3D reconstruction from endoscopy images: A survey.

Three-dimensional reconstruction of images acquired through endoscopes is playing a vital role in an increasing number of medical applications. Endoscopes used in the clinic are commonly classified as monocular endoscopes and binocular endoscopes. We have reviewed the classification of methods for depth estimation according to the type of endoscope. Basically, depth estimation relies on feature matching of images and multi-view geometry theory. However, these traditional techniques have many problems in the endoscopic environment. With the increasing development of deep learning techniques, there is a growing number of works based on learning methods to address challenges such as inconsistent illumination and texture sparsity. We have reviewed over 170 papers published in the 10 years from 2013 to 2023. The commonly used public datasets and performance metrics are summarized. We also give a taxonomy of methods and analyze the advantages and drawbacks of algorithms. Summary tables and result atlas are listed to facilitate the comparison of qualitative and quantitative performance of different methods in each category. In addition, we summarize commonly used scene representation methods in endoscopy and speculate on the prospects of deep estimation research in medical applications. We also compare the robustness performance, processing time, and scene representation of the methods to facilitate doctors and researchers in selecting appropriate methods based on surgical applications.

Real-time soft body dissection simulation with parallelized graph-based shape matching on GPU.

BACKGROUND AND OBJECTIVE: Interactive soft tissue dissection has been a fundamental procedure in virtual surgery systems. Existing cutting algorithms involve complex topology changes of simulation meshes, which can increase simulation overhead and produce visual artifacts. In this paper, we proposed a novel graph-based shape-matching method that allows for real-time, flexible, progressive, and discontinuous cuts on soft tissue. METHODS: We employed shape-matching constraints within the position-based dynamics (PBD) framework, a widely adopted approach for real-time simulation applications. The soft tissue was effectively modeled using overlapping clusters, each governed by shape-matching constraints. The dissection process was bifurcated into two distinct stages. In the first stage, the surgical scalpel presses the surface of the soft tissue. The soft tissue is cut apart when the surface pressure exceeds a threshold, entering the second stage. To address the discrepancy between the visual mesh and the simulation model during cluster separation, we developed an Aggregate Finding Connected Components (AFCC) algorithm, optimized for GPU computation and integrated with a background grid. This approach also avoids ghost forces and fragmentation artifacts. To control the increase in the number of clusters, we also propose a merging strategy that can run in parallel. RESULTS: Our simulation outcomes demonstrated that the AFCC dissection algorithm effectively manages cluster separation and expansion with robustness. There were no ghost forces between the cutting surface and unrealistic fragments. Our simulation capability extended to supporting intricate and discontinuous cutting routes. Our dissection simulation maintained real-time performance even with over 100,000 particles constituting the soft tissue. CONCLUSIONS: Our real-time and robust surgical dissection simulation technique enables the performance of complex cuts in various surgical scenarios, demonstrating its potential in virtual surgery applications.

Molecular engineering of fluorescent dyes for long-term specific visualization of the plasma membrane based on alkyl-chain-regulated cell permeability.

