Real-time Imaging Research Articles

Gain-based Green Ant Colony Optimization for 3D Path Planning on Remote Sensing Images

Metaheuristic algorithms are powerful methods for handling complexities in 3D environments because of their adaptability property. This paper proposes a gain-based, green-ant colony optimization (GGACO) method for 3D path planning on remote sensing images. Shortest paths do not always imply minimum energy consumption. Moreover, computational complexity tends to increase in the case of higher-dimensional data. A novel method is proposed to alleviate this issue, one that provides an efficient path with minimum energy consumption by adding a gain quantity during its search. The results are validated using performance measures, viz., path length, time, and energy cost. Real-time images, along with their corresponding ground truth and “digital surface models (DSM)”, have been sourced from the “International Society for Photogrammetry and Remote Sensing (ISPRS)”. Comparisons have been made against state-of-the-art algorithms and analyzed. Finally, the convergence and stability of the proposed method have been verified; it has been found that the proposed method outperforms the existing method by 6%, 11% and 5% regarding length, computation time, and energy, respectively.

Automated segmentation of retinal layers in optical coherence tomography images using Xception70 feature extraction

Optical coherence tomography (OCT) imaging plays a critical role in evaluating retinal layer thickness, serving as a pivotal diagnostic tool for numerous retinal conditions. However, challenges such as speckle noise, poor image contrast, and ambiguous retinal detachments, like drusen, often hinder accurate segmentation of retinal layers. To address these challenges and enhance diagnostic precision, we introduce the Retinal Segmentation Network, or "Ret-Seg Net," a deep neural network-based approach. Leveraging the advanced Xception70 feature extractor, Ret-Seg Net extracts and comprehends the intricate properties of retinal layers. By integrating acquired feature maps from Xception70 into the atrous spatial pyramid-pooling module, Ret-Seg Net extracts multiscale feature information. The encoder-decoder module of Ret-Seg Net achieves automated segmentation of retinal layers in OCT images by reconstructing distinct retinal layer borders. This advanced module accurately recognizes and differentiates retinal layer boundaries, providing precise and reliable segmentation. Validation of our approach using real-time images and the Duke dataset, comprising 310 volumes with 40 B-scans each, demonstrates outstanding performance. Mean intersection over union (MIoU) and sensitivity (Se) metrics achieved remarkable values of 94.52 % and 96.25 % respectively. Furthermore, our approach offers a versatile segmentation framework applicable to various tissues and cell types in clinical settings. Automating segmentation of retinal layers enhances precision in disease identification and monitoring while significantly improving labor efficiency by reducing the need for manual segmentation.

Action of high intensity focused ultrasound (HIFU) ablation on the venous wall: Histopathological analysis, insights, and perspectives.

In the medical field, ultrasound is commonly used for diagnostic imaging due to its ability to provide real-time images. However, High-Intensity Focused Ultrasound (HIFU) is also used for other clinical purposes, such as thermal tissue ablation. In the case of varicose veins, the potential role and benefits of HIFU have been known for a long time despite in-vivo results have mostly been preliminary and experimental, particularly regarding its effect on venous wall. The aim of this brief report is to describe how HIFU acts on venous wall in human incompetent great saphenous veins.

ATPase Valosin-Containing Protein (VCP) Is Involved During the Replication and Egress of Sialodacryoadenitis Virus (SDAV) in Neurons.

Sialodacryoadenitis virus (SDAV) has been identified as the etiological agent responsible for the respiratory system and salivary gland infections in rats. The existing literature on SDAV infections is insufficient to address the topic adequately, particularly in relation to the central nervous system. In order to ascertain how SDAV gains access to neuronal cells and subsequently exits, our attention was focused on the small molecule valosin-containing protein (VCP), which is an ATPase. VCP is acknowledged for its function in the ubiquitin-mediated proteasomal degradation of proteins, including those of viral origin. To ascertain the potential influence of VCP on SDAV replication and egress, high-content screening was employed to determine the viral titer and protein content. Western blot analysis was employed to ascertain the relative expression of VCP. Real-time imaging of SDAV-infected cells and confocal imaging for qualitative morphological analysis were conducted. The Eeyarestatin I (EerI) inhibitor was employed to disrupt VCP involvement in the endoplasmic reticulum-associated protein degradation pathway (ERAD) in both pre- and post-incubation systems, with concentrations of 5 μM/mL and 25 μM/mL, respectively. We demonstrated for the first time that SDAV productively replicates in cultured primary neurons. VCP expression is markedly elevated during SDAV infection. The application of 5 μM/mL EerI in the post-treatment system yielded a statistically significant inhibition of the SDAV yield. It is likely that this modulates the efficacy of virion assembly by arresting viral proteins in the submembrane area.

