Imaging Setup Research Articles

SYMPATHIQUE: image-based tracking of symptoms and monitoring of pathogenesis to decompose quantitative disease resistance in the field

BackgroundQuantitative disease resistance (QR) is a complex, dynamic trait that is most reliably quantified in field-grown crops. Traditional disease assessments offer limited potential to disentangle the contributions of different components to overall QR at critical crop developmental stages. Yet, a better functional understanding of QR could greatly support a more targeted, knowledge-based selection for QR and improve predictions of seasonal epidemics. Image-based approaches together with advanced image processing methodologies recently emerged as valuable tools to standardize relevant disease assessments, increase measurement throughput, and describe diseases along multiple dimensions.ResultsWe present a simple, affordable, and easy-to-operate imaging set-up and imaging procedure for in-field acquisition of wheat leaf image sequences. The development of Septoria tritici blotch and leaf rusts was monitored over time via robust methods for symptom detection and segmentation, spatial alignment of images, symptom tracking, and leaf- and symptom characterization. The average accuracy of the spatial alignment of images in a time series was approximately 5 pixels (~ 0.15 mm). Leaf-level symptom counts as well as individual symptom property measurements revealed stable patterns over time that were generally in excellent agreement with visual impressions. This provided strong evidence for the robustness of the methodology to variability typically inherent in field data. Contrasting patterns in the number of lesions resulting from separate infection events and lesion expansion dynamics were observed across wheat genotypes. The number of separate infection events and average lesion size contributed to different degrees to overall disease intensity, possibly indicating distinct and complementary mechanisms of QR.ConclusionsThe proposed methodology enables rapid, non-destructive, and reproducible measurement of several key epidemiological parameters under field conditions. Such data can support decomposition and functional understanding of QR as well as the parameterization, fine-tuning, and validation of epidemiological models. Details of pathogenesis can translate into specific symptom phenotypes resolvable using time series of high-resolution RGB images, which may improve biological understanding of plant-pathogen interactions as well as interactions in disease complexes.

Quantification of cavitating flows with neutron imaging

The current experimental investigation demonstrates the capability of neutron imaging to quantify cavitation, in terms of vapour content, within an orifice of an abruptly constricting geometry. The morphology of different cavitation regimes setting in was properly visualised owing to the high spatial resolution of 16 μm achieved, given the extensive field of view of 12.9 × 12.9 mm2 offered by the imaging set-up. At a second step, the method was proven capable of highlighting subtle differences between fluids of different rheological properties. More specifically, a reference liquid was comparatively assessed against a counterpart additised with a Quaternary Ammonium Salt (QAS) agent, thus obtaining a viscoelastic behaviour. In accordance with previous studies, it was verified, yet in a quantifiable manner, that the presence of viscoelastic additives affects the overall cavitation topology by promoting the formation of more localised vortical cavities rather than cloud-like structures occupying a larger portion of the orifice core. To the authors’ best knowledge, the present work is the first to demonstrate that neutron imaging is suitable for quantifying in-nozzle cavitating flow at the micrometre level, consequently elucidating the distinct forms of vaporous structures that arise. The potential of incorporating neutron irradiation for the quantification of two-phase flows in metallic microfluidics devices has been established.

Event-based high-resolution neutron image formation analysis using intensified CMOS cameras

We present a versatile optical setup for high-resolution neutron imaging with an adaptable field of view and magnification that can resolve individual neutron absorption events with an image intensifier and a CMOS camera. Its imaging performance is characterized by evaluating the resolution limits of the individual optical components and resulting design aspects are discussed. Neutron radiography measurements of a Siemens star pattern were performed in event mode acquisition comparing two common high-resolution neutron scintillators, crystalline Gadolinium Gallium Garnet (GGG) and powdered Gadolinium Oxysulfide (GOS). An analysis of the light signature caused by neutron absorption events is performed and some resulting issues for both GGG and GOS regarding optical system design are addressed. Both scintillators reach similar resolution (4–5 μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\upmu }$$\\end{document}m) in event mode acquisition despite different light emission characteristics. The findings suggest that, in the case of GOS, the resolution is limited by the size of the light clusters which in turn originate from the photon scattering at the boundaries of the powder particles comprising it, while with GGG the lower light conversion efficiency makes it challenging to collect enough photons to trigger sufficient signal amplification in the image intensifier. Overall, the proposed event-based evaluation of scintillators allows for quantifying and optimizing various design parameters, which is much more complex than adopting conventional methods based on integrated images.