Long-term visualization of changes in plasma membrane dynamics during important physiological processes can provide intuitive and reliable information in a 4D mode. However, molecular tools that can visualize plasma membranes over extended periods are lacking due to the absence of effective design rules that can specifically track plasma membrane fluorescent dye molecules over time. Using plant plasma membranes as a model, we systematically investigated the effects of different alkyl chain lengths of FMR dye molecules on their performance in imaging plasma membranes. Our findings indicate that alkyl chain length can effectively regulate the permeability of dye molecules across plasma membranes. The study confirms that introducing medium-length alkyl chains improves the ability of dye molecules to target and anchor to plasma membranes, allowing for long-term imaging of plasma membranes. This provides useful design rules for creating dye molecules that enable long-term visualization of plasma membranes. Using the amphiphilic amino-styryl-pyridine fluorescent skeleton, we discovered that the inclusion of short alkyl chains facilitated rapid crossing of the plasma membrane by the dye molecules, resulting in staining of the cell nucleus and indicating improved cell permeability. Conversely, the inclusion of long alkyl chains hindered the crossing of the cell wall by the dye molecules, preventing staining of the cell membrane and demonstrating membrane impermeability to plant cells. The FMR dyes with medium-length alkyl chains rapidly crossed the cell wall, uniformly stained the cell membrane, and anchored to it for a long period without being transmembrane. This allowed for visualization and tracking of the morphological dynamics of the cell plasma membrane during water loss in a 4D mode. This suggests that the introduction of medium-length alkyl chains into amphiphilic fluorescent dyes can transform them from membrane-permeable fluorescent dyes to membrane-staining fluorescent dyes suitable for long-term imaging of the plasma membrane. In addition, we have successfully converted a membrane-impermeable fluorescent dye molecule into a membrane-staining fluorescent dye by introducing medium-length alkyl chains into the molecule. This molecular engineering of dye molecules with alkyl chains to regulate cell permeability provides a simple and effective design rule for long-term visualization of the plasma membrane, and a convenient and feasible means of chemical modification for efficient transmembrane transport of small molecule drugs.

The bibliometric analysis of extended reality in surgical training: Global and Chinese perspective.

Objectives: The prospect of extended reality (XR) being integrated with surgical training curriculum has attracted scholars. However, there is a lack of bibliometric analysis to help them better understand this field. Our aim is to analyze relevant literature focusing on development trajectory and research directions since the 21st century to provide valuable insights. Methods: Papers were retrieved from the Web of Science Core Collection. Microsoft Excel, VOSviewer, and CiteSpace were used for bibliometric analysis. Results: Of the 3337 papers published worldwide, China contributed 204, ranking fifth. The world's enthusiasm for this field has been growing since 2000, whereas China has been gradually entering since 2001. Although China had a late start, its growth has accelerated since around 2016 due to the reform of the medical postgraduate education system and the rapid development of Chinese information technology, despite no research explosive period has been yet noted. International institutions, notably the University of Toronto, worked closely with others, while Chinese institutions lacked of international and domestic cooperation. Sixteen stable cooperation clusters of international scholars were formed, while the collaboration between Chinese scholars was not yet stable. XR has been primarily applied in orthopedic surgery, cataract surgery, laparoscopic training and intraoperative use in neurosurgery worldwide. Conclusions: There is strong enthusiasm and cooperation in the international research on the XR-based surgical training. Chinese scholars are making steady progress and have great potential in this area. There has not been noted an explosive research phase yet in the Chinese pace. The research on several surgical specialties has been summarized at the very first time. AR will gradually to be more involved and take important role of the research.

Multimodal Physiological Analysis of Impact of Emotion on Cognitive Control in VR.

Cognitive control is often perplexing to elucidate and can be easily influenced by emotions. Understanding the individual cognitive control level is crucial for enhancing VR interaction and designing adaptive and self-correcting VR/AR applications. Emotions can reallocate processing resources and influence cognitive control performance. However, current research has primarily emphasized the impact of emotional valence on cognitive control tasks, neglecting emotional arousal. In this study, we comprehensively investigate the influence of emotions on cognitive control based on the arousal-valence model. A total of 26 participants are recruited, inducing emotions through VR videos with high ecological validity and then performing related cognitive control tasks. Leveraging physiological data including EEG, HRV, and EDA, we employ classification techniques such as SVM, KNN, and deep learning to categorize cognitive control levels. The experiment results demonstrate that high-arousal emotions significantly enhance users' cognitive control abilities. Utilizing complementary information among multi-modal physiological signal features, we achieve an accuracy of 84.52% in distinguishing between high and low cognitive control. Additionally, time-frequency analysis results confirm the existence of neural patterns related to cognitive control, contributing to a better understanding of the neural mechanisms underlying cognitive control in VR. Our research indicates that physiological signals measured from both the central and autonomic nervous systems can be employed for cognitive control classification, paving the way for novel approaches to improve VR/AR interactions.

