Equivalent Image Research Articles

On the accuracy of data assimilation algorithms for dense flow field reconstructions

Within the framework of the European Union Horizon 2020 project HOMER (Holistic Optical Metrology for Aero-Elastic Research), data assimilation (DA) algorithms for dense flow field reconstructions developed by different research teams, hereafter referred to as the participants, were comparatively assessed. The assessment is performed using a synthetic database that reproduces the turbulent flow in the wake of a cylinder in ground effect, placed at the distance of one diameter from a lower wall. Downstream of the cylinder, this wall continues either in the form of a flat steady wall, or of a flexible panel undergoing periodic oscillations; these two situations correspond to two different test cases, the latter being introduced to extend the evaluation to fluid–structure interaction problems. The input data for the data assimilation algorithms were datasets containing the particle locations and their trajectories identification numbers, at increasing tracer concentrations from 0.04 to 1.4 particles/mm3 (equivalent image density values between 0.005 and 0.16 particles per pixel, ppp). The outputs of the DA algorithms considered for the assessment were the three components of the velocity, the nine components of the velocity gradient tensor and the static pressure, defined in the flow field on a Cartesian grid, as well as the static pressure on the wall surface, and its position in the deformable wall case. The results were analysed in terms of errors of the output quantities with respect to the ground-truth values and their distributions. Additionally, the performances of the different DA algorithms were compared with that of a standard linear interpolation approach. The velocity errors were found in the range between 3 and 11% of the bulk velocity; furthermore, the use of the DA algorithms enabled an increase of the measurement spatial resolution by a factor between 3 and 4. The errors of the velocity gradients were of the order of 10–15% of the peak vorticity magnitude. Accurate pressure reconstruction was achieved in the flow field, whereas the evaluation of the surface pressure revealed more challenging. As expected, lower errors were obtained for increasing seeding concentration. The difference of accuracy among the results of the different data assimilation algorithms was noticeable especially for the pressure field and the compliance with governing equations of fluid motion, and in particular mass conservation. The analysis of the flexible panel test case showed that the panel position could be reconstructed with micrometric accuracy, rather independently of the data assimilation algorithm and the seeding concentration. The accurate evaluation of the static pressure field and of the surface pressure proved to be a challenge, with typical errors between 3 and 20% of the free-stream dynamic pressure.

Ultrafast MRI for Pediatric Brain Assessment in Routine Clinical Practice.

To assess the feasibility of ultrafast brain magnetic resonance imaging (MRI) in pediatric patients. We retrospectively reviewed 194 pediatric patients aged 0 to 19 years (median 10.2 years) who underwent both ultrafast and conventional brain MRI between May 2019 and August 2020. Ultrafast MRI sequences included T1 and T2-weighted images (T1WI and T2WI), fluid-attenuated inversion recovery (FLAIR), T2*-weighted image (T2*WI), and diffusion-weighted image (DWI). Qualitative image quality and lesion evaluations were conducted on 5-point Likert scales by two blinded radiologists, with quantitative assessment of lesion count and size on T1WI, T2WI, and FLAIR sequences for each protocol. Wilcoxon signed-rank tests and intraclass correlation coefficient (ICC) analyses were used for comparison. The total scan times for equivalent image contrasts were 1 minute 44 seconds for ultrafast MRI and 15 minutes 30 seconds for conventional MRI. Overall, image quality was lower in ultrafast MRI than in conventional MRI, with mean quality scores ranging from 2.0 to 4.8 for ultrafast MRI and 4.8 to 5.0 for conventional MRI across sequences (P < 0.001 for T1WI, T2WI, FLAIR, and T2*WI for both readers; P = 0.018 [reader 1] and 0.031 [reader 2] for DWI). Lesion detection rates on ultrafast MRI relative to conventional MRI were as follows: T1WI, 97.1%; T2WI, 99.6%; FLAIR, 92.9%; T2*WI, 74.1%; and DWI, 100%. The ICC (95% confidence interval) for lesion size measurements between ultrafast and conventional MRI was as follows: T1WI, 0.998 (0.996-0.999); T2WI, 0.998 (0.997-0.999); and FLAIR, 0.99 (0.985-0.994). Ultrafast MRI significantly reduces scan time and provides acceptable results, albeit with slightly lower image quality than conventional MRI, for evaluating intracranial abnormalities in pediatric patients.