Automated acute skin toxicity scoring in a mouse model through deep learning.

This study presents a novel approach to skin toxicity assessment in preclinical radiotherapy trials through an advanced imaging setup and deep learning. Skin reactions, commonly associated with undesirable side effects in radiotherapy, were meticulously evaluated in 160 mice across four studies. A comprehensive dataset containing 7542 images was derived from proton/electron trials with matched manual scoring of the acute toxicity on the right hind leg, which was the target area irradiated in the trials. This dataset was the foundation for the subsequent model training. The two-step deep learning framework incorporated an object detection model for hind leg detection and a classification model for toxicity classification. An observer study involving five experts and the deep learning model, was conducted to analyze the retrospective capabilities and inter-observer variations. The results revealed that the hind leg object detection model exhibited a robust performance, achieving an accuracy of almost 99%. Subsequently, the classification model demonstrated an overall accuracy of about 85%, revealing nuanced challenges in specific toxicity grades. The observer study highlighted high inter-observer agreement and showcased the model's superiority in accuracy and misclassification distance. In conclusion, this study signifies an advancement in objective and reproducible skin toxicity assessment. The imaging and deep learning system not only allows for retrospective toxicity scoring, but also presents a potential for minimizing inter-observer variation and evaluation times, addressing critical gaps in manual scoring methodologies. Future recommendations include refining the system through an expanded training dataset, paving the way for its deployment in preclinical research and radiotherapy trials.

Multianalyte Detection with Metasurface-Based Midinfrared Microspectrometer.

Midinfrared (2.5-25 μm) spectroscopy is an ideal tool for identifying chemicals in a nondestructive manner. The traditional platform is a Fourier transform infrared (FTIR) spectrometer, but this is too bulky, expensive, and power-hungry for many applications. There is therefore a growing demand for small, lightweight, and cost-effective microspectrometers for use in the field. One emerging platform is the filter-array detector-array microspectrometer. It pairs a broadband detector array with a thin and rigid array of spectral filters to offer a robust, compact platform for real-time in situ sensing. However, most demonstrations have only focused on identifying a single chemical against a null sample, even though many applications would involve multianalyte detection. In this work, we show a rare attempt at simultaneously tracking multiple analytes with a metasurface filter-array microspectrometer. The metasurface consists of periodic lattices of subwavelength circular apertures in an aluminum layer to create an array of bandpass filters. The filter array is imaged with an off-the-shelf microbolometer via a reverse-lens imaging setup to simultaneously monitor the concentration of ethanol and methanol in gasoline. This represents an important application of fuel quality monitoring. Chemometric models (PLS and SVR) are trained and tested on gasoline blends with ethanol and methanol contents, both ranging from 0% to 20% v/v. A support vector machine regression (SVR) model with a cubic kernel was found to have the lowest combined prediction errors. The root-mean-square-error of prediction (RMSEP) for ethanol and methanol are 1.23% and 1.84% v/v; the corresponding pseudounivariate limit of detection is found to be 4.22% and 6.86% v/v, respectively. This work takes the emerging field of metasurface-based mid-infrared spectrometers from single- to multianalyte detection, thereby considerably expanding their range of potential applications.