Intraoperative Ultrasound in Glioma Tumors

Introduction: Glioma tumor is the most common primary brain tumor and extent of resection of this tumor plays a significant role in the survival rate of the patients. Intraoperative ultrasound (IOUS) has become a convenient technique in brain tumor surgery due to its non-invasiveness, affordability, and real-time imaging capabilities, making it appealing to neurosurgeons, and assists neurosurgeons in identifying the tumor's location and adjacent structures during surgery. Methods: This study was done prospectively, in Rasul Akram and Shahada-ye-Haftam Tir hospitals, Tehran, Iran, and evaluated IOUS's use in 20 patients with brain glioma between 2019 and 2022. Simple random sampling was used to select patients. The resection was performed using IOUS, and the extent of resection was assessed through imaging. We used SonoSite convex ultrasound probe with bandwidth of 10-3 MHz and scan depth of 3 to 18 cm. Results: The study found that with ultrasound guidance, the average mass resection rate was 89.3%. The use of ultrasound during surgery improved the resection rate compared to previous studies. Motor complications observed in 15% of patients after surgery included paralysis and verbal deficits, while the rate of meningitis was low. The average length of hospitalization for patients was 10 days, and the average intraoperative bleeding was 344 cubic centimeters. Conclusion: Ultrasound can be a valuable tool in resection of brain glioma as it provides real-time imaging and assists in tumor resection, and detect adjacent structures. However, it should be used in conjunction with other monitoring modalities. Further studies are necessary to explore the full potential and limitations of IOUS in neurosurgery. J Bangladesh Coll Phys Surg 2024; 42: 387-392

Impaired myocardial energetics and lower cardiac reserve in response to exercise stress in adults born preterm

Abstract Background/Introduction Preterm birth (<37 weeks’ gestation) affects 10% of live births globally and associates with an increased risk of heart failure, early cardiovascular-related mortality, and altered cardiac morphology. At rest, left ventricular (LV) ejection fraction (EF) is similar between adults born preterm and term, but right ventricular (RV) EF is lower in preterm-born adults. Exercise stress uncovers LVEF impairment in preterm-born adults but RVEF response to exercise stress has not been studied. The underlying cause of the impaired LVEF reserve and lower RVEF at rest in preterm-born adults remains uncertain and whether altered myocardial energetics contribute to this cardiac dysfunction is currently unknown. Purpose To measure resting myocardial energetics and ventricular function at rest and during exercise stress to determine whether reduced cardiac reserve in preterm-born adults may be explained by impaired myocardial energetics. Methods We conducted a cross-sectional, observational, case-control study of preterm (n=64; mean gestational age =31 weeks) and term-born (n=36) adults aged 34±5 years to compare myocardial energetics and ventricular function. Following maximal cardiopulmonary exercise testing (CPET), participants underwent 31-phosphorus magnetic resonance (MR) spectroscopy (31-P MRS) and cardiovascular MR imaging (CMR) on a 3-Tesla Siemens MR scanner. 31-P MRS was used to measure myocardial phosphocreatine to adenosine triphosphate ratio (PCr/ATP) as a measure of myocardial energetics. We obtained CMR-derived LVEF and RVEF at rest and during 40% peak wattage exercise (determined from CPET) using a MR compatible cardio-step module and steady-state free precession real-time cine imaging. Outcomes were analysed using unpaired Student’s t-tests and presented as mean±standard deviation. Results Age, body mass index, and sex did not differ between groups (p>0.05). PCr/ATP was 1.8±0.4 in preterm-born adults and 2.0±0.4 in term-born adults (p=0.01, Figure 1). Preterm-born adults had a similar LVEF versus term-born adults at rest (60.3±7.6 versus 62.1±4.5; p=0.19) though resting RVEF was lower in preterm-born adults (53.4±6.7 versus 55.9±4.5; p=0.05). LVEF increased by 6.5±5.5% in preterm-born adults and 10.9±6.0% in term-born adults from rest to 40% peak exercise (p<0.001), while RVEF increased by 9.2±6.3% in preterm-born adults versus 12.0±6.3% in term-born adults (p=0.06) (Figure 2). PCr/ATP associated with resting RVEF in preterm-born adults (R²=0.27, p=0.002), but was not associated with resting LVEF or the delta change in LVEF and RVEF with exercise stress. Conclusions Resting myocardial energetics is impaired in adults born preterm relative to term-born adults, with both the LV and RV demonstrating reduced functional reserve when challenged with low to moderate exercise stress. Metabolic modulators may help prevent progression to heart failure in preterm-born adults.Figure 1Figure 2