A comparison of routine [68Ga]Ga-PSMA-11 preparation using Locametz and Illuccix kits

BackgroundApproval of Locametz and Illuccix kits for the manufacture of [68Ga]Ga-PSMA-11 (gallium Ga68 gozetotide), a PET imaging agent for prostate cancer, as well as the corresponding therapeutic ([177Lu]Lu-PSMA-617 Pluvicto), has led to a rapid increase in demand for [68Ga]Ga-PSMA-11 PET imaging. Radiopharmaceutical manufacturers, using 68Ge/68Ga generators, may decide to adopt Locametz and/or Illuccix kits, which requires a comparison to select the most suitable kit for day-to-day use. The objective of this article is to compare both kits and provide guidance for selecting one for routine use, as well as evaluate labeling consistency of both kits during routine production. Additionally, we report our experience during 1.5 years of daily [68Ga]Ga-PSMA-11 production at our facility using both kits.ResultsLocametz (n = 181) and Illuccix (n = 256) kits were prepared using non-silicone coated and silicone-coated needles with 68Ga activities ranging from 0.53 to 3.16 GBq, with a failure rate of 1 in 128 runs for both kits. With Locametz, a 3.7 GBq generator and 10-min incubation at room temperature gave doses that passed quality control (QC) testing. Use of non-silicone coated needles in the process led to solution discoloration, and QC failure. Additionally, lack of vial inversion led to inconsistent labeling, which improved with subsequent vial agitation. For Illuccix, addition of the acetate buffer to the precursor vial prior to adding the [68Ga]GaCl3 simplifies the workflow. The maximum tolerated activity was 1.85 GBq. Lack of vial inversion led to failures, which were rectified by agitating the vial to properly incorporate the acetate solution with the generator eluate.ConclusionsBoth kits benefited from using a syringe pump to elute the 68Ge/68Ga generator, vial agitation, and longer length/smaller bore silicone coated needles. Both kits have similar workflows, comparable QC outcomes, and result in equivalent clinical images. Thus, the decision between kits will ultimately be determined by production preferences. Since radiopharmacies have an established “kit-based” workflow, Locametz kits with higher allowed activities and longer shelf-life may offer benefits. Conversely, more traditional PET manufacturing facilities might benefit from using Illuccix kits due to compatibility with cyclotron-produced [68Ga]GaCl3 allowing for kit batching. Ultimately, the commercial availability of 2 approved kits for production of [68Ga]Ga-PSMA-11 PET has facilitated ready access to this important new imaging agent.

Impact of a Prototype 29:1 Ratio Grid on Image Quality and Radiation Dose in Abdominal Angiography

Objectives The aim of this study was to evaluate the impact of a prototype grid with a 29:1 ratio (r29) and a 15:1 (r15) grid on the image quality (IQ) and radiation dose in abdominal angiography. Materials and Methods Six typical abdominal angiographic image scenarios were created in 4 pigs. Polymethylmethacrylate and aluminum plates were used to add 10 cm of patient equivalent thickness to simulate different body types. Fluoroscopic images were acquired with a source-to-image receptor distance of 120 cm. Tantalum- and iron-specific acquisition protocols at different IQ levels were acquired. IQ of radiation dose equivalent image pairs, created with the r29 and r15 grids, respectively, was quantitatively evaluated using contrast-to-noise ratio (CNR) measurements. Differences in radiation dose were estimated using the dose-weighted CNR. Two blinded readers compared IQ of these images using a Likert scale. In a second step, the readers selected pairs of the r29 and r15 images with subjectively equivalent IQ. Radiation doses were then compared. Results Compared with the r15 grid, the r29 grid images achieved similar CNR at an average of 26% (±12%) lower radiation dose at a mean patient equivalent thickness of 26 cm and 36 cm. Both readers noted a significant increase in IQ (P &lt; 0.001) for dose equivalent images, whereas the interobserver agreement was 0.59. For the selected IQ equivalent images, a radiation dose reduction of 38% (±17%; P &lt; 0.001, interobserver agreement 0.92) was noted when using the r29 grid. Conclusions The use of an r29 grid at a large source-to-image receptor distance can significantly improve the IQ compared with the r15 grid at the same radiation dose in abdominal angiography or can reduce radiation dose while preserving IQ.

