Image Data Research Articles

Assessment of Reason for Exam Imaging Reporting and Data System (RI-RADS) in inpatient diagnostic imaging referrals

ObjectivesTo test the Reason for Exam Imaging Reporting and Data System (RI-RADS) in assessing the quality of radiology requests in an Italian cohort of inpatients; to evaluate the interobserver reliability of RI-RADS.MethodsA single-center quality care study was designed to retrospectively identify consecutive radiology request forms for computed tomography, magnetic resonance imaging, and conventional radiography examinations. One radiologist scored the requests using the RI-RADS. The association between RI-RADS and clinical request variables (urgent request, on-call requests, indication for imaging, requesting specialty, imaging modality, and body region) was evaluated. We calculated interobserver agreement between four readers in a subset of 450 requests.ResultsWe included 762 imaging requests. RI-RADS grades A (adequate request), B (barely adequate request), C (considerably limited request), D (deficient request), and X were assigned to 8 (1%), 49 (7%), 237 (31%), 404 (53%), and 64 (8%) of cases, respectively. In the multivariate analysis, the indication for imaging, body region, and requesting specialty significantly influenced the RI-RADS. Indications for imaging with a high risk of poor RI-RADS grade were routine preoperative imaging and device check requests. The upper extremity was the body region with the highest risk of poor RI-RADS grade. Requesting specialties with a high risk of poor RI-RADS grade were cardiovascular surgery, intensive care medicine, and orthopedics. The analysis of the interobserver agreement revealed substantial agreement for the RI-RADS grade.ConclusionThe majority of radiology exam requests were inadequate according to RI-RADS, especially those for routine imaging. RI-RADS demonstrated substantial reliability, suggesting that it can be satisfactorily employed in clinical settings.Critical relevant statementThe implementation of RI-RADS can provide a framework for standardizing radiology requests, thereby enabling quality assurance and promoting a culture of quality improvement.Key PointsRI-RADS aims to grade the completeness of radiology requests.Over half of the imaging requests were RI-RADS D grade; RI-RADS demonstrated substantial reliability.Most radiology requests were inadequate and RI-RADS could classify them in clinical practice.Graphical

Resting state functional magnetic resonance imaging in patients with multiple sclerosis before and after high-dose immunosuppressive therapy and autologous hemopoietic stem cell transplantation

BACKGROUND: Multiple sclerosis is a chronic autoimmune disease characterized by multifocal foci of demyelination in the central nervous system, usually affecting people of working age. The disease causes damage to the blood-brain barrier, the development of multifocal inflammation, destruction of the myelin sheath of axons and various degrees of damage. It is clinically manifested by restriction of motor activity, visual acuity, as well as other symptoms leading to loss of performance and disability of the patient. AIM: determination of changes in the functional connectivity of brain neural networks in patients with multiple sclerosis before and after high-dose immunosuppressive therapy and autologous hematopoietic stem cell transplantation by performing functional magnetic resonance imaging at rest. MATERIALS AND METHODS: The data of functional magnetic resonance imaging of patients with multiple sclerosis were analyzed in dynamics before and after the use of high-dose immunosuppressive therapy followed by autologous hematopoietic stem cell transplantation. The study involved 25 patients with a verified diagnosis of multiple sclerosis. Each underwent complex magnetic resonance imaging at two time points (before and after high-dose immunosuppressive therapy followed by autologous hematopoietic stem cell transplantation) with a difference of 12 months, which included structural magnetic resonance imaging - in order to exclude the presence of pathological foci in the brain (in addition to foci of multiple sclerosis) and functional magnetic resonance imaging.-resonance imaging at rest — to assess functional connectivity. Also, according to the method generally accepted in classical neurology, a clinical neurological examination was performed. RESULTS: At the stage of comparing data on the two groups obtained using functional magnetic resonance imaging at rest, changes in functional activity were detected in various parts of the brain, presumably responsible for clinical differences in the studied groups. CONCLUSION: Currently, the links between brain structures and morphological changes that cause cognitive impairment in multiple sclerosis are being studied. To predict the progression of the disease, the development of biomarkers, including those based on functional magnetic resonance imaging, is required. Evaluating changes in the functional connectivity of brain neural networks can help personalize therapeutic and rehabilitation approaches.

Right hemisphere stroke is linked to reduced social connectedness in the UK Biobank cohort

Social connectedness is fundamental to health and life satisfaction. Empathic capacities that support social connections are commonly impaired following damage to the brain’s right hemisphere, but how these acquired socio-emotional deficits correspond to real-world social outcomes remains unclear. Using anatomical brain imaging and behavioral data from a large sample of stroke survivors included in the UK Biobank (n = 209), we link damage to regions of the right hemisphere involved in emotion recognition to lower social relationship satisfaction and higher loneliness. The effect was driven by lesions to the right anterior insula and not explained by stroke extent and motor function; it was further corroborated by an exploratory analysis of social decline in a few participants for whom data were available from before and after a stroke to the right anterior insula (n = 3; comparison n = 13). These correlational findings provide new insight into the role of the right hemisphere in maintaining social connections and bear important implications for treatment and rehabilitation post-stroke.