Effects of the restrictive cardiomyopathy-associated cardiac troponin I K178E mutation on cAMP signalling at the myofilament

Abstract Background Catecholaminergic stimulation of cardiac β-adrenergic receptors (β-ARs) and subsequent cAMP signalling control cardiac inotropy and lusitropy. cAMP signalling is known to be compartmentalised to optimise sympathetic control by producing heterogeneous cAMP signals and protein kinase A (PKA) activation at distinct subcellular sites. This signalling may be disrupted in diseases such as restrictive cardiomyopathy (RCM) characterised by diastolic dysfunction, which is associated with certain mutations including the K178E missense mutation in cardiac troponin I (TPNI). The impaired ventricular relaxation associated with this mutation is thought to arise from myofilament calcium hypersensitivity, which can be modulated by PKA-dependent TPNI phosphorylation. However, a detailed understanding of the pathological effects mediated by the K178E-TPNI mutation is lacking. Purpose This study aims to determine whether the K178E-TPNI mutation alters local cAMP/PKA signalling at TPNI, potentially affecting myofilament calcium sensitivity and cardiac myocyte relaxation. Methods Real-time imaging of cAMP was carried out using the Fluorescence Resonance Energy Transfer (FRET)-based sensor CUTie (cAMP Universal Tag for imaging experiments) [1] fused to wild type (WT)- or K178E-TPNI. The sensors were expressed by transfection in neonatal rat ventricular myocytes (NRVMs) and FRET-changes were recorded at the myofilament on application of the β-AR stimulant isoproterenol (ISO). We further assessed the involvement of cAMP hydrolysing phosphodiesterases (PDEs) in the regulation of cAMP levels selectively at TPNI. Results The peak FRET-change and FRET-change after 5mins on ISO application were significantly higher in NRVMs expressing the mutant compared to the WT sensor, as was the rate of cAMP accumulation. A strong trend showed that the rise in FRET following PDE4 inhibition was larger in NRVMs expressing the WT compared to the mutant sensor, suggesting the involvement of this isoform in degrading cAMP locally at TPNI and reduced PDE4 activity in the presence of the K178E mutation. Co-immunoprecipitation analysis in HEK293 cells demonstrated interaction between WT-TPNI and a PDE4D isoform, and a weaker interaction of this isoform with the K178E-TPNI mutant. Conclusions This study shows that the K178E-TPNI mutation attenuates the TPNI/PDE4D interaction and alters the magnitude and kinetics of the local cAMP response at the myofilament. This mechanistic insight may aid the development of therapeutics targeting the TPNI/PDE4D interaction to treat RCM-associated diastolic dysfunction.The TPNI-CUTie SensorFRET Response Trace

Widefield optical coherence tomography by electro-optical modulation

Optical coherence tomography (OCT) is a unique imaging modality capable of axial sectioning with a resolution of only a few microns. Its ability to image with high resolution deep within tissue makes it ideal for material inspection, dentistry, and, in particular, ophthalmology. Widefield retinal imaging has garnered increasing clinical interest for the detection of numerous retinal diseases. However, real-time applications in clinical practice demand the contrast of swept-source OCT at scan speeds that limit their depth range. The curvature of typical samples, such as teeth, corneas, or retinas, thus restricts the field-of-view of fast OCT systems. Novel high-speed swept sources are expected to further improve the scan rate; however, not without exacerbating the already severe trade-off in depth range. Here, we show how, without the need for mechanical repositioning, harmonic images can be rapidly synthesized at any depth. This is achieved by opto-electronic modulation of a single-frequency swept source laser in tandem with tailored numerical dispersion compensation. We demonstrate experimentally how real-time imaging of highly-curved samples is enabled by extending the effective depth-range 8-fold. Even at the scan speed of a 400 kHz swept source, harmonic OCT enables widefield retinal imaging.