An seamless stitching method for large field equivalent center projection image based on rotating camera

Digital cameras are limited by a narrow field of view and a large photosensitive unit, resulting in images with a small frame size and low resolution. This reduces the acquisition range and measurement accuracy of stereo vision in close-range photogrammetry, making it difficult to meet the requirements for precise close-range photogrammetry in high-precision industrial engineering fields, and limiting the significant development of digital close-range photogrammetry. For this reason, based on the characteristics of ground close-range photogrammetry, this paper proposes a large-format image acquisition method for rotating cameras. By designing a simple and structurally relaxed rotating camera, a rigorous seamless stitching model for large-format images is constructed, forming a large-format equivalent central projection image acquisition mechanism that meets the requirements of precise close-range photogrammetry. Finally, the effectiveness of the proposed method is verified through experiments. The results show that the proposed method effectively increases the coverage of a single camera station. The large-format image obtained through three degrees of rotation increases the image size from 916 × 687 pixels in a single image to 4977 × 671 pixels in a large-format image. This method solves the problem of the small view field of digital cameras, complementing the theory of precision close-range photogrammetry and providing necessary theoretical support for technological development in the field of precision industrial engineering.

Imaging pollen using a Raspberry Pi and LED with deep learning

The production of low-cost, small footprint imaging sensor would be invaluable for airborne global monitoring of pollen, which could allow for mitigation of hay fever symptoms. We demonstrate the use of a white light LED (light emitting diode) to illuminate pollen grains and capture their scattering pattern using a Raspberry Pi camera. The scattering patterns are transformed into 20× microscope magnification equivalent images using deep learning. We show the ability to produce images of pollen from plant species previously unseen by the neural network in training. Such a technique could be applied to imaging airborne particulates that contribute to air pollution, and could be used in the field of environmental science, health science and agriculture.

A paradigm shift in oncology imaging: a prospective cross-sectional study to assess low-dose deep learning image reconstruction versus standard-dose iterative reconstruction for comprehensive lesion detection in dual-energy computed tomography.

