease and damage identification, and enhancing quality control across the food industry. Practical implications include the implementation of non‐destructive inspection methods to reduce produce loss and ensure consistent quality in mango lots. Future research could focus on validating the model's efficacy with external datasets, evaluating its robustness in detecting various internal irregularities, and exploring advancements in X‐ray imaging technology for broader applications in agriculture.Practical applicationsThe suggested non‐destructive approach provides a practical solution for quality control, especially in export scenarios. The versatility of the ML‐powered X‐ray imaging technique to examine internal irregularities in various fruits beyond mangoes broadens its potential application in the fruit industry for sorting and grading purposes. This innovation contributes to heightened consumer satisfaction, providing a cost‐effective and scalable solution for widespread implementation. In summary, the findings of the study show promise for revolutionizing mango quality control, particularly for the esteemed Alphonso variety, and propelling advancements in fruit inspection methodologies across the industry.

Protein content prediction of rice grains based on hyperspectral imaging

This study utilized hyperspectral imaging technology combined with mathematical modeling methods to predict the protein content of rice grains. Firstly, the Kjeldahl method was used to determine the protein content of rice grains, and different preprocessing techniques were applied to the spectral information. Then, a prediction model for rice grain protein content was developed by combining the spectral data with the protein content. After performing multiplicative scatter correction (MSC) preprocessing and selecting feature wavelengths based on successive projections algorithm (SPA), the multivariate linear regression (MLR) model showed the best prediction performance, with a calibration set R2C of 0.9393, a validation set R2V of 0.8998, an RMSEV of 0.1725, and an RPD of 3.16. Finally, the quantitative protein content model was mapped pixel by pixel to visualize the distribution of rice protein, providing possibilities for non-destructive protein content detection.

From criteria to clinic: How updated SLiM CRAB criteria influence multiple myeloma diagnostic activity.

7556 Background: Multiple myeloma (MM) is a plasma cell malignancy, which is preceded by asymptomatic, precursor state of monoclonal gammopathy of undetermined significance (MGUS). Once diagnosed with MGUS, monitoring for progression to MM is recommended. The diagnostic criteria for active MM include presence of ≥1 CRAB features (hypercalcemia, renal failure, anemia, or one or more osteolytic bone lesions on skeletal radiology, CT or PET/CT imaging) in addition to clonal plasma cells on bone marrow. In 2014, the International Myeloma Working Group (IMWG) added SLiM to the criteria: clonal bone marrow plasma cells ≥60%, serum free light chain (FLC) ratio ≥100, and >1 focal lesion on magnetic resonance imaging (MRI). This study investigated the longitudinal influence of the updated diagnostic criteria on the utilization of imaging examination and the number of MM diagnoses overtime among patients with MGUS. Methods: We used data from the U.S. Veterans Health Administration (VHA) 2010-18. Patients with MGUS were identified by a published natural language processing (NLP) model. MM cases diagnosed under the old criteria were identified using International Classification of Diseases (ICD)-9/10 codes for CRAB symptoms, while cases diagnosed under the new criteria were identified using a published NLP-assisted model developed based on the SLiM-CRAB criteria. We then performed a counterfactual comparison to contrast the number of MM cases that would have been diagnosed under the old criteria with those under the new criteria. To monitor changes in the use of advanced imaging technology, we calculated the frequency of imaging examinations orders <1 year following MGUS diagnosis, including whole-body/spine computed tomography (CT), MRI and positron emission tomography-CT. Results: A total of 29,336 MGUS patients were identified. MGUS patients undergoing whole-body or spine MRI/CT scans <1 year following-MGUS diagnosis increased from 15.72% during 2010-13 to 21.06% during 2015-18 ( P = 0.002). In 2010-13, 346 MM cases were diagnosed per year on average with the old criteria. This number increased to 666 cases in 2015-18 with the updated criteria. Had the new criteria been applied retrospectively to 2010-13, the number of diagnoses would have increased 28% (to 441 cases per year). Conversely, had the old criteria been applied to 2015-18, the number of diagnoses would have decreased by 29% (to 473 cases per year). Conclusions: Our study revealed a significant increase in MM diagnoses after adding the SLiM biomarkers into the updated diagnostic criteria. In parallel, there has been an increase in the use of CT/MRI imaging for MGUS patients, suggesting a shift towards more aggressive diagnostic strategies. Further analysis is warranted to evaluate the positive detection rate of active MM through imaging and to understand the implications of early detection on MM patient outcomes.

Implementation and Execution of Big Data-based Studies in Ophthalmology within the Framework of the GDPR.