Semi-automatic robotic puncture system based on deformable soft tissue point cloud registration.

Traditional surgical puncture robot systems based on computed tomography (CT) and infrared camera guidance have natural disadvantages for puncture of deformable soft tissues such as the liver. Liver movement and deformation caused by breathing are difficult to accurately assess and compensate by current technical solutions. We propose a semi-automatic robotic puncture system based on real-time ultrasound images to solve this problem. Real-time ultrasound images and their spatial position information can be obtained by robot in this system. By recognizing target tissue in these ultrasound images and using reconstruction algorithm, 3D real-time ultrasound tissue point cloud can be constructed. Point cloud of the target tissue in the CT image can be obtained by using developed software. Through the point cloud registration method based on feature points, two point clouds above are registered. The puncture target will be automatically positioned, then robot quickly carries the puncture guide mechanism to the puncture site and guides the puncture. It takes about just tens of seconds from the start of image acquisition to completion of needle insertion. Patient can be controlled by a ventilator to temporarily stop breathing, and patient's breathing state does not need to be the same as taking CT scan. The average operation time of 24 phantom experiments is 64.5 s, and the average error between the needle tip and the target point after puncture is 0.8mm. Two animal puncture surgeries were performed, and the results indicated that the puncture errors of these two experiments are 1.76mm and 1.81mm, respectively. Robot system can effectively carry out and implement liver tissue puncture surgery, and the success rate of phantom experiments and experiments is 100%. It also shows that the puncture robot system has high puncture accuracy, short operation time, and great clinical value.

Telemedicine networks for acute stroke: An analysis of global coverage, gaps, and opportunities.

Despite the proven efficacy of telestroke in improving clinical outcomes by providing access to specialized expertise and allowing rapid expert hyperacute stroke management and decision-making, detailed operational evidence is scarce, especially for less developed or lower income regions. We aimed to map the global telestroke landscape and characterize existing networks. We employed a four-tiered approach to comprehensively identify telestroke networks, primarily involving engagement with national stroke experts, stroke societies, and international stroke authorities. A carefully designed questionnaire was then distributed to the leaders of all identified networks to assess these networks' structures, processes, and outcomes. We identified 254 telestroke networks distributed across 67 countries. High-income countries (HICs) concentrated 175 (69%) of the networks. No evidence of telestroke services was found in 58 (30%) countries. From the identified networks, 88 (34%) completed the survey, being 61 (71%) located in HICs. Network setup was highly heterogeneous, ranging from 17 (22%) networks with more than 20 affiliated hospitals, providing thousands of annual consultations using purpose-built highly specialized technology, to 11 (13%) networks with fewer than 120 consultations annually using generic videoconferencing equipment. Real-time video and image transfer was employed in 64 (75%) networks, while 62 (74%) conducting quality monitoring. Most networks established in the past 3 years were located in low- and middle-income countries (LMICs). This comprehensive global survey of telestroke networks found significant variation in network coverage, setup, and technology use. Most services are in HICs, and a few services are in LMICs, although an emerging trend of new networks in these regions marks a pivotal moment in global telestroke care. The wide variation in quality monitoring practices across networks, with many failing to report key performance metrics, underscores the urgent need for standardized, resource-appropriate, quality assurance measures that can be adapted to diverse settings.