Low-kiloelectron volt (keV) virtual monochromatic images (VMIs) from low-dose (LD) dual-energy computed tomography (DECT) can enhance lesion contrast but suffer from high image noise. Recently, a deep learning image reconstruction (DLIR) algorithm has been developed and shown significant potential in suppressing image noise and improving image quality. To date, the capacity of LD low-keV thoracic-abdominal-pelvic DECT with DLIR to detect various types of tumor lesions have not been assessed. Hence, this study aimed to evaluate the image quality and lesion detection capabilities of LD VMIs using DLIR with thoracic-abdominal-pelvic DECT versus standard-dose (SD) iterative reconstruction (IR) in oncology patients. This prospective intraindividual study included 56 oncology patients who received a SD (13.86 mGy) and a consecutive LD (7.15 mGy) thoracic-abdominal-pelvic DECT from April 2022 to July 2023 at The First Affiliated hospital of Zhengzhou University. SD VMIs were reconstructed using IR at 50 keV (SD-IR50 keV), while LD VMIs were processed using DLIR at 50 keV (LD-DL50 keV) and 40 keV (LD-DL40 keV), respectively. Quantitative image parameters [computed tomography (CT) values, image noise, and contrast-to-noise ratios (CNRs)], qualitative metrics (image noise, vessel conspicuity, image contrast, artificial sensation, and overall image quality), and lesion CNRs and conspicuity were compared. The lesion detection rates in the SD-IR50 keV, LD-DL50 keV, and LD-DL40 keV VMIs were assessed according to lesion location (lung, liver, and lymph), type, and size. Repeated measures analysis of variance and the Friedman test were applied for comparing quantitative and qualitative measures, respectively. The Cochran Q test was used for comparing lesion detection rates. Compared to SD-IR50 keV VMIs, LD-DL50 keV VMIs showed similar CT values and image noise (P>0.05), similar (P>0.05) or higher(P<0.05) CNRs, similar (P>0.05) or superior (P<0.05) perceptual image quality, and similar (P>0.05) or higher (P<0.001) lesion CNR and conspicuity. LD-DL40 keV VMIs exhibited higher CT values (by 40.4-47.1%) and CNRs (by 21.8-39.8%) (P<0.001), equivalent image noise, similar (P>0.05) or superior (P<0.05) perceptual image quality except for artificial sensation, and similar (P>0.05) or higher (P<0.001) lesion CNRs (by 16.5-46.3%) and conspicuity. The VMIs of LD-DL50 keV and LD-DL40 keV were consistent with those of SD-IR50 keV in terms of lesion detection capability in pulmonary nodules [SD-IR50 keV vs. LD-DL50 keV vs. LD-DL40 keV: 88/88 (100.0%) vs. 88/88 (100.0%) vs. 88/88 (100.0%); P>0.99], for lymph nodes [125/126 (99.2%) vs. 123/126 (97.6%) vs. 124/126 (98.4%); P>0.05], and high-contrast liver lesions [12/12 (100.0%) vs. 12/12 (100.0%) vs. 12/12 (100.0%); P>0.05], but not for small liver lesions (≤0.5 cm) [63/65 (96.9%) vs. 43/65 (66.2%) vs. 51/65 (78.5%); P<0.05] or low-contrast liver lesions [198/200 (99.0%) vs. 174/200 (87.0%) vs. 183/200 (91.5%); P<0.05]. VMIs at 40 keV with DLIR enables a 50% decrease in the radiation dose while largely maintaining diagnostic capabilities for multidetection of pulmonary nodules, lymph nodes, and liver lesions in oncology patients.

Compressed SENSitivity Encoding (SENSE): Qualitative and Quantitative Analysis.

This study aimed to qualitatively and quantitatively evaluate T1-TSE, T2-TSE and 3D FLAIR sequences obtained with and without Compressed-SENSE technique by assessing the contrast (C), the contrast-to-noise ratio (CNR) and the signal-to-noise ratio (SNR). A total of 142 MRI images were acquired: 69 with Compressed-SENSE and 73 without Compressed-SENSE. All the MRI images were contoured, spatially aligned and co-registered using 3D Slicer Software. Two radiologists manually drew 12 regions of interests on three different structures of CNS: white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF). C values were significantly higher in Compressed-SENSE T1-TSE compared to No Compressed-SENSE T1-TSE for three different structures of the CNS. C values were also significantly lower for Compressed-SENSE 3D FLAIR and Compressed-SENSE T2-TSE compared to the corresponding No Compressed-SENSE scans. While CNR values did not significantly differ in GM-WM between Compressed-SENSE and No Compressed-SENSE for the 3D FLAIR and T1-TSE sequences, the differences in GM-CSF and WM-CSF were always statistically significant. Compressed-SENSE for 3D T2 FLAIR, T1w and T2w sequences enables faster MRI acquisition, reducing scan time and maintaining equivalent image quality. Compressed-SENSE is very useful in specific medical conditions where lower SAR levels are required without sacrificing the acquisition of helpful diagnostic sequences.

