Virtual Objects Research Articles

The optimal energy level of virtual monochromatic imaging in Dual-energy CT arthrography of the wrist.

To suggest an optimal energy level of virtual monochromatic images (VMIs) in dual-energy CT arthrography of the wrist. This retrospective study included 53 patients with wrist CT arthrography. Conventional polychromatic images and VMIs at four energy levels (40 to 70 keV at 10 keV intervals) were obtained. Image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured, and qualitative analysis of image quality and diagnostic confidence was performed. For each patient, an energy level with the best image quality was chosen by consensus. Comparisons of quantitative and qualitative parameters between VMI sets were performed. The image noise of bone and muscle were increased with decreasing energy level (p < 0.001). The noise of contrast was highest on 60 keV VMI. SNR and CNR (between contrast and muscle) were increased with decreasing energy level and were markedly increased between 60 and 50 keV (p < 0.001). The 60 keV VMI demonstrated the highest image quality and diagnostic confidence, chosen as the best diagnostic image (n = 31/53). Given that the attenuation of the contrast material was low on the conventional image, the optimal energy level of the best VMI tended to be low. Wrist dual-energy CT arthrography with VMIs at 60 keV or less could improve image quality and diagnostic performance by increasing SNR and CNR in cases with low contrast attenuation. Wrist dual-energy CT arthrography with VMIs at variable keV could be utilised to enhance SNR and CNR, thereby achieving diagnostic images of high quality.

Mormyroidea-inspired electronic skin for active non-contact three-dimensional tracking and sensing.

The capacity to discern and locate positions in three-dimensional space is crucial for human-machine interfaces and robotic perception. However, current soft electronics can only obtain two-dimensional spatial locations through physical contact. In this study, we report a non-contact position targeting concept enabled by transparent and thin soft electronic skin (E-skin) with three-dimensional sensing capabilities. Inspired by the active electrosensation of mormyroidea fish, this E-skin actively ascertains the 3D positions of targeted objects in a contactless manner and can wirelessly convey the corresponding positions to other devices in real-time. Consequently, this E-skin readily enables interaction with machines, i.e., manipulating virtual objects, controlling robotic arms, and drones in either virtual or actual 3D space. Additionally, it can be integrated with robots to provide them with 3D situational awareness for perceiving their surroundings, avoiding obstacles, or tracking targets.

Digital Dark Field-Higher Contrast and Greater Specificity Dark Field Imaging Using a 4DSTEM Approach.

A new method for dark field imaging is introduced, which uses scanned electron diffraction (or 4DSTEM-4-dimensional scanning transmission electron microscopy) datasets as its input. Instead of working on simple summation of intensity, it works on a sparse representation of the diffraction patterns in terms of a list of their diffraction peaks. This is tested on a thin perovskite film containing structural ordering resulting in additional superlattice spots that reveal details of domain structures, and is shown to give much better selectivity and contrast than conventional virtual dark field imaging. It is also shown to work well in polycrystalline aggregates of CuO nanoparticles. In view of the higher contrast and selectivity, and the complete exclusion of diffuse scattering from the image formation, it is expected to be of significant benefit for characterization of a wide variety of crystalline materials.

Improved Diagnostic Performance Using Dual-Energy CT-Derived Slope Parameter Images in Crohn's Disease.

The objective of the study is to explore the image quality and diagnosis performance of the dual-energy CT-derived slope parameter images (SPI) generated by the algorithm based on the slope function in the diagnosis of Crohn's disease (CD). Seventy-six CD patients and 53 disease-free control group subjects who underwent dual-energy CT enterography were retrospectively collected. Portal venous phase 120kVp-like and virtual monoenergetic images at 40-100keV (VMI40-100) were reconstructed. SPIs corresponding to the spectral curve between 40 and 100keV (SPI40-100) were generated using Python. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of normal and abnormal intestinal walls were calculated. Image quality, noise, and contrast were independently scored by two radiologists using a 5-point scale. Four radiologists conducted CD diagnosis with three reading models (120kVp-like, 120kVp-like with optimal VMI, and 120kVp-like with SPI40-100). The diagnostic performances of the three reading models for diagnosing CD were evaluated using receiver operating characteristic (ROC) curves. The CNR in SPI40-100 was higher than in the other images (P < 0.05). The subjective evaluation showed that there was no statistical difference between the contrast of SPI40-100 and VMI40 (P > 0.05), but that of the two images was higher than the other images (P < 0.05). The scoring on the overall image quality of VMI50 was superior to that of other images (P < 0.05). The combined model of 120kVp-like with SPI40-100 had the strongest confidence (cases with high confidence: 36, 58, 49, 47 for radiologists 1, 2, 3, 4) and the highest efficiency in diagnosing CD (areas under the ROC curve: 0.973, 0.977, 0.982, 0.991 for radiologists 1, 2, 3, 4). SPI40-100 generated by the algorithm based on the slope function exhibited good image quality. The combined model of 120kVp-like with SPI40-100 could improve radiologists' diagnostic efficiency and confidence in diagnosing CD.