The processing of the retrospective data pool for ophthalmology holds huge potential, especially for the research sector. "Big Data" enables medical science to draw conclusions for the future from historical data. Based on the evaluation of such data, algorithms could be trained, for instance, that are capable of making decisions with the help of artificial intelligence. As a result, the medical decision-making process on certain issues could be accelerated, enriched in qualitative and quantitative terms, or even completely be taken over. Ophthalmology is a rapidly evolving field. Due to the multitude of partly automated medical imaging technologies and the predestined accessibility of the eye for such technologies, ophthalmology, similarly to radiology or dermatology, is well suited for artificial intelligence-assisted image data analysis and the frequently associated initiation of diagnosis and therapy. Meanwhile, numerous studies exist based on AI-assisted image data analysis of ophthalmological image data. To the extent that the algorithms filter out results from the data pools by means of calculation rules and are even capable of making independent decisions on the basis of decision trees, the enormous benefit and, simultaneously, the profit for scientific research is quite obvious. Accordingly, it would be desirable to have unrestricted and comprehensive possibility of corresponding data processing of these health data for ophthalmological research. In spite of the potential for ophthalmology, for which there is only fragmentary evidence, the question of practical feasibility arises. In particular, the legal requirements and limits of European and national data protection law must be taken into account, prior to any unreflected processing of personal (health) data. Only by doing so can we circumvent existing obstacles and pitfalls, which can lead to severe fines. Most important are to date the requirements of two legal texts: The General Data Protection Regulation (GDPR) and the Federal Data Protection Act (BDSG). This article provides an overview of the relevant legal requirements applicable in the field of ophthalmology and highlights the major pitfalls and implementation requirements.

Use of Microwave Tomography Technology for Water Seepage Investigation in Buildings

In metropolitan cities like Hong Kong, it is densely populated and most people live in privately owned premises of reinforced concrete residential buildings. Owing to material deterioration under (a) environmental exposure, (b) poor construction workmanship, (c) lack of proper maintenance and (d) alteration of premises into sub-divided flats with bathrooms, water may seep through the floor slabs of the premises and infiltrate into the subject premises below, and this create nuisance to the subject premises. In this paper, the causes of deterioration of concrete material are elaborated.

Design and synthesis of hemicyanine-based NIRF probe for detecting Aβ aggregates in Alzheimer’s disease

Alzheimer’s disease (AD), a progressive neurodegenerative disorder, has garnered increased attention due to its substantial economic burden and the escalating global aging phenomenon. Amyloid-β deposition is a key pathogenic marker observed in the brains of Alzheimer’s sufferers. Based on real-time, safe, low-cost, and commonly used, near-infrared fluorescence (NIRF) imaging technology have become an essential technique for the detection of AD in recent years. In this work, NIRF probes with hemicyanine structure were designed, synthesized and evaluated for imaging Aβ aggregates in the brain. We use the hemicyanine structure as the parent nucleus to enhance the probe’s optical properties. The introduction of PEG chain is to improve the probe’s brain dynamice properties, and the alkyl chain on the N atom is to enhance the fluorescence intensity of the probe after binding to the Aβ aggregates as much as possible. Among these probes, Z2, Z3, Z6, X3, X6 and T1 showed excellent optical properties and high affinity to Aβ aggregates (Kd = 24.31 ∼ 59.60 nM). In vitro brain section staining and in vivo NIRF imaging demonstrated that X6 exhibited superior discrimination between Tg mice and WT mice, and X6 has the best brain clearance rate. As a result, X6 was identified as the optimal probe. Furthermore, the docking theory calculation results aided in describing X6′s binding behavior with Aβ aggregates. As a high-affinity, high-selectivity, safe and effective probe of targeting Aβ aggregates, X6 is a promising NIRF probe for in vivo detection of Aβ aggregates in the AD brain.

Mitochondrial nanoprobe for precise cellular and drug analysis via graph Neural network

Mitochondrial morphology is crucial for cell identification and drug toxicity evaluation, yet it is challenged by mitochondrial complexities and limitations in imaging technologies. We develop an ultrasmall (∼1.5 nm) fluorescent carbon dot (CD), specifically designed to target mitochondria in live cells. These CDs exhibit near-infrared fluorescence at 685 nm, enhancing fluorescence imaging’s signal-to-noise ratio by 2–3 times. Their dense-blinking behavior enables rapid fluctuation-based super-resolution imaging with as few as 10 frames, thereby allowing for detailed visualization of mitochondrial morphology and dynamics. We further propose a graph-based deep learning framework that integrates multidimensional mitochondrial features, which are updated using a Graph Neural Network (GNN), to achieve precise mitochondria-based cellular typing and drug toxicity analysis with up to 98% accuracy. Additionally, we pioneer the use of interpretability algorithms to elucidate the GNN model, revealing how the depicted mitochondrial features by the CDs drive these predictions. This approach has significant implications in cellular and toxicological research, offering a unique tool for deciphering cellular behaviors and drug interactions.