Near field food contamination detection technique employing a compact Antipodal Vivaldi antenna

This paper presents a simple yet robust technique for identification of the presence of contaminants in food samples by detecting the variation in the transmission coefficient (S21) in a near field measurement setup. A compact step-notched antipodal Vivaldi antenna with a frequency range of operation 3 - 18 GHz, and realized gain of 12.81 dB at 16 GHz having compact dimensions 60mm×40mm×0.51mm, i.e., 0.6×0.4×0.005λ03 (where λ0 is the free space wavelength at the lowest frequency of operation) is designed for contamination detection due to its compact size, high directivity and high boresight gain for near-field measurement. An equivalent image is constructed from the S - parameters, measured at different positions and different antenna polarizations over the frequency range of 3-18 GHz for metallic contaminants, i.e., iron ball and aluminum foil. An algorithm is developed for detection of contamination from the images using the reference case of no-contamination scenario. The proposed algorithm is validated using full-wave simulation.

An efficient ultrasonic wavenumber-domain plane wave imaging method towards the inspection of curved structures

Ultrasonic phased array testing is commonly employed for inspecting curved structures. Conventional plane wave imaging techniques, based on delay-and-sum in the time-domain, offer high image quality and inspection accuracy but suffer from low frame rates due to their high computational complexity. In this work, an efficient wavenumber-domain imaging method that combines non-stationary wavefield extrapolation and f-k migration is proposed for curved structure inspection. Special emission focal laws are designed to generate a sequence of steered plane waves through the curved interface. The raw data is then extrapolated to the top boundary of the region of interest, followed by f-k migration to reconstruct images with high time efficiency. Simulation and experimental evaluations demonstrate a time reduction by a factor of up to 32.24 compared to conventional time-domain plane wave image reconstruction with equivalent image quality, highlighting its potential for monitoring flaws in real-time.

Intracardiac Echocardiography-Applications in the Electrophysiology and the Cardiac Catheterization Labs.

Background. Intracardiac echocardiography (ICE) is routinely used in cardiac electrophysiology and catheterization labs. It plays a vital role in understanding cardiac anatomy, procedural planning, and early identification of complications. In this review, we describe the utility of ICE for procedures in the electrophysiology lab, including atrial fibrillation ablation, left atrial appendage occlusion device implantation, and cardiac implantable electronic device (CIED) extraction. Intracardiac echocardiography also helps in the identification of complications such as pericardial effusion, pulmonary vein stenosis, and left atrial appendage thrombus. Compared with traditional echocardiographic modalities such as transesophageal echocardiogram (TEE), ICE has equivalent image quality, requires less sedation, and possesses no risk of esophageal injury. The disadvantages of ICE include a learning curve and necessity for central vascular access.

Clinical evaluation of deep learning-enhanced lymphoma pet imaging with accelerated acquisition.

This study aims to evaluate the clinical performance of a deep learning (DL)-enhanced two-fold accelerated PET imaging method in patients with lymphoma. A total of 123 cases devoid of lymphoma underwent whole-body 18F-FDG-PET/CT scans to facilitate the development of an advanced SAU2Net model, which combines the advantages of U2Net and attention mechanism. This model integrated inputs from simulated 1/2-dose (0.07 mCi/kg) PET acquisition across multiple slices to generate an estimated standard dose (0.14 mCi/kg) PET scan. Additional 39 cases with confirmed lymphoma pathology were utilized to evaluate the model's clinical performance. Assessment criteria encompassed peak-signal-to-noise ratio (PSNR), structural similarity index (SSIM), a 5-point Likert scale rated by two experienced physicians, SUV features, image noise in the liver, and contrast-to-noise ratio (CNR). Diagnostic outcomes, including lesion numbers and Deauville score, were also compared. Images enhanced by the proposed DL method exhibited superior image quality (P<0.001) in comparison to low-dose acquisition. Moreover, they illustrated equivalent image quality in terms of subjective image analysis and lesion maximum standardized uptake value (SUVmax) as compared to the standard acquisition method. A linear regression model with y=1.017x + 0.110 ( ) can be established between the enhanced scans and the standard acquisition for lesion SUVmax. With enhancement, increasedsignal-to-noise ratio (SNR), CNR, and reduced image noise were observed, surpassing those of the standard acquisition. DL-enhanced PET images got diagnostic results essentially equavalent to standard PET images according totwo experienced readers. The proposed DL method could facilitate a 50% reduction in PET imaging duration for lymphoma patients, while concurrently preserving image quality and diagnostic accuracy.