Target-activated multicolor fluorescent dyes for 3D imaging of plasma membranes and tracking of apoptosis.

Real-time tracking of dynamic changes in the three-dimensional morphology of the cell plasma membrane is of great importance for a deeper understanding of physiological processes related to the cell plasma membrane. However, there is a lack of imaging dyes that can specifically be used for a long term labelling of plasma membranes, especially for plant cells. Here, we have used molecular engineering strategies to develop a series of target-activated multicolour fluorescent dyes that can be used for long-term and three-dimensional imaging of plant cell plasma membranes. By combining different electron acceptors and donors, four molecular backbones with different emission colours from green to NIR have been obtained. In the designed styrene-based dyes, referred to as the SD dyes, several functional groups were introduced into the backbones to achieve the properties of target-activated fluorescence, rapid and wash-free staining, high plasma membrane targeting ability and long-term imaging function. Using onion epidermal cells as a platform, these dye molecules can provide high-quality imaging of the plasma membrane for up to 6 hours, providing a powerful tool for long-term monitoring of plasma membrane-related biological events. Calcium-mediated apoptosis of plant cells has been tracked for the first time by monitoring the morphological changes of the plasma membrane in real time using SD dyes. These dyes also exhibit excellent 3D imaging performance of the plasma membrane and were further used to track in real time the 3D morphological changes of the plasma membrane during plasmolysis of plant cells, providing a powerful imaging tool for three-dimensional (3D) biology. This work provides a set of multi-colour dye tools for long-term and three-dimensional imaging of plant cell plasma membranes, and also provides molecular design principles for guiding the transmembrane transport of small molecules.

Self-Supervised Lightweight Depth Estimation in Endoscopy Combining CNN and Transformer.

In recent years, an increasing number of medical engineering tasks, such as surgical navigation, pre-operative registration, and surgical robotics, rely on 3D reconstruction techniques. Self-supervised depth estimation has attracted interest in endoscopic scenarios because it does not require ground truth. Most existing methods depend on expanding the size of parameters to improve their performance. There, designing a lightweight self-supervised model that can obtain competitive results is a hot topic. We propose a lightweight network with a tight coupling of convolutional neural network (CNN) and Transformer for depth estimation. Unlike other methods that use CNN and Transformer to extract features separately and then fuse them on the deepest layer, we utilize the modules of CNN and Transformer to extract features at different scales in the encoder. This hierarchical structure leverages the advantages of CNN in texture perception and Transformer in shape extraction. In the same scale of feature extraction, the CNN is used to acquire local features while the Transformer encodes global information. Finally, we add multi-head attention modules to the pose network to improve the accuracy of predicted poses. Experiments demonstrate that our approach obtains comparable results while effectively compressing the model parameters on two datasets.

State of the Art in Immersive Interactive Technologies for Surgery Simulation: A Review and Prospective.

Immersive technologies have thrived on a strong foundation of software and hardware, injecting vitality into medical training. This surge has witnessed numerous endeavors incorporating immersive technologies into surgery simulation for surgical skills training, with a growing number of researchers delving into this domain. Relevant experiences and patterns need to be summarized urgently to enable researchers to establish a comprehensive understanding of this field, thus promoting its continuous growth. This study provides a forward-looking perspective by reviewing the latest development of immersive interactive technologies for surgery simulation. The investigation commences from a technological standpoint, delving into the core aspects of virtual reality (VR), augmented reality (AR) and mixed reality (MR) technologies, namely, haptic rendering and tracking. Subsequently, we summarize recent work based on the categorization of minimally invasive surgery (MIS) and open surgery simulations. Finally, the study showcases the impressive performance and expansive potential of immersive technologies in surgical simulation while also discussing the current limitations. We find that the design of interaction and the choice of immersive technology in virtual surgery development should be closely related to the corresponding interactive operations in the real surgical speciality. This alignment facilitates targeted technological adaptations in the direction of greater applicability and fidelity of simulation.