Attendance Marking using Face Detection

The demand for automated attendance management systems has significantly increased in educational institutions and corporate environments where manual methods are prone to inefficiencies and errors. This paper presents a real-time, facial recognition-based attendance system developed using Flask, OpenCV, and machine learning techniques. The system employs OpenCV’s Haar Cascade classifier for face detection and the K-Nearest Neighbours (KNN) algorithm for face recognition. During user registration, the system captures 10 images per individual to ensure robust recognition across various facial expressions. The web interface, built using Flask, facilitates user interaction for managing attendance records stored in CSV format. The system achieves an accuracy of 92% under optimal lighting conditions, though performance decreases to 80% in low-light environments. Recognition is completed in approximately 2 seconds, making the system suitable for real-time applications in classrooms or offices. Despite handling real-time image streaming efficiently, challenges such as reduced accuracy when handling obstructions like masks or glasses remain. Future enhancements will explore the integration of deep learning models, such as Convolutional Neural Networks (CNNs), to improve robustness and scalability. This system demonstrates the potential of facial recognition technology for automating attendance tracking, with significant applications in both educational and corporate sectors

An optimized siamese neural network with deep linear graph attention model for gynaecological abdominal pelvic masses classification.

An adnexal mass, also known as a pelvic mass, is a growth that develops in or near the uterus, ovaries, fallopian tubes, and supporting tissues. For women suspected of having ovarian cancer, timely and accurate detection of a malignant pelvic mass is crucial for effective triage, referral, and follow-up therapy. While various deep learning techniques have been proposed for identifying pelvic masses, current methods are often not accurate enough and can be computationally intensive. To address these issues, this manuscript introduces an optimized Siamese circle-inspired neural network with deep linear graph attention (SCINN-DLGN) model designed for pelvic mass classification. The SCINN-DLGN model is intended to classify pelvic masses into three categories: benign, malignant, and healthy. Initially, real-time MRI pelvic mass images undergo pre-processing using semantic-aware structure-preserving median morpho-filtering to enhance image quality. Following this, the region of interest (ROI) within the pelvic mass images is segmented using an EfficientNet-based U-Net framework, which reduces noise and improves the accuracy of segmentation. The segmented images are then analysed using the SCINN-DLGN model, which extracts geometric features from the ROI. These features are classified into benign, malignant, or healthy categories using a deep clustering algorithm integrated into the linear graph attention model. The proposed system is implemented on a Python platform, and its performance is evaluated using real-time MRI pelvic mass datasets. The SCINN-DLGN model achieves an impressive 99.9% accuracy and 99.8% recall, demonstrating superior efficiency compared to existing methods and highlighting its potential for further advancement in the field.

Dynamic control and manipulation of near-fields using direct feedback

Shaping and controlling electromagnetic fields at the nanoscale is vital for advancing efficient and compact devices used in optical communications, sensing and metrology, as well as for the exploration of fundamental properties of light-matter interaction and optical nonlinearity. Real-time feedback for active control over light can provide a significant advantage in these endeavors, compensating for ever-changing experimental conditions and inherent or accumulated device flaws. Scanning nearfield microscopy, being slow in essence, cannot provide such a real-time feedback that was thus far possible only by scattering-based microscopy. Here, we present active control over nanophotonic near-fields with direct feedback facilitated by real-time near-field imaging. We use far-field wavefront shaping to control nanophotonic patterns in surface waves, demonstrating translation and splitting of near-field focal spots at nanometer-scale precision, active toggling of different near-field angular momenta and correction of patterns damaged by structural defects using feedback enabled by the real-time operation. The ability to simultaneously shape and observe nanophotonic fields can significantly impact various applications such as nanoscale optical manipulation, optical addressing of integrated quantum emitters and near-field adaptive optics.

A mitochondria-targeted nitric oxide probe with large Stokes shift for real-time imaging and evaluation of inflammatory bowel disease in situ