Breast density prediction from low and standard dose mammograms using deep learning: effect of image resolution and model training approach on prediction quality

Purpose. To improve breast cancer risk prediction for young women, we have developed deep learning methods to estimate mammographic density from low dose mammograms taken at approximately 1/10th of the usual dose. We investigate the quality and reliability of the density scores produced on low dose mammograms focussing on how image resolution and levels of training affect the low dose predictions. Methods. Deep learning models are developed and tested, with two feature extraction methods and an end-to-end trained method, on five different resolutions of 15,290 standard dose and simulated low dose mammograms with known labels. The models are further tested on a dataset with 296 matching standard and real low dose images allowing performance on the low dose images to be ascertained. Results. Prediction quality on standard and simulated low dose images compared to labels is similar for all equivalent model training and image resolution versions. Increasing resolution results in improved performance of both feature extraction methods for standard and simulated low dose images, while the trained models show high performance across the resolutions. For the trained models the Spearman rank correlation coefficient between predictions of standard and low dose images at low resolution is 0.951 (0.937 to 0.960) and at the highest resolution 0.956 (0.942 to 0.965). If pairs of model predictions are averaged, similarity increases. Conclusions. Deep learning mammographic density predictions on low dose mammograms are highly correlated with standard dose equivalents for feature extraction and end-to-end approaches across multiple image resolutions. Deep learning models can reliably make high quality mammographic density predictions on low dose mammograms.

Validation and Feasibility of Ultrafast Cervical Spine MRI Using a Deep Learning-Assisted 3D Iterative Image Enhancement System.

This study aimed to evaluate the feasibility of ultrafast (2 min) cervical spine MRI protocol using a deep learning-assisted 3D iterative image enhancement (DL-3DIIE) system, compared to a conventional MRI protocol (6 min 14s). Fifty-one patients were recruited and underwent cervical spine MRI using conventional and ultrafast protocols. A DL-3DIIE system was applied to the ultrafast protocol to compensate for the spatial resolution and signal-to-noise ratio (SNR) of images. Two radiologists independently assessed and graded the quality of images from the dimensions of artifacts, boundary sharpness, visibility of lesions and overall image quality. We recorded the presence or absence of different pathologies. Moreover, we examined the interchangeability of the two protocols by computing the 95% confidence interval of the individual equivalence index, and also evaluated the inter-protocol intra-observer agreement using Cohen's weighted kappa. Ultrafast-DL-3DIIE images were significantly better than conventional ones for artifacts and equivalent for other qualitative features. The number of cases with different kinds of pathologies was indistinguishable based on the MR images from ultrafast-DL-3DIIE and conventional protocols. With the exception of disc degeneration, the 95% confidence interval for the individual equivalence index across all variables did not surpass 5%, suggesting that the two protocols are interchangeable. The kappa values of these evaluations by the two radiologists ranged from 0.65 to 0.88, indicating good-to-excellent agreement. The DL-3DIIE system enables 67% spine MRI scan time reduction while obtaining at least equivalent image quality and diagnostic results compared to the conventional protocol, suggesting its potential for clinical utility.