An experimental study on the diagnostic advantage of dual-energy computed tomography over single-energy scan to evaluate the treatment effect following transcatheter arterial chemoembolization.

We assessed the diagnostic advantage of dual-energy computed tomography (DECT) over single-energy computed tomography (SECT) to evaluate lipiodol accumulation in target lesions following transcatheter arterial chemoembolization (TACE). TACE was performed in 10 rabbits in whom the VX2 tumor was implanted in their left liver lobes. The miriplatin-lipiodol mixture was injected into the common hepatic artery. All rabbits were sacrificed 2 days after TACE, and the liver was harvested. CT was performed using both single-energy and dual-energy scan modes. The specimen was stained with Oil Red O to evaluate lipiodol accumulation; this was considered the reference standard. Mutual information (MI) was used to evaluate the significance of radiological-pathological correlation. Estimated iodine content values on iodine material density images were compared with actual values obtained using mass spectroscopy. Mean MI values were 0.69, 0.32, 0.83, 0.72, 0.65, and 0.58 for single-energy scan; iodine density images; and virtual monoenergetic images for energy levels of 40, 60, 80, and 100 keV, respectively. The MI value of the monochromatic image (40 keV) was the highest among all sequences. However, this was not significant compared with the single-energy scan (p = 0.81). A significant correlation was observed between the estimated and actual values of iodine content (Pearson's product moment coefficient = 0.70, p = 0.023). More accurate and quantitative lipiodol evaluation in targeted tumors after TACE can be achieved by applying DECT rather than SECT.

Influence of habituation on pseudo-haptic weight perception of virtual objects

As the demand for realistic sensations in virtual reality (VR) environments escalates, interfaces that exploit cross-modal interactions between vision and haptics are gaining prominence. Pseudo-haptics, offering a tactile illusion without mechanical feedback devices, has emerged as a viable solution to enhance user immersion by simulating various sensations such as texture, fluid resistance, and weight. However, a potential issue in the long-term effectiveness of these illusions, particularly habituation, where the illusion diminishes after prolonged exposure, remains a concern. This study investigates whether habituation to pseudo-haptic weight illusions occurs with extended use. We conducted quantitative measurements of pseudo-haptic weight perception before and after prolonged exposure. The findings indicate a diminishing effect of pseudo-haptic weight illusion with repeated exposure, particularly among participants with high accuracy in weight perception. This study contributes important insights into the design of VR experiences, highlighting the need to consider the habituation effects when implementing pseudo-haptic illusions for weight perception.

Reflectance mapping with microsphere-assisted white light interference nanoscopy

The characterisation of novel materials presents a challenge that requires new and original developments. To face some of these demands for making measurements at the nanoscale, a new microsphere-assisted white light interference nanoscope performing local reflectance mapping is presented. This technique presents the advantages of being non-destructive, full-field and label-free. A 145 μm diameter microsphere, glued to the end of an optical fiber, is inserted inside the white light interference microscope to improve the lateral resolution from 940 nm to 520 nm. The acquisition and the Fourier transform processing of a stack of interference images superimposed on the virtual image produced by the microsphere allows the extraction of the local reflectance over a wavelength range of 460 nm to 900 nm and a field of view of 8 μm in diameter. The enhancement in the lateral resolution of the reflectance is demonstrated through the spectral distinction of neighboring ripples on a laser-textured colored stainless-steel sample that cannot be resolved without the microsphere, on regions with a surface of 279 × 279 nm2 horizontally spaced 279 nm apart. Future improvements could potentially lead to a lateral resolution of reflectance measurement over a 100 nm diameter area in air, paving the way to sub-diffraction reflectance mapping.