Translated object identification for efficient ghost imaging

Alignment of a single-pixel quantum ghost imaging setup is complex and requires extreme precision. Due to misalignment, easily created by human error in the alignment process, reconstructed images are often translated off the central imaging axis. This becomes problematic for intelligent object detection and identification in fast imaging cases, as these algorithms are unable to achieve early image identification. Here, we implemented a U-net algorithm to correctly recognize images in the early reconstruction stage regardless of any off-axis translation. The U-net was trained on a uniquely curated blurred, noised, and off-axis translated dataset. We achieved a 5× reduction in imaging speeds by early image identification in four different translation directions.

Dosimetric consequences of adapting the craniocaudal isocenter distance to daily patient position in craniospinal irradiation using volumetric modulated arc therapy.

In craniospinal irradiation, two or three isocenter groups along the craniocaudal axis are required to cover the long treatment target. Adapting the isocenter distance according to daily deviations in patient position is challenging because dosimetric hot or cold spots may occur in the field junction. The aim of this study was to quantify the effect of adapting the isocenter distance to patient position on the dose distribution of the field overlap region in craniospinal irradiation using partial-arc volumetric modulated arc therapy. The magnitude of isocenter distance deviations in craniocaudal direction was quantified by registering the setup images of 204 fractions of 12 patients to the planning images. The dosimetric effect of these deviations was determined by shifting the isocenters of the original treatment plan and calculating the resulting dose distribution. On fraction-level, deviations larger than 3mm caused more than 5 percentage point changes in the doses covering 2% (D2%) and 98% (D98%) of the junction volume in several patients. On treatment course-level, the changes in D2% and D98% of the junction volume were less than 5 percentage points in all cases except for one patient. Craniocaudal isocenter distance adaptation can be conducted provided that the mean isocenter distance deviation over the treatment course is within 3mm.

A multispectral camera in the VIS–NIR equipped with thermal imaging and environmental sensors for non invasive analysis in precision agriculture

Multispectral imaging (MSI) is a technique used to inspect materials properties in different domains, ranging from industrial to medical and cultural heritage and, recently, precision agriculture. Even though several MSI solutions are already commercially available, the research community is working to optimize multispectral cameras in terms of performance and cost. Systems for the agricultural field are usually very compact, combined with drones for large areas acquisition. In this work, we detail the implementation of an innovative, modular and low-cost solution of a multispectral camera based on three core camera systems in the optical (VIS–NIR) and thermal (LWIR) range. Multispectral imaging is performed with a rotating wheel of interchangeable band-pass filters. The system is also equipped with a set of environmental sensors to acquire CO2 concentration values, light intensity, temperature, and relative humidity of the surrounding environment. The technology and the measurement protocol were experimentally validated in laboratory and in open field. Advantages with respect to the available MSI cameras mounted on UAV is the integrated imaging in both the reflectance and the thermal emissive band in a close-up imaging setup and the use of environmental sensors. From the multispectral stack the spectral signature of the plants can be obtained and various vegetation indices (e.g., NDVI, NDRE) can be calculated for investigating the health status of the plant, while thermography provide additional monitoring. Close-up multispectral imaging is expected to tackle the new challenges of precision agriculture by enabling the acquisition of high-quality dataset on single plants.

Evolutionary Grid Optimization and Deep Learning for Improved In Vitro Cellular Spheroid Localization

The recently developed high-throughput system for cell spheroid generation (SpheroWell) is a promising technology for cost- and time-efficient in vitro analysis of, for example, chondrogenic differentiation. It is a compartmental growth surface where spheroids develop from a cell monolayer by self-assembling and aggregation. In order to automatize the analysis of spheroids, we aimed to develop imaging software and improve the localization of cell compartments and fully formed spheroids. Our workflow provides automated detection and localization of spheroids in different formation stages within Petri dishes based on images created with a low-budget camera imaging setup. This automated detection enables a fast and inexpensive analysis workflow by processing a stack of images within a short period of time, which is essential for the extraction of early readout parameters. Our workflow combines image processing algorithms and deep learning-based image localization/segmentation methods like Mask R-CNN and Unet++. These methods are refined by an evolution strategy for automated grid detection, which is able to improve the overall segmentation and classification quality. Besides the already pre-trained neural networks and predefined image processing parameters, our evolution-based post-processing provides the required adaptability for our workflow to deliver a consistent and reproducible quality. This is especially important due to the use of a low-budget imaging setup with various light conditions. The to-be-detected objects of the three different stages show improved results using our evolutionary post-processing for monolayer and starting aggregation with Dice coefficients of 0.7301 and 0.8562, respectively, compared with the raw scores of 0.2879 and 0.8187. The Dice coefficient of the fully formed spheroids in both cases is 0.8829. With our algorithm, we provide automated analyses of cell spheroid by self-assembling in SpheroWell dishes, even if the images are created using a low-budget camera setup.