Modelization of extended axial field-of-view PET scanners to analyze the performance improvement

Positron Emission Tomography (PET) offers non-invasive insights into physiological processes. However, in clinical practice conventional PET scanners face limitations in comprehensive imaging due to their restricted axial field of view (FOV).This study explores the benefits and challenges of extending PET scanner AFOV using Monte Carlo simulation with GATE 9.1. The Biograph Vision Quadra scanner is a novel system based on the Digital Biograph Vision 600 scanner. Both devices use silicon photomultipliers and 3.2 x 3.2 × 20 mm Lutetium Oxyorthosilicate crystals (LSO). However, the 32 detector rings of the Quadra scanner enable a four-fold expansion in the Axial Field Of View (FOV) compared to Vision. As a result, higher sensitivity, and an enhancement of noise equivalent count rate and image quality are achieved. The purpose of this study is to analyze the effect of increasing the number of detector rings and the axial FOV on the scanner performance. To accomplish this, four different geometries are modeled using GATE (GEANT4 Application for Tomographic Emission). Based on the Biograph Vision 600 model, the length and the number of detector rings are gradually extended to reach the length of the Quadra scanner. Thus, four geometries with 8 (25.6 cm), 16 (51.2 cm), 24 (76.8 cm) and 32 (102.4 cm) detector rings are simulated, keeping the crystal configuration and the dimensions in the transaxial plane. The study evaluates sensitivity, Noise Equivalent Count Rate (NECR), and image quality parameters. Results indicate a significant increase in sensitivity and NECR with FOV extension, underscoring enhanced performance. However, artifact occurrence, particularly concentric circular patterns and background homogeneity changes, accentuates with extended AFOV. Normalization algorithms, particularly component-based normalization, mitigate these artifacts. This study contributes to the understanding of the advantages and limitations of extending PET scanner AFOV, offering insights into the effectiveness of component-based normalization for improving PET image quality.

Non-spherical Janus microparticles localization using equivalent geometric center and image processing

In recent years, Janus microspheres technology has been widely applied and developed in various fields. However, existing studies often rely on electron microscopy images, treating Janus microspheres as spherical to observe their submicron structures. This approach falls short in meeting the demand for precise measurement of the specific position and orientation of microspheres at the single-molecule scale. Here, we propose a standardized processing procedure that integrates multiple image processing algorithms to measure the position of Janus microspheres through the image capture. Our approach enhances the two-dimensional planar positioning accuracy of non-spherical Janus microspheres in micro-nano optical measurement systems. Utilizing this method, we can conduct a further research on the structure and motion characteristics of the microsphere at the single-particle scale. This method has been validated on a transmission illumination optical tweezers platforms, successfully calibrating the central position of the optical trap. Our work offers a greater convenience for measuring non-spherical Janus microspheres in liquid environments. It also provides a fresh perspective for subsequent studies on the motion trajectories and force dynamics of heterogeneous microspheres, showcasing its broad potential for application.

Virtual histological staining of unlabeled autopsy tissue

Traditional histochemical staining of post-mortem samples often confronts inferior staining quality due to autolysis caused by delayed fixation of cadaver tissue, and such chemical staining procedures covering large tissue areas demand substantial labor, cost and time. Here, we demonstrate virtual staining of autopsy tissue using a trained neural network to rapidly transform autofluorescence images of label-free autopsy tissue sections into brightfield equivalent images, matching hematoxylin and eosin (H&E) stained versions of the same samples. The trained model can effectively accentuate nuclear, cytoplasmic and extracellular features in new autopsy tissue samples that experienced severe autolysis, such as COVID-19 samples never seen before, where the traditional histochemical staining fails to provide consistent staining quality. This virtual autopsy staining technique provides a rapid and resource-efficient solution to generate artifact-free H&E stains despite severe autolysis and cell death, also reducing labor, cost and infrastructure requirements associated with the standard histochemical staining.

The Effect of Prone and Supine Limb Positioning on the Radiographic Evaluation of Posterolateral Plate Fixation of the Posterior Malleolus.