A framework to model charge sharing and pulse pileup for virtual imaging trials of photon-counting CT

Objective.This study describes the development, validation, and integration of a detector response model that accounts for the combined effects of x-ray crosstalk, charge sharing, and pulse pileup in photon-counting detectors.Approach.The x-ray photon transport was simulated using Geant4, followed by analytical charge sharing simulation in MATLAB. The analytical simulation models charge clouds with Gaussian-distributed charge densities, which are projected on a 3×3 pixel neighborhood of interaction location to compute detected counts. For pulse pileup, a prior analytical method for redistribution of energy-binned counts was implemented for delta pulses. The x-ray photon transport and charge sharing components were validated using experimental data acquired on the CdTe-based Pixirad-1/Pixie-III detector using monoenergetic beams at 26, 33, 37, and 50 keV. The pulse pileup implementation was verified with a comparable Monte Carlo simulation. The model output without pulse pileup was used to generate spatio-energetic response matrices for efficient simulation of scanner-specific photon-counting CT (PCCT) images with DukeSim, with pulse pileup modeled as a post-processing step on simulated projections. For analysis, images for the Gammex multi-energy phantom and the XCAT chest phantom were simulated at 120 kV, both with and without pulse pileup for a range of doses (27-1344 mAs). The XCAT images were evaluated qualitatively at 120 mAs, while images for the Gammex phantom were evaluated quantitatively for all doses using measurements of attenuation coefficients and Calcium concentrations.Main results.Reasonable agreement was observed between simulated and experimental spectra with Mean Absolute Percentage Error Values (MAPE) between 10%and 31%across all incident energies and detector modes. The increased pulse pileup from increased dose affected attenuation coefficients and calcium concentrations, with an effect on calcium quantification as high as MAPE of 28%.Significance.The presented approach demonstrates the viability of the model for enabling VITs to assess and optimize the clinical performance of PCCT.

Study on virtual tooth image generation utilizing CF-fill and Pix2pix for data augmentation.

Traditional dental prosthetics require a significant amount of work, labor, and time. To simplify the process, a method to convert teeth scan images, scanned using an intraoral scanner, into 3D images for design was developed. Furthermore, several studies have used deep learning to automate dental prosthetic processes. Tooth images are required to train deep learning models, but they are difficult to use in research because they contain personal patient information. Therefore, we propose a method for generating virtual tooth images using image-to-image translation (pix2pix) and contextual reconstruction fill (CR-Fill). Various virtual images can be generated using pix2pix, and the images are used as training images for CR-Fill to compare the real image with the virtual image to ensure that the teeth are well-shaped and meaningful. The experimental results demonstrate that the images generated by the proposed method are similar to actual images. In addition, only using virtual images as training data did not perform well; however, using both real and virtual images as training data yielded nearly identical results to using only real images as training data.

Deep Learning-Based In Situ Micrograph Synthesis and Augmentation for Crystallization Process Image Analysis

Deep learning-based in situ imaging and analysis for crystallization process are essential for optimizing product qualities, reducing experimental costs through real-time monitoring, and controlling the process. However, large and high-quality annotated datasets are required to train accurate models, which are time consuming. Therefore, we proposed a novel methodology that applied image synthesis neural networks to generate virtual information-rich images, enabling efficient and rapid dataset expansion while simultaneously reducing annotation costs. Experiments were conducted on the L-alanine crystallization process to obtain process images and to validate the proposed workflow. The proposed method, aided by interpolation augmentation and data warping augmentation to enhance data richness, utilized only 25% of the training annotations, consistently segmenting crystallization process images comparable to those models utilizing 100% of the training data annotations, achieving an average precision of nearly 98%. Additionally, based on the analysis of Kullback–Leibler divergence, the proposed method demonstrated excellent performance in extracting in situ information regarding aspect ratios and crystal size distributions during the crystallization process. Moreover, its ability to leverage expert labels with a four-fold enhanced efficiency holds great potential for advancing various applications in crystallization processes.