Lagrangian particle tracking at large Reynolds numbers.

In the study of fluid turbulence, the Lagrangian frame of reference represents the most appropriate methodology for investigating transport and mixing. This necessitates the tracking of particles advected by the flow over space and time at high resolution. In the past, the purely spatial counterpart, the Eulerian frame of reference, has been the subject of extensive investigation utilizing hot wire anemometry that employs Taylor's frozen flow hypotheses. Measurements were reported for Taylor scale Reynolds number Rλ > 104 in atmospheric flows, which represent the highest strength of turbulence observed on Earth. The inherent difficulties in accurately tracking particles in turbulent flows have thus far constrained Lagrangian measurements to Taylor scale Reynolds numbers up to approximately Rλ = 103. This study presents the Lagrangian particle tracking setup in the MaxPlanck Variable Density Turbulence Tunnel (VDTT), where Taylor scale Reynolds numbers between 100 and 6000 can be reached. It provides a comprehensive account of the imaging setup within the pressurized facility, the laser illumination, the particles used, and the particle seeding mechanism employed, as well as a detailed description of the experimental procedure. The suitability of KOBO Cellulobeads D-10 particles as tracers within the VDTT is illustrated. The results demonstrate that there is no significant charge exhibited by the particles and that the impact of their inertia on the results is negligible across a wide range of experimental conditions. Typical data are presented, and the challenges and constraints of the experimental approach are discussed in detail.

Setup errors analysis in iterative kV CBCT: A clinical study of cervical cancer treated with Volumetric Modulated Arc Therapy.

This study aims to analyze setup errors in pelvic Volumetric Modulated Arc Therapy (VMAT) for patients with non-surgical primary cervical cancer, utilizing the onboard iterative kV cone beam CT (iCBCT) imaging system on the Varian Halcyon 2.0 ring gantry structure accelerator to enhance radiotherapy precision. We selected 132 cervical cancer patients who underwent VMAT with daily iCBCT imaging guidance. Before each treatment session, a registration method based on the bony structure was employed to acquire iCBCT images with the corresponding planning CT images. Following verification and adjustment of image registration results along the three axes (but not rotational), setup errors in the lateral (X-axis), longitudinal (Y-axis), and vertical (Z-axis) directions were recorded for each patient. Subsequently, we analyzed 3642 iCBCT image setup errors. The mean setup errors for the X, Y, and Z axes were 4.50±3.79mm, 6.08±6.30mm, and 1.48±2.23mm, respectively. Before correction with iCBCT, setup margins based on the Van Herk formula for the X, Y, and Z axes were 6.28, 12.52, and 3.26mm, respectively. In individuals aged 60 years and older, setup errors in the X and Y axes were significantly larger than those in the younger group (p<0.05). Additionally, there is no significant linear correlation between setup errors and treatment fraction numbers. Data analysis underscores the importance of precise Y-axis setup for cervical cancer patients undergoing VMAT. Radiotherapy centers without daily iCBCT should appropriately extend the planning target volume (PTV) along the Y-axis for cervical cancer patients receiving pelvic VMAT. Elderly patients exhibit significantly larger setup errors compared to younger counterparts. In conclusion, iCBCT-guided radiotherapy is recommended for cervical cancer patients undergoing VMAT to improve setup precision.