Evaluation of Augmented Reality Surgical Navigation in Percutaneous Endoscopic Lumbar Discectomy: Clinical Study.

BACKGROUND: The puncture procedure in percutaneous endoscopic lumbar discectomy (PELD) is non-visual, and the learning curve for PELD is steep. METHODS: An augmented reality surgical navigation (ARSN) system was designed and utilized in PELD. The system possesses three core functionalities: augmented reality (AR) radiograph overlay, AR puncture needle real-time tracking, and AR navigation. We conducted a prospective randomized controlled trial to evaluate its feasibility and effectiveness. A total of 20 patients with lumbar disc herniation treated with PELD were analyzed. Of these, 10 patients were treated with the guidance of ARSN (ARSN group). The remaining 10 patients were treated using C-arm fluoroscopy guidance (control group). RESULTS: The AR radiographs and AR puncture needle were successfully superimposed on the intraoperative videos. The anteroposterior and lateral AR tracking distance errors were 1.55 ± 0.17 mm and 1.78 ± 0.21 mm. The ARSN group exhibited a significant reduction in both the number of puncture attempts (2.0 ± 0.4 vs. 6.9 ± 0.5, p = 0.000) and the number of fluoroscopies (10.6 ± 0.9 vs. 18.5 ± 1.6, p = 0.000) compared with the control group. Complications were not observed in either group. CONCLUSIONS: The results indicate that the clinical application of the ARSN system in PELD is effective and feasible.

Implementation of a Virtual Reality Based Digital-Twin Robotic Minimally Invasive Surgery Simulator.

The rapid development of computers and robots has seen robotic minimally invasive surgery (RMIS) gradually enter the public's vision. RMIS can effectively eliminate the hand vibrations of surgeons and further reduce wounds and bleeding. However, suitable RMIS and virtual reality-based digital-twin surgery trainers are still in the early stages of development. Extensive training is required for surgeons to adapt to different operating modes compared to traditional MIS. A virtual-reality-based digital-twin robotic minimally invasive surgery (VRDT-RMIS) simulator was developed in this study, and its effectiveness was introduced. Twenty-five volunteers were divided into two groups for the experiment, the Expert Group and the Novice Group. The use of the VRDT-RMIS simulator for face, content, and structural validation training, including the peg transfer module and the soft tissue cutting module, was evaluated. Through subjective and objective evaluations, the potential roles of vision and haptics in robot surgery training were explored. The simulator can effectively distinguish surgical skill proficiency between experts and novices.

Augmented Reality Surgical Navigation in Minimally Invasive Spine Surgery: A Preclinical Study.

BACKGROUND: In minimally invasive spine surgery (MISS), where the surgeon cannot directly see the patient's internal anatomical structure, the implementation of augmented reality (AR) technology may solve this problem. METHODS: We combined AR, artificial intelligence, and optical tracking to enhance the augmented reality minimally invasive spine surgery (AR-MISS) system. The system has three functions: AR radiograph superimposition, AR real-time puncture needle tracking, and AR intraoperative navigation. The three functions of the system were evaluated through beagle animal experiments. RESULTS: The AR radiographs were successfully superimposed on the real intraoperative videos. The anteroposterior (AP) and lateral errors of superimposed AR radiographs were 0.74 ± 0.21 mm and 1.13 ± 0.40 mm, respectively. The puncture needles could be tracked by the AR-MISS system in real time. The AP and lateral errors of the real-time AR needle tracking were 1.26 ± 0.20 mm and 1.22 ± 0.25 mm, respectively. With the help of AR radiographs and AR puncture needles, the puncture procedure could be guided visually by the system in real-time. The anteroposterior and lateral errors of AR-guided puncture were 2.47 ± 0.86 mm and 2.85 ± 1.17 mm, respectively. CONCLUSIONS: The results indicate that the AR-MISS system is accurate and applicable.