Dual Energy Metal Artifact Reduction for Iodine-125 Seed Identification in Postimplant CT after Prostate Brachytherapy.

To assess the metal artifact reductions of dual-energy computed tomography (DECT) high-energy virtual monochromatic images (VMI) combined with the single energy metal artifact reduction (SEMER) (CANON MEDICAL SYSTEMS, Otawara, Japan) processing techniques for iodine (I)-125 seed identification in postimplant computed tomography (CT) after prostate brachytherapy. Dual-energy acquisition with fast tube voltage switching was performed on a prostate phantom with simulated seeds and six clinical cases treated with I-125 prostate brachytherapy. The images were retrospectively reconstructed at VMI energy levels of 65-200 keV and with and without SEMAR (SEMAR and non-SEMAR images). To estimate seed swelling, the caliber of iodine-125 seed was calculated as the full width at half maximum. The metal artifacts were evaluated using the artifact index (AI). The dose distributions were calculated and were compared among the high-energy VMI (SEMAR and non-SEMAR images) and low-energy VMI (SEMAR images). The blooming artifacts decreased at higher energy levels. In addition, the SEMAR process markedly reduced AI, which helped reduce overestimation of high dose ranges in the treatment planning dose map. The locations and number of iodine-125 seed were clearly identified in the dose distribution map of the treatment planning using 200keV VMI with SEMAR. The high-energy VMI of the dual energy CT in combination with SEMAR is appropriate for the postimplant planning process of I-125 prostate brachytherapy.

Searching Across Realities: Investigating ERPs and Eye-Tracking Correlates of Visual Search in Mixed Reality.

Mixed Reality allows us to integrate virtual and physical content into users' environments seamlessly. Yet, how this fusion affects perceptual and cognitive resources and our ability to find virtual or physical objects remains uncertain. Displaying virtual and physical information simultaneously might lead to divided attention and increased visual complexity, impacting users' visual processing, performance, and workload. In a visual search task, we asked participants to locate virtual and physical objects in Augmented Reality and Augmented Virtuality to understand the effects on performance. We evaluated search efficiency and attention allocation for virtual and physical objects using event-related potentials, fixation and saccade metrics, and behavioral measures. We found that users were more efficient in identifying objects in Augmented Virtuality, while virtual objects gained saliency in Augmented Virtuality. This suggests that visual fidelity might increase the perceptual load of the scene. Reduced amplitude in distractor positivity ERP, and fixation patterns supported improved distractor suppression and search efficiency in Augmented Virtuality. We discuss design implications for mixed reality adaptive systems based on physiological inputs for interaction.

Impact of virtual monochromatic images of different low-energy levels in dual-energy CT on radiomics models for predicting muscle invasion in bladder cancer.

The purpose of this study was to investigate the impact of different low-energy virtual monochromatic images (VMIs) in dual-energy CT on the performance of radiomics models for predicting muscle invasive status in bladder cancer (BCa). A total of 127 patients with pathologically proven muscle-invasive BCa (n = 49) and non-muscle-invasive BCa (n = 78) were randomly allocated into the training and test cohorts at a ratio of 7:3. Feature extraction was performed on the venous phase images reconstructed at 40, 50, 60 and 70-keV (single-energy analysis) or in combination (multi-energy analysis). Recursive feature elimination (RFE) and the least absolute shrinkage and selection operator(LASSO) were employed to select the most relevant features associated with BCa. Models were built using a support vector machine(SVM) classifier. Diagnostic performance was assessed through receiver operating characteristic curves, evaluating sensitivity, specificity, accuracy, precision, and the area-under-the curve (AUC) values. In the test cohort, the multi-energy model achieved the best diagnostic performance with AUC, sensitivity, specificity, accuracy, and precision of 0.917, 0.800, 0.833, 0.821, and 0.750, respectively. Conversely, the single-energy model exhibited lower AUC and sensitivity in predicting the muscle invasion status. By combining information from VMIs of various energies, the multi-energy model displays superior performance in preoperatively predicting the muscle invasion status of bladder cancer.

Depth Perception in Optical See-Through Augmented Reality: Investigating the Impact of Texture Density, Luminance Contrast, and Color Contrast.