All-fiber miniature non-contact photoacoustic probe based on photoacoustic remote sensing microscopy for vascular imaging in vivo.

Photoacoustic (PA) remote sensing (PARS) microscopy represents a significant advancement by eliminating the need for traditional acoustic coupling media in PA microscopy (PAM), thereby broadening its potential applications. However, current PARS microscopy setups predominantly rely on free-space optical components, which can be cumbersome to implement and limit the scope of imaging applications. In this study, we develop an all-fiber miniature non-contact PA probe based on PARS microscopy, utilizing a 532-nm excitation wavelength, and showcase its effectiveness in in vivo vascular imaging. Our approach integrates various fiber-optic components, including a wavelength division multiplexer, a mode field adaptor, a fiber lens, and an optical circulator, to streamline the implementation of the PARS microscopy system. Additionally, we have successfully developed a miniature PA probe with a diameter of 4 mm. The efficacy of our imaging setup is demonstrated through in vivo imaging of mouse brain vessels. By introducing this all-fiber miniature PA probe, our work may open up new opportunities for non-contact PAM applications.

Unlocking the Potential of the CICERO Spinning Disk System: Exploring Diverse Applications

Abstract CrestOptics aims to make their products accessible to researchers, enabling a variety of applications, and to create products that are both efficient and user-friendly. The CICERO confocal spinning disk system embodies these principles, designed for easy integration into any imaging setup. Its compactness and flexibility make advanced fluorescence microscopy accessible to laboratories worldwide. This article will focus on the great versatility and strengths of the CICERO system, which is a complete widefield (WF) and spinning disk confocal (CF) solution. It will demonstrate its key features, namely its use on both inverted and upright microscopy configurations, its wide spectral range, the high resolution and sectioning power, and its large field of view (FOV). Microscopy Today has awarded CICERO a 2024 Innovation Award for these features.

Robust quantitative X-ray phase diagnostic for carbon composite characterisation in the context of lightning induced risk

Getting complementary physical information from a single image acquisition is particularly valuable for materials analysis. Grating based X-ray Phase Contrast Imaging (XPCI) methods allow decoupling attenuation, phase and scattering information. However, the phase and scattering extraction processes can easily suffer from artefacts, which is detrimental to implement this imaging technique in societal applications. In this paper, we demonstrate that grating based XPCI can provide a robust phase measurement in complex materials such as damaged composites. The technique allows the phase to be analysed using a self-assessment method that first identifies the artefacts from the imaging setup, and then can be used as an indicator to interpret the signal from a material. We focus on carbon fibre reinforced polymers which we subjected to laboratory-controlled lightning strikes. We evidence that the combined information from phase and attenuation allow identifying the type of defect induced by the lightning current. The phase information is converted into relative mass density variation within the sample and depicts areas with a loss in density up to 40%. We ensure that these results are valid by comparing them with an X-ray attenuation contrast tomographic reconstruction.

Studying Drosophila Larval Behavior in Agarose Channels.

Larvae of the fruit fly Drosophila melanogaster are a popular and tractable model system for studying the development and function of sensorimotor circuits, thanks to the relative numerical simplicity of their nervous system and the wealth of available genetic tools to manipulate the anatomy, activity, and function of specific cell types. Researchers studying the role of a particular gene or cell type in sensorimotor circuit activity or function may wish to observe the effects of an experimental manipulation during behavior in the intact animal. Observing these effects, which may include changes in the intracellular calcium concentration or movement of small numbers of neurons, muscles, etc., typically requires high-spatial-resolution imaging, which poses several difficulties in the freely crawling larva. Freely crawling larvae can move quickly and with changeable heading, making manual or automatic tracking challenging; additionally, they may make three-dimensional movements, such as rearing, that can degrade imaging focus. These challenges are potentially solvable using advanced imaging and algorithmic tracking setups, but cost, space, or development time may be prohibitive. This protocol describes a simple and cost-effective method for placing larvae inside agarose channels, thereby restricting larval crawling to a single dimension and enabling higher-magnification time-series imaging of fluorescently labeled structures during many cycles of locomotion. By using larvae that express fluorescent calcium indicators in cells of interest, researchers can apply this method to study the effects of experimental manipulations on neural or muscular activity during behavior in the intact animal.