To facilitate the posterolateral approach to the posterior malleolus patients are often positioned prone initially, then turned supine to complete fixation at the medial malleolus. We sought to define observed differences in the radiographic appearance of implants relative to the joint line, in prone and supine positions. A 3.5mm tubular plate and a 3.5mm posterior distal tibial periarticular plate were applied sequentially to 3 individual cadaveric legs, via a posterolateral approach. The tubular plate was positioned to simulate buttress fixation and the posterolateral plate placed more distally. Each limb was secured on a custom jig and radiographs were taken on a mobile c-arm fluoroscopy machine with a calibration ball. A series of prone AP, supine PA and mortise radiographs were taken. Prone radiographs were also taken in different degrees of caudal tilt to simulate knee flexion which occurs in practice, during intraoperative positioning. Plate tip-joint line distances were measured and Mann-Whitney U tests performed. There was no statistically significant difference in plate tip-joint line distance when comparing equivalent prone and supine views (PA/AP or mortise). However, significant differences in apparent implant position were noted with alterations in caudal tilt. When taking a prone image, when the knee is flexed to 20 degrees, the plate tip will appear 6.5-8.5mm more proximal than in the equivalent supine image where the knee is extended and the fluoroscopy beam is orthogonal to the anatomic axis of the tibia. Observed differences in radiographic appearance of metalwork in the prone and supine position are most likely due to knee flexion and the resulting variation in the angle of the fluoroscopy beam, rather than projectional differences between supine and prone views. Surgeons should be alert to this when analysing intraoperative images.

Evaluation of Spectral X-Ray Imaging for Panoramic Dental Images Based on a Simulation Framework.

Modern photon counting detectors allow the calculation of virtual monoenergetic or material decomposed X-ray images but are not yet used for dental panoramic radiography systems. To assess the diagnostic potential and image quality of photon counting detectors in dental panoramic radiography, ethics approval from the local ethics committee was obtained for this retrospective study. Conventional CT scans of the head and neck region were segmented into bone and soft tissue. The resulting datasets were used to calculate panoramic equivalent thickness bone and soft tissue images by forward projection, using a geometry like that of conventional panoramic radiographic systems. The panoramic equivalent thickness images were utilized to generate synthetic conventional panoramic radiographs and panoramic virtual monoenergetic radiographs at various energies. The conventional, two virtual monoenergetic images at 40keV and 60keV, and material-separated bone and soft tissue panoramic equivalent thickness X-ray images simulated from 17 head CTs were evaluated in a reader study involving three experienced radiologists regarding their diagnostic value and image quality. Compared to conventional panoramic radiographs, the material-separated bone panoramic equivalent thickness image exhibits a higher image quality and diagnostic value in assessing the bone structure and details such as teeth or root canals . Panoramic virtual monoenergetic radiographs do not show a significant advantage over conventional panoramic radiographs. The conducted reader study shows the potential of spectral X-ray imaging for dental panoramic imaging to improve the diagnostic value and image quality.

Mesoscale FEM approach on cemented sands: Generating and testing the digital twin

The mechanics of Cemented Granular Material (CGM) have been studied by means of geotechnical experimental testing, whose output consists the basis of mathematical models which approach the material response in various loading states. The information derived from standard experimental response curves is the basis of understanding and handling the material. Still, it is intuitive to analyse the CGM down to the mesoscale and dray conclusions over the interaction of the constituent material phases. Diverging from the practice of equivalent continuum, the alternative description of a three phase composite of sand particles, cement binder and void pores has been realised in this study. In order to implement the specific morphology of this multiphase, geomaterial, X-ray Computed Tomography is used to capture the internal structure and quantify it into a three dimensional greyvalue map (or a three dimensional image). The distinction of the material phases is made possible by the application of a developed filter, which corrects the artefacts caused by beam hardening phenomena and allows for the generation of a phase segmented equivalent image. An image adapted meshing algorithm has been utilized to transform the labelled image into a tetrahedral mesh, grouped into sets that correspond to the different materials. The tetrahedral domain was assigned boundary conditions and was numerically tested under uniaxial compression using the finite element method. The kinematics of the simulation proved that the mesoscale approach, which carries internal structure information of the granular fabric and the cement paste distribution, provides a output which captures the kinematics of the granular skeleton.