The immersive augmented reality (AR) system necessitates precise depth registration between virtual objects and the real scene. Prior studies have emphasized the efficacy of surface texture in providing depth cues to enhance depth perception across various media, including the real scene, virtual reality, and AR. However, these studies predominantly focus on black-and-white textures, leaving a gap in understanding the effectiveness of colored textures. To address this gap and further explore texture-related factors in AR, a series of experiments were conducted to investigate the effects of different texture cues on depth perception using the perceptual matching method. Findings indicate that the absolute depth error increases with decreasing contrast under black-and-white texture. Moreover, textures with higher color contrast also contribute to enhanced accuracy of depth judgments in AR. However, no significant effect of texture density on depth perception was observed. The findings serve as a theoretical reference for texture design in AR, aiding in the optimization of virtual-real registration processes.

CGLight: An effective indoor illumination estimation method based on improved convmixer and GauGAN

Illumination consistency is a key factor for seamlessly integrating virtual objects with real scenes in augmented reality (AR) systems. High dynamic range (HDR) panoramic images are widely used to estimate scene lighting accurately. However, generating environment maps requires complex deep network architectures, which cannot operate on devices with limited memory space. To address this issue, this paper proposes CGLight, an effective illumination estimation method that predicts HDR panoramic environment maps from a single limited field-of-view (LFOV) image. We first design a CMAtten encoder to extract features from input images, which learns the spherical harmonic (SH) lighting representation with fewer model parameters. Guided by the lighting parameters, we train a generative adversarial network (GAN) to generate HDR environment maps. In addition, to enrich lighting details and reduce training time, we specifically introduce the color consistency loss and independent discriminator, considering the impact of color properties on the lighting estimation task while improving computational efficiency. Furthermore, the effectiveness of CGLight is verified by relighting virtual objects using the predicted environment maps, and the root mean square error and angular error are 0.0494 and 4.0607 in the gray diffuse sphere, respectively. Extensive experiments and analyses demonstrate that CGLight achieves a balance between indoor illumination estimation accuracy and resource efficiency, attaining higher accuracy with nearly 4 times fewer model parameters than the ViT-B16 model.

Exploring the Efficacy of Various CNN Architectures in Diagnosing Oral Cancer from Squamous Cell Carcinoma

Oral cancer can result from mutations in cells located in the lips or mouth. Diagnosing oral cavity squamous cell carcinoma (OCSCC) is particularly challenging, often occurring at advanced stages. To address this, computer-aided diagnosis methods are increasingly being used. In this work, a deep learning-based approach utilizing models such as VGG16, ResNet50, LeNet-5, MobileNetV2, and Inception V3 is presented. NEOR and OCSCC datasets were used for feature extraction, with virtual slide images divided into tiles and classified as normal or squamous cell cancer. Performance metrics like accuracy, F1-score, AUC, precision, and recall were analyzed to determine the prerequisites for optimal CNN performance. The proposed CNN approaches were effective for classifying OCSCC and oral dysplasia, with the highest accuracy of 95.41% achieved using MobileNetV2. Key findingsDeep learning models, particularly MobileNetV2, achieved high classification accuracy (95.41%) for OCSCC.CNN-based methods show promise for early-stage OCSCC and oral dysplasia diagnosis. Performance parameters like precision, recall, and F1-score help optimize CNN model selection for this task.

Assessment of photon-counting Computed Tomography for quantitative imaging in radiation therapy: PCCT for quantitative imaging in radiotherapy.