Freeform metasurface color router for deep submicron pixel image sensors.

Advances in imaging technologies have led to a high demand for ultracompact, high-resolution image sensors. However, color filter-based image sensors, now miniaturized to deep submicron pixel sizes, face challenges such as low signal-to-noise ratio due to fewer photons per pixel and inherent efficiency limitations from color filter arrays. Here, we demonstrate a freeform metasurface color router that achieves ultracompact pixel sizes while overcoming the efficiency limitations of conventional architectures by splitting and focusing visible light instead of filtering. This development is enabled by a fully differentiable topology optimization framework to maximize the use of the design space while ensuring fabrication feasibility and robustness to fabrication errors. The metasurface can distribute an average of 85% of incident visible light according to the Bayer pattern with a pixel size of 0.6 μm. The device and design methodology enable the compact, high-sensitivity, and high-resolution image sensors for various modern technologies and pave the way for the advanced photonic device design.

A study on the comprehensive application of installation art based on phantom imaging technology

A study on the comprehensive application of installation art based on phantom imaging technology

Free-Breathing Functional Pulmonary Proton MRI: A Novel Approach Using Voxel-Wise Lung Ventilation (VOLVE) Assessment in Healthy Volunteers and Patients With Chronic Obstructive Pulmonary Disease.

In respiratory medicine, there is a need for sensitive measures of regional lung function that can be performed using standard imaging technology, without the need for inhaled or intravenous contrast agents. To describe VOxel-wise Lung VEntilation (VOLVE), a new method for quantifying regional lung ventilation (V) and perfusion (Q) using free-breathing proton MRI, and to evaluate VOLVE in healthy never-smokers, healthy people with smoking history, and people with chronic obstructive pulmonary disease (COPD). Prospective pilot. Twelve healthy never-smoker participants (age 30.3 ± 12.5 years, five male), four healthy participants with smoking history (>10 pack-years) (age 42.5 ± 18.3 years, one male), and 12 participants with COPD (age 62.8 ± 11.1 years, seven male). Single-slice free-breathing two-dimensional fast field echo sequence at 3 T. A novel postprocessing was developed to evaluate the MR signal changes in the lung parenchyma using a linear regression-based approach, which makes use of all the data in the time series for maximum sensitivity. V/Q-weighted maps were produced by computing the cross-correlation, lag and gradient between the respiratory/cardiac phase time course and lung parenchyma signal time courses. A comparison of histogram median and skewness values and spirometry was performed. Kruskal-Wallis tests with Dunn's multiple comparison tests to compare VOLVE metrics between groups; Spearman correlation to assess the correlation between MRI and spirometry-derived parameters; and Bland-Altman analysis and coefficient of variation to evaluate repeatability were used. A P-value <0.05 was considered significant. Significant differences between the groups were found for ventilation between healthy never-smoker and COPD groups (median XCCV, LagV, and GradV) and perfusion (median XCCQ, LagQ, and GradQ). Minimal bias and no significant differences between intravisit scans were found (P range = 0.12-0.97). This preliminary study showed that VOLVE has potential to provide metrics of function quantification. 2 TECHNICAL EFFICACY: Stage 1.

Photonics sensors: A perspective on current advancements, emerging challenges, and potential solutions (Invited)

Photonic sensors play a vital role in the modern world, revolutionizing various industries with their efficiency and precision. These sensors utilize light particles (photons) to detect, measure, and analyze different parameters such as light intensity, wavelength, phase, and polarization. One of their key advantages lies in their ability to offer highly sensitive, fast, and accurate measurements across a wide range of applications. In healthcare, they are used in medical imaging technologies like optical coherence tomography for non-invasive diagnostics and imaging of tissues. Additionally, photonic sensors have transformed environmental monitoring, allowing real-time analysis of pollutants, gases, and other substances in air and water. Moreover, their application extends to defence and security, where they are utilized in surveillance, reconnaissance, and laser-based defence systems. In this concise paper, we aim to offer our insights into the latest advancements, emerging challenges, and potential solutions in the ongoing development of photonic sensors.