Transportable hyperspectral imaging setup based on fast, high-density spectral scanning for in situ quantitative biochemical mapping of fresh tissue biopsies.

Histopathological examination of surgical biopsies, such as in glioma and glioblastoma resection, is hindered in current clinical practice by the long time required for the laboratory analysis and pathological screening, typically taking several days or even weeks to be completed. We propose here a transportable, high-density, spectral scanning-based hyperspectral imaging (HSI) setup, named HyperProbe1, that can provide in situ, fast biochemical analysis, and mapping of fresh surgical tissue samples, right after excision, and without the need for fixing, staining nor compromising the integrity of the tissue properties. HyperProbe1 is based on spectral scanning via supercontinuum laser illumination filtered with acousto-optic tunable filters. Such methodology allows the user to select any number and type of wavelength bands in the visible and near-infrared range between 510 and 900nm (up to a maximum of 79) and to reconstruct 3D hypercubes composed of high-resolution (4 to ), widefield images ( ) of the surgical samples, where each pixel is associated with a complete spectrum. The HyperProbe1 setup is here presented and characterized. The system is applied to 11 fresh surgical biopsies of glioma from routine patients, including different grades of tumor classification. Quantitative analysis of the composition of the tissue is performed via fast spectral unmixing to reconstruct the mapping of major biomarkers, such as oxy-( ) and deoxyhemoglobin (HHb), as well as cytochrome-c-oxidase (CCO). We also provided a preliminary attempt to infer tumor classification based on differences in composition in the samples, suggesting the possibility of using lipid content and differential CCO concentrations to distinguish between lower and higher-grade gliomas. A proof of concept of the performances of HyperProbe1 for quantitative, biochemical mapping of surgical biopsies is demonstrated, paving the way for improving current post-surgical, histopathological practice via non-destructive, in situ streamlined screening of fresh tissue samples in a matter of minutes after excision.

Design and development of a low-cost hyperspectral imaging setup via a machine-learningbased approach

This article presents a potential solution for developing a low-cost hyperspectral imaging (HSI) setup by preserving pertinent information acquired from a conventional hyperspectral imaging setup. Conventional hyperspectral images (HSI) of three different types of leaves, gongura (Hibiscus sabdariffa), amaranthus (Amaranthus viridis), and banana (Musa acuminata) were acquired with 204 wavelengths/bands. The spectra are processed using linear discriminant analysis (LDA) to find a set of signature wavelengths for leaf classification. Afterwards, 20 visible range wavelengths (440 to 700 nm) were found to be incorporated into a low-cost setup involving a monochromator, beam steering elements, and a smartphone camera with associated machine learning (ML) classifiers. For building the datasets, we extracted 90 spectra from the images captured using the conventional HSI setup under the full spectra range (397 nm to 1003 nm). Similarly, 90 spectra were extracted from the images captured from the low-cost setup under the 20 signature wavelengths. For further experimentation, we split the datasets into Dataset-A, containing 70% of the total spectra, and the remaining 30% in Dataset-B for HSI as well as low-cost setup. Dataset- B was reserved to evaluate the robustness of the classifiers on an unseen dataset. LDA surpasses the other classifiers in leaves classification for HSI as well as low-cost setup. For the HSI setup, LDA achieved a 100% average score for performance matrices (classification accuracy, precision, Recall, and F1-score) on dataset A as well as on dataset B. Moreover, for the low-cost setup, LDA achieved 98.33% ± 4.99% classification accuracy, 98.89% ± 3.33% precision, 98.33% ± 4.99% recall, and 98.22% ± 5.33% F1-score on dataset A. Additionally, LDA achieved 96.29% classification accuracy, 96.67% precision, 96.29% recall, and 96.28% F1-score for dataset B. The promising results indicated that the low-cost set-up closely emulates the HSI system’s performance in terms of performance metrics, delivering a low-cost HSI system for various applications in the future.