To test a first generation clinical PCCT scanner's capabilities to characterize materials in an anthropomorphic head phantom for radiation therapy purposes. A CIRS 731-HN head-and-neck phantom (CIRS/SunNuclear, Norfolk, USA) was scanned on a NAEOTOM Alpha photon-counting CT (PCCT) and a SOMATOM Definition AS+ with single-energy and dual-energy CT techniques (SECT and DECT, respectively), both scanners manufactured by Siemens (Siemens Healthineers, Forscheim, Germany). A method was developed to derive relative electron density (RED) and effective atomic number (EAN) from linear attenuation coefficients (LAC) of virtual mono-energetic images and applied for the PCCT and DECT data. For DECT, Siemens' syngo.via 'Rho/Z'-algorithm was also utilized. Proton stopping-power ratios (SPR) were calculated based on RED/EAN with the Bethe equation. For SECT, a stoichiometric calibration to SPR was used. Nine materials in the phantom were segmented, excluding border pixels. Distributions and root-mean-square deviations (RMSD) within the material regions were evaluated for LAC, RED/EAN and SPR, respectively. Two example ray-projections were also examined for LAC, SPR and water-equivalent thickness (WET), for illustrations of a more treatment-like scenario. There was a tendency towards narrower distributions for PCCT compared to both DECT methods for the investigated quantities, observed across all materials for RED only. Likewise the scored RMSDs showed overall superiority for PCCT with a few exceptions: for water-like materials, EAN and SPR were comparable between the modalities; for titanium the RED and SPR estimates were inferior for PCCT. The PCCT data gave the smallest deviations from theoretic along the more complex example ray profile whereas the more standard projection showed similar results between the modalities. This study shows promising results for tissue characterization in a human-like geometry for radiotherapy purposes using PCCT. The significance of improvements for clinical practice remains to be demonstrated.

Endoluminal Visualization of Dural Arteriovenous Fistula Using Virtual Angioscopy

BackgroundVirtual Angioscopy (VA) has been used for vessel investigation and treatment planning in fields such as vascular surgery. To our knowledge, the utilization of VA in neurointerventional cases has not been demonstrated. Methods2 cases of dural arteriovenous fistula (dAVF) where virtual angioscopy was utilized were reviewed. Results2 cases of dAVF are reported where virtual angioscopic images were derived from 3D volume rendered (VR) images. The endoluminal visualization of the fistula served as additional tool that helped anatomic understanding and treatment planning. ConclusionThese cases demonstrate the utility of VA in endoluminal visualization of dural arteriovenous fistula that may aid in treatment planning considerations.

Augmented Reality Control based Energy Management System for Residence

Introduction: The roots of virtual reality (VR) go back to the work of Ivan Sutherland in the 1960s. At the time, virtual reality showed promise in universities and labs, peaking in the early 1990s at a stable level. The latest form of IoT is merging with the Internet of Everything (IOE). Using this technology, users can access any device, device, and machine in the environment through any middleware application or system. In recent years, smart speakers have become a common commercial method for controlling household appliances, solving some of the problems of remote control. For example, speech recognition is difficult in noisy environments and is minimized using noise cancellation. It also takes a long time to determine the current state of the device as the user need to request or issue a command to the smart speaker and need to wait for a response. The evaluation of prototype system found, using a device with gestures and interacting with a virtual 3D device instead of a real device was acceptable in terms of usability. In this article, the Unity and Vuforia are used to create an AR-based IoT virtual switch that can control any device connected to a LAN controller via HTTP requests. Methods: The system is made experimental with the Augmented Reality (AR), the virtual communication system is designed instead of physical mode. It provide the real time based image for active control of Energy management. It consist of virtual switches, it supports the AR based power control system. Results: The proposed system are made evaluated and tested under various load conditions. The virtual image consist of virtual switches to make real time control of power equipment. The proposed system achieve the latency of about 0.06 sec. Conclusion: The advancement in day today activities required with AR based energy management system with proper control measures. The designed system is supportive for such mechanism. The further system is proposed to make more energy saving concept for enhancement in proposed work.

Resolution Improvement in Near-Virtual-Image-Mode Light-Field Display Using Resolution-Priority Technique

A light-field display with a near-virtual-image mode, which employs both a lens array and an aperture array, was previously proposed to provide a wide viewing zone angle and bright three-dimensional (3D) images. However, it is desirable to enhance its resolutions, which are presently equal to those of conventional displays. Thus, we proposed a technique for increasing the resolutions of 3D images generated by the light-field display with the near-virtual-image mode. The gap between the flat-panel display and lens array is reduced to decrease the magnification of the virtual images of the pixels and to enable the observation of multiple virtual pixel images through each lens. Further, we imaged the aperture array using the lens array to eliminate the gaps between the multiple pixels observed through adjacent lenses. We constructed a prototype display based on the proposed technique and verified the increase in the resolution of the prototype display compared to the original near-virtual-image light-field display.