A Cross-Domain Weakly Supervised Diabetic Retinopathy Lesion Identification Method Based on Multiple Instance Learning and Domain Adaptation.

Accurate identification of lesions and their use across different medical institutions are the foundation and key to the clinical application of automatic diabetic retinopathy (DR) detection. Existing detection or segmentation methods can achieve acceptable results in DR lesion identification, but they strongly rely on a large number of fine-grained annotations that are not easily accessible and suffer severe performance degradation in the cross-domain application. In this paper, we propose a cross-domain weakly supervised DR lesion identification method using only easily accessible coarse-grained lesion attribute labels. We first propose the novel lesion-patch multiple instance learning method (LpMIL), which leverages the lesion attribute label for patch-level supervision to complete weakly supervised lesion identification. Then, we design a semantic constraint adaptation method (LpSCA) that improves the lesion identification performance of our model in different domains with semantic constraint loss. Finally, we perform secondary annotation on the open-source dataset EyePACS, to obtain the largest fine-grained annotated dataset EyePACS-pixel, and validate the performance of our model on it. Extensive experimental results on the public dataset FGADR and our EyePACS-pixel demonstrate that compared with the existing detection and segmentation methods, the proposed method can identify lesions accurately and comprehensively, and obtain competitive results using only coarse-grained annotations.

Photoactivation of Solid-State Fluorescence through Controllable Intermolecular [2+2] Photodimerization.

Intermolecular [2+2] photodimerization provides a distinctive approach to construct photoresponsive fluorescent materials in a manner of switching on solid-state fluorescence. Herein, we report efficient photoactivation of bright solid-state fluorescence based on controllable intermolecular [2+2] photodimerization reaction of benzo[b]thiophene 1,1-dioxide (BTO) derivatives, which provides a simple and effective way to construct smart photoresponsive solid-state fluorescent materials. Rational choice of substituents in BTO molecular skeleton enables them to efficiently undergo photodimerization through regulating molecular stacking in crystal, and also leads to photoactivation of solid-state fluorescence due to the generation of brightly fluorescent photodimers. This intermolecular photodimerization reaction also offers an effective method to synthesize photostable AIEgens with purely through-space conjugation.

Establishment of Surgical Difficulty Grading System and Application of MRI-Based Artificial Intelligence to Stratify Difficulty in Laparoscopic Rectal Surgery.

(1) Background: The difficulty of pelvic operation is greatly affected by anatomical constraints. Defining this difficulty and assessing it based on conventional methods has some limitations. Artificial intelligence (AI) has enabled rapid advances in surgery, but its role in assessing the difficulty of laparoscopic rectal surgery is unclear. This study aimed to establish a difficulty grading system to assess the difficulty of laparoscopic rectal surgery, as well as utilize this system to evaluate the reliability of pelvis-induced difficulties described by MRI-based AI. (2) Methods: Patients who underwent laparoscopic rectal surgery from March 2019 to October 2022 were included, and were divided into a non-difficult group and difficult group. This study was divided into two stages. In the first stage, a difficulty grading system was developed and proposed to assess the surgical difficulty caused by the pelvis. In the second stage, AI was used to build a model, and the ability of the model to stratify the difficulty of surgery was evaluated at this stage, based on the results of the first stage; (3) Results: Among the 108 enrolled patients, 53 patients (49.1%) were in the difficult group. Compared to the non-difficult group, there were longer operation times, more blood loss, higher rates of anastomotic leaks, and poorer specimen quality in the difficult group. In the second stage, after training and testing, the average accuracy of the four-fold cross validation models on the test set was 0.830, and the accuracy of the merged AI model was 0.800, the precision was 0.786, the specificity was 0.750, the recall was 0.846, the F1-score was 0.815, the area under the receiver operating curve was 0.78 and the average precision was 0.69; (4) Conclusions: This study successfully proposed a feasible grading system for surgery difficulty and developed a predictive model with reasonable accuracy using AI, which can assist surgeons in determining surgical difficulty and in choosing the optimal surgical approach for rectal cancer patients with a structurally difficult pelvis.