Reconstruction algorithm for cross-waveband optical computing imaging

Abstract In a single-pixel fast imaging setup, the data collected by the single-pixel detector needs to be processed by a computer, but the speed of the latter will affect the image reconstruction time. Here we propose two kinds of setups which are able to transform non-visible into visible light imaging, wherein their computing process is replaced by a camera integration mode. The image captured by the camera has a low contrast, so here we present an algorithm that can realize a high quality image in near-infrared to visible cross-waveband imaging. The scheme is verified both by simulation and in actual experiments. The setups demonstrate the great potential for single-pixel imaging and high-speed cross-waveband imaging for future practical applications.

Domain adaptation strategies for 3D reconstruction of the lumbar spine using real fluoroscopy data

In this study, we address critical barriers hindering the widespread adoption of surgical navigation in orthopedic surgeries due to limitations such as time constraints, cost implications, radiation concerns, and integration within the surgical workflow. Recently, our work X23D showed an approach for generating 3D anatomical models of the spine from only a few intraoperative fluoroscopic images. This approach negates the need for conventional registration-based surgical navigation by creating a direct intraoperative 3D reconstruction of the anatomy. Despite these strides, the practical application of X23D has been limited by a significant domain gap between synthetic training data and real intraoperative images.In response, we devised a novel data collection protocol to assemble a paired dataset consisting of synthetic and real fluoroscopic images captured from identical perspectives. Leveraging this unique dataset, we refined our deep learning model through transfer learning, effectively bridging the domain gap between synthetic and real X-ray data. We introduce an innovative approach combining style transfer with the curated paired dataset. This method transforms real X-ray images into the synthetic domain, enabling the in-silico-trained X23D model to achieve high accuracy in real-world settings.Our results demonstrated that the refined model can rapidly generate accurate 3D reconstructions of the entire lumbar spine from as few as three intraoperative fluoroscopic shots. The enhanced model reached a sufficient accuracy, achieving an 84% F1 score, equating to the benchmark set solely by synthetic data in previous research. Moreover, with an impressive computational time of just 81.1 ms, our approach offers real-time capabilities, vital for successful integration into active surgical procedures.By investigating optimal imaging setups and view angle dependencies, we have further validated the practicality and reliability of our system in a clinical environment. Our research represents a promising advancement in intraoperative 3D reconstruction. This innovation has the potential to enhance intraoperative surgical planning, navigation, and surgical robotics.

High Resolution Terahertz (THz) Imaging

Terahertz (THz) imaging is essential for non-contact and non-destructive testing due to its ability to penetrate numerous materials. Typically, the sample is raster-scanned through the beam waist of a confocal optical setup to generate an image in a single-pixel detection scheme. However, the spatial resolution achieved using such imaging configurations remains no less than millimeters, restricting the application of THz imaging. Here in this work, a simple hollow-core metal waveguide (HCMWG) based terahertz imaging setup has been designed and implemented in transmission configuration to record THz hyperspectral images of a sample. The sample is kept in the near-field range of the HCMWG to exploit the THz electric field confinement of the guided mode toward attaining high-resolution imaging. The THz images are acquired by raster scanning the sample in front of the HCMWG output aperture using a single-pixel detection setup. Additionally, spectroscopic sensing using the same setup has been shown by extracting the absorption spectrum of a chemical compound. Further, a plant leaf is used as a sample to demonstrate the applicability of this technique, where the highly resolved transmitted THz image is acquired using the proposed setup. This image is compared with the image recorded using a conventional confocal lens-based THz optical setup. The results show that the technique can resolve subwavelength features (approx. 0.8λ) of the sample under study while preserving spectroscopic information.