BackgroundInflammatory bowel disease (IBD) is a prevalent inflammatory disorder, and the abnormal expression of nitric oxide (NO) produced by biocatalysis of iNOS enzyme in mitochondria is directly associated with the occurrence and progression of IBD. Activatable fluorescent probes offer promising tools for early diagnosis of IBD, however, inadequate biodistribution and limited targeting properties of these probes in vivo severely impede accurate diagnosis of IBD and real-time evaluation of inflammatory levels in situ. Therefore, it is necessary to designed a highly efficient fluorescent probe towards NO to overcome inadequate biodistribution and achieve accurate diagnosis and evaluation of IBD in situ. ResultsWe designed a highly efficient mitochondria-targeted “turn-on” NIR fluorescent probe Cy-OMe which has excellent targeting properties and imaging ability. The response mechanism is probe Cy-OMe rapidly undergoes N-nitrosation reaction resulting in turn-on NIR fluorescence signal when exposed to NO. Cy-OMe exhibits high sensitivity and specificity in detecting NO content in vitro, owing to its large Stokes shift. Furthermore, the probe Cy-OMe not only efficiently targets mitochondria but also enables precise assessment of fluctuations in endogenous NO concertation across various cell types. Importantly, by virtue of large Stokes shift and excellent mitochondrial targeting ability, Cy-OMe has the capability to specifically evaluate dynamic fluctuations of NO in lipopolysaccharide (LPS)‐stimulated IBD mouse models in situ and Cy-OMe was achieved high-contrast imaging and precision diagnosis of intestinal inflammation diseases. SignificanceCy-OMe can accurately assess fluctuations in NO levels and show high signal fidelity in the diseased intestine region, which has prospects in the non-invasive diagnosis of intestinal inflammation in vivo. At the same time, it is expected to serve as a potential diagnose platform for investigating the physiological processes underlying NO-related inflammatory diseases and promoting understanding of the pathological functions of NO across diverse inflammatory diseases.

A Vision-Guided Robotic System for Safe Dental Implant Surgery.

Background: Recent advancements in dental implantology have significantly improved outcomes, with success rates of 90-95% over a 10-year period. Key improvements include enhanced preplanning processes, such as precise implant positioning, model selection, and optimal insertion depth. However, challenges remain, particularly in achieving correct spatial positioning and alignment of implants for optimal occlusion. These challenges are pronounced in patients with reduced bone substance or complex anthropometric features, where even minor misalignments can result in complications or defects. Methods: This paper introduces a vision-guided robotic system designed to improve spatial positioning accuracy during dental implant surgery. The system incorporates advanced force-feedback control to regulate the pressure applied to bone, minimizing the risk of bone damage. A preoperative CBCT scan, combined with real-time images from a robot-mounted camera, guides implant positioning. A personalized marker holder guide, developed from the initial CBCT scan, is used for patient-robot calibration. The robot-mounted camera provides continuous visual feedback of the oral cavity during surgery, enabling precise registration of the patient with the robotic system. Results: Initial experiments were conducted on a 3D-printed mandible using a personalized marker holder. Following successful patient-robot registration, the robotic system autonomously performed implant drilling. To evaluate the accuracy of the robotic-assisted procedure, further tests were conducted on 40 identical molds, followed by measurements of implant positioning. The results demonstrated improved positioning accuracy compared to the manual procedure. Conclusions: The vision-guided robotic system significantly enhances the spatial accuracy of dental implants compared to traditional manual methods. By integrating advanced force-feedback control and real-time visual guidance, the system addresses key challenges in implant positioning, particularly for patients with complex anatomical structures. These findings suggest that robotic-assisted implant surgery could offer a safer and more precise alternative to manual procedures, reducing the risk of implant misalignment and associated complications.

W1-Net: a highly scalable ptychography convolutional neural network

X-ray ptychography is a coherent diffraction imaging technique that allows for the quantitative retrieval of both the amplitude and phase information of a sample in diffraction-limited resolution. However, traditional reconstruction algorithms require a large number of iterations to obtain phase and amplitude images exactly, and the expensive computation precludes real-time imaging. To solve the inverse problem of ptychography data, PtychoNN uses deep convolutional neural networks for real-time imaging. However, its model is relatively simple, and its accuracy is limited by the size of the training dataset, resulting in lower robustness. To address this problem, a series of W-Net neural network models have been proposed which can robustly reconstruct the object phase information from the raw data. Numerical experiments demonstrate that our neural network exhibits better robustness, superior reconstruction capabilities and shorter training time with high-precision ptychography imaging.