Drusen-aware model for age-related macular degeneration recognition.

INTRODUCTION: The purpose of this study was to build an automated age-related macular degeneration (AMD) colour fundus photography (CFP) recognition method that incorporates confounders (other ocular diseases) and normal age-related changes by using drusen masks for spatial feature supervision. METHODS: A range of clinical sources were used to acquire 7588 CFPs. Contrast limited adaptive histogram equalisation was used for pre-processing. ResNet50 was used as the backbone network, and a spatial attention block was added to integrate prior knowledge of drusen features into the backbone. The evaluation metrics used were sensitivity, specificity and F1 score, which is the harmonic mean of precision and recall (sensitivity) and area under the receiver-operating characteristic (AUC). Fivefold cross-validation was performed, and the results compared with four other methods. RESULTS: Excellent discrimination results were obtained with the algorithm. On the public dataset (n = 6565), the proposed method achieved a mean (SD) sensitivity of 0.54 (0.09), specificity of 0.99 (0.00), F1 score of 0.62 (0.06) and AUC of 0.92 (0.02). On the private dataset (n = 1023), the proposed method achieved a sensitivity of 0.92 (0.02), specificity of 0.98 (0.01), F1 score of 0.92 (0.01) and AUC of 0.98 (0.01). CONCLUSION: The proposed drusen-aware model outperformed baseline and other vessel feature-based methods in F1 and AUC on the AMD/normal CFP classification task and achieved comparable results on datasets that included other diseases that often confound classification. The method also improved results when a five-category grading protocol was used, thereby reflecting discriminative ability of the algorithm within a real-life clinical setting.

Tucker network: Expressive power and comparison.

Deep neural networks have achieved great success in solving many machine learning and computer vision problems. In this paper, we propose a deep neural network called the Tucker network derived from the Tucker format and analyze its expressive power. The results demonstrate that the Tucker network has exponentially higher expressive power than the shallow network. In other words, a shallow network with an exponential width is required to realize the same score function as that computed by the Tucker network. Moreover, we discuss the expressive power between the hierarchical Tucker tensor network (HT network) and the proposed Tucker network. To generalize the Tucker network into a deep version, we combine the hierarchical Tucker format and Tucker format to propose a deep Tucker tensor decomposition. Its corresponding deep Tucker network is presented. Experiments are conducted on three datasets: MNIST, CIFAR-10 and CIFAR-100. The results experimentally validate the theoretical results and show that the Tucker network and deep Tucker network have better performance than the shallow network and HT network.

Multi-user upper limb rehabilitation training system integrating social interaction.

COVID-19 causes persistent symptoms such as weakness and myasthenia in most patients. Due to the cross-infection of COVID-19, the traditional face-to-face rehabilitation services are risky for the elderly. To ensure that the elderly in urgent need of rehabilitation services receive training while minimizing the disturbance of the COVID-19 pandemic on their social activities. We have improved the existing virtual upper limb training system, and added a social factor to the system. Seniors with upper limb rehabilitation needs can use the system to compete or collaborate with others for training. In addition, a set of natural and scientific exclusive gestures have been designed under the direction of following the doctor's advice. The experiment is conducted jointly with the chief physicians of the geriatrics department in the authoritative class-A hospitals of Class III. Our experiment, which lasted for two months, showed that the virtual training system with social factors added had the best rehabilitation effect and enhanced the initiative of patients. The system has value for popularization during the COVID-19 epidemic.

Highlight removal for endoscopic images based on accelerated adaptive non-convex RPCA decomposition.