Wireless image transfer by various antennas using transmitter and receiver modules at 5.8 GHz

Abstract This research proposes an inexpensive technique for wireless image transfer for security and surveillance applications. The technique uses a 5.8 GHz transmitter and receiver module, along with external antennas in the real-time image transfer within a radius of 100 m. The transferred images are stored in a laptop using a Python code-based graphical user interface application. Different antennas, dipole, circular split-ring resonators, hexagonal split-ring resonators, and metamaterial antennas are utilized for comparison. The Blind/Referenceless Image Spatial Quality Evaluator method is used to assess the picture quality of transferred images to quantify image transfer performance when no ground truth or reference photos are supplied. According to the presented results, images transferred using metamaterial antennas have higher quality than those transferred with other types of antennas. For security considerations, such a system can communicate and store the images in real time.

Application value of surgical navigation system based on deep learning and mixed reality for guiding puncture in percutaneous nephrolithotomy: a retrospective study.

This study was conducted to investigate the clinical value of a navigation system based on deep learning and mixed reality for the treatment of kidney stones with percutaneous nephrolithotomy (PNL), and to improve its theoretical basis for the treatment of kidney stones. The data of 136 patients with kidney stones from October 2021 to December 2023 were retrospectively analyzed. All patients underwent PNL, and were categorized into a control group (Group 1) and a surgical navigation group (Group 2) according to puncture positioning method. Preoperative computed tomography (CT) was performed in both groups. In group 1, procedures were performed under standard ultrasound guidance. PNL was performed with navigation system fused with ultrasound to guide percutaneous puncture in group 2. The baseline information and procedural characteristics of both groups were compared. PNL was successfully performed in both groups. No significant difference was found in the baseline date between the two groups. In group 2, real-time ultrasound images could be accurately matched with CT images with the aid of navigation system. The success rate of single puncture, puncture time, and decrease in hemoglobin were significantly improved in group 2 compared to group 1. (p < 0.05). The application of navigation system based on deep learning and mixed reality in PNL for kidney stones allows for real-time intraoperative navigation, with acceptable accuracy and safety. Most importantly, this technique is easily mastered, particularly by novice surgeons in the field of PNL.

Online self-evaluation of fMRI-based neurofeedback performance.

This study explores the subjective evaluation of supplementary motor area (SMA) regulation performance in a real-time functional magnetic resonance imaging neurofeedback (fMRI-NF) task. In fMRI-NF, people learn how to self-regulate their brain activity by performing mental actions to achieve a certain target level (TL) of blood-oxygen-level-dependent (BOLD) activation. Here, we studied two types of self-evaluation: performance predictions and perceived confidence in the prediction judgement. Participants completed three sessions of SMA regulation in a 7 T fMRI scanner, performing a mental drawing task. During each trial, they modulated their imagery strategy to achieve one of two different levels of SMA activation and reported a performance prediction and their confidence in the prediction before receiving delayed BOLD-activation feedback. Results show that participants' performance predictions improved with learning throughout the three sessions, and that these improvements were not driven exclusively by their knowledge of previous performance. Confidence reports on the other hand showed no change throughout training and did not correlate with better and worse predictions. In addition to shedding light on mechanisms of internal self-evaluation during neurofeedback training, these results also point to a dissociation between predictions of performance and confidence reports in the presence of feedback. This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.

Inducing representational change in the hippocampus through real-time neurofeedback.

When you perceive or remember something, other related things come to mind, affecting how these competing items are subsequently perceived and remembered. Such behavioural consequences are believed to result from changes in the overlap of neural representations of these items, especially in the hippocampus. According to multiple theories, hippocampal overlap should increase (integration) when there is high coactivation between cortical representations. However, prior studies used indirect proxies for coactivation by manipulating stimulus similarity or task demands. Here, we induce coactivation in visual cortex more directly using closed-loop neurofeedback from real-time functional magnetic resonance imaging (fMRI). While viewing one object, participants were rewarded for activating the representation of another object as strongly as possible. Across multiple real-time fMRI sessions, participants succeeded in using this neurofeedback to increase coactivation. Compared with a baseline of untrained objects, this protocol led to memory integration in behaviour and the brain: the trained objects became harder for participants to discriminate behaviourally in a categorical perception task and harder to discriminate neurally from patterns of fMRI activity in their hippocampus as a result of losing unique features. These findings demonstrate that neurofeedback can be used to alter and combine memories.This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.