OBJECTIVE: Highlights always occur in endoscopic images due to their special imaging environment. It not only increases the difficulty of observation and diagnosis from surgeons, but also influences the performance of Mixed/Augmented Reality techniques in surgery navigation. METHODS: In this paper, we propose a novel accelerated adaptive non-convex robust principal component analysis method (AANC-RPCA) to remove highlights in endoscopic images. We first detect absolute highlight pixels of the enhanced endoscopic images with adaptive threshold segmentation. The quasi-convex function is proposed to approximate a new non-convex objective function. With detected highlight pixels and quasi-convex function, it introduces gradient to shrink sparse matrix and obtains a faster speed of convergence. Then we divide the image into multiple blocks and perform the parallel computation to enhance the efficiency. Finally, we design a weighted template that decays outward with dilation and linear filtering to reconstruct the endoscopic images. Our approach is almost independent of hyper-parameters and can achieve adaptive decomposition. RESULTS: It has been verified on multiple types of endoscopic images through experiments and clinical blind tests. The results demonstrate that our method can obtain the best performance for the recovered images with more details in a shorter time (about 3-5 times). CONCLUSION: Coupled with the user study, both the quantitative and qualitative results indicate that our approach has the potential to be highly useful in endoscopy images. Compared with the existing highlight removal approaches, our method obtains the SOTA results and has the potential to be applied in the various medical processing processes.

Neurophysiological and Subjective Analysis of VR Emotion Induction Paradigm.

The ecological validity of emotion-inducing scenarios is essential for emotion research. In contrast to the classical passive induction paradigm, immersive VR fully engages the psychological and physiological components of the subject, which is considered an ecologically valid paradigm for studying emotion. Several studies investigate the emotional responses to different VR tasks or games using subjective scales. However, little research regards VR as an eliciting material, especially when systematically analyzing emotional processes in VR from a neurophysiological perspective. To fill this gap and scientifically evaluate VR's ability to be used as an active method for emotion elicitation, we investigate the dynamic relationship between explicit information (subjective evaluations) and implicit information (objective neurophysiological data). A total of 28 participants are enlisted to watch eight VR videos while their SAM/IPQ scores and EEG data are recorded simultaneously. In ecologically valid scenarios, the subjective results demonstrate that VR has significant advantages for evoking emotion in arousal-valence. This conclusion is backed by our examination of objective neurophysiological evidence that VR videos effectively induce high-arousal emotions. In addition, we obtain features of critical channels and frequency oscillations associated with emotional valence, thereby validating previous research in more lifelike circumstances. In particular, we discover hemispheric asymmetry in the occipital region under high and low emotional arousal, which adds to our understanding of neural features and the dynamics of emotional arousal. As a result, we successfully integrate EEG and VR to demonstrate that VR is more pragmatic for evoking natural feelings and is beneficial for emotional research. Our research has set a precedent for new methodologies of using VR induction paradigms to acquire a more reliable explanation of affective computing.

Augmented Reality Navigation-Guided Core Decompression for Osteonecrosis of Femoral Head.

Osteonecrosis of the femoral head (ONFH) is a common joint disease in young and middle-aged patients, which seriously burdens their lives and work. For early-stage ONFH, core decompression surgery is a classical and effective hip preservation therapy. In traditional procedures of core decompression with Kirschner wire, there are still many problems such as X-ray exposure, repeated puncture verification, and damage to normal bone tissue. The blindness of the puncture process and the inability to provide real-time visualization are crucial reasons for these problems. To optimize this procedure, our team developed an intraoperative navigation system on the basis of augmented reality (AR) technology. This surgical system can intuitively display the anatomy of the surgical areas and render preoperative images and virtual needles to intraoperative video in real-time. With the guide of the navigation system, surgeons can accurately insert Kirschner wires into the targeted lesion area and minimize the collateral damage. We conducted 10 cases of core decompression surgery with this system. The efficiency of positioning and fluoroscopy is greatly improved compared to the traditional procedures, and the accuracy of puncture is also guaranteed.