Integrative approaches in cognitive neuroscience: Computational tools for analyzing EEG and fMRI data

Abstract. This paper explores the integrative approaches in cognitive neuroscience that utilize computational tools to analyze electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) data. These methodologies provide comprehensive insights into the complex mechanisms underlying cognitive functions. By leveraging advanced computational models, researchers can decode brain activity patterns and understand the neural correlates of cognitive processes. This paper discusses technological advancements, such as machine learning algorithms, statistical models, and signal processing techniques, and their applications in studying memory, learning, attention, perception, and decision-making. The challenges of data integration, model interpretability, and computational resources are also examined. Detailed case studies and quantitative analyses demonstrate the effectiveness of these methods in cognitive neuroscience research. The future prospects and potential improvements in this field are highlighted, emphasizing the role of interdisciplinary collaboration and technological innovation.

Magnetic Resonance Elastography Combined With PI-RADS v2.1 for the Identification of Clinically Significant Prostate Cancer.

Multiparametric MRI may cause overdiagnosis of clinically significant prostate cancer (csPCa) with the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1). To investigate the diagnostic performance of stiffness as a standalone and complementary marker to PI-RADS v2.1 for diagnosing csPCa. Prospective. One hundred forty-seven participants with pathologically confirmed prostate lesions (≥1 cm), including 71 with csPCa. T1-weighted fast spin-echo, T2-weighted fast spin-echo, single-shot echo-planar diffusion-weighted imaging, fast 3D gradient-echo T1-weighted dynamic contrast-enhanced imaging, and 3D single-shot spin-echo based echo-planar MR elastography at 3.0 T. The PI-RADS v2.1 score was assessed by three radiologists independently. Lesion shear stiffness (SS) values at 60 Hz and 90 Hz were measured. A modified PI-RADS integrating stiffness with PI-RADS v2.1 was developed. Diagnostic performance for csPCa was compared between stiffness, PI-RADS v2.1 and the modified PI-RADS. Spearman's correlation, Fleiss κ and intraclass correlation, Pearson correlation, one-way analysis of variance, area under the receiver operating characteristic curve (AUC), and the Delong test. Significance level was P < 0.05. In the peripheral zone, csPCa (N = 35) had significantly higher SS than non-csPCa at 60 Hz (3.22 ± 0.66 kPa vs. 2.56 ± 0.56 kPa) and at 90 Hz (5.64 ± 1.30 kPa vs. 4.48 ± 0.84 kPa). PI-RADS v2.1 showed 100% sensitivity, 58% specificity, and 0.79 AUC for detecting csPCa. SS achieved 97% sensitivity, 52% specificity, and 0.80 AUC at 60 Hz, while SS had 63% sensitivity, 87% specificity, and 0.78 AUC at 90 Hz. The modified PI-RADS, combing SS at 60 Hz with PI-RADS v2.1, resulted in a significantly increased AUC (0.86) compared to that of PI-RADS v2.1, with a sensitivity of 97% and specificity of 75%. Stiffness can help identifying csPCa in the peripheral zone. Combining stiffness with the PI-RADS v2.1 improved the diagnostic accuracy and specificity for csPCa. 1 TECHNICAL EFFICACY: Stage 2.

Enhancing the diagnostic capacity of [18F]PSMA-1007 PET/MRI in primary prostate cancer staging with artificial intelligence and semi-quantitative DCE: an exploratory study

BackgroundTo investigate the ability of artificial intelligence (AI)-based and semi-quantitative dynamic contrast enhanced (DCE) multiparametric MRI (mpMRI), performed within [18F]-PSMA-1007 PET/MRI, in differentiating benign from malignant prostate tissues in patients with primary prostate cancer (PC).ResultsA total of seven patients underwent whole-body [18F]-PSMA-1007 PET/MRI examinations including a pelvic mpMRI protocol with T2w, diffusion weighted imaging (DWI) and DCE image series. Conventional analysis included visual reading of PET/MRI images and Prostate Imaging Reporting & Data System (PI-RADS) scoring of the prostate. On the prostate level, we performed manual segmentations for time-intensity curve parameter formation and semi-quantitative analysis based on DCE segmentation data of PC-suspicious lesions. Moreover, we applied a recently introduced deep learning (DL) pipeline previously trained on 1010 independent MRI examinations with systematic biopsy-enhanced histopathological targeted biopsy lesion ground truth in order to perform AI-based lesion detection, prostate segmentation and derivation of a deep learning PI-RADS score. DICE coefficients between manual and automatic DL-acquired segmentations were compared. On patient-based analysis, PET/MRI revealed PC-suspicious lesions in the prostate gland in 6/7 patients (Gleason Score-GS ≥ 7b) that were histologically confirmed. Four of these patients also showed lymph node metastases, while two of them had bone metastases. One patient with GS 6 showed no PC-suspicious lesions. Based on DCE segmentations, a distinction between PC-suspicious and normal appearing tissue was feasible with the parameters fitted maximum contrast ratio (FMCR) and wash-in-slope. DICE coefficients (manual vs. deep learning) were comparable with literature values at a mean of 0.44. Further, the DL pipeline could identify the intraprostatic PC-suspicious lesions in all six patients with clinically significant PC.ConclusionFirstly, semi-quantitative DCE analysis based on manual segmentations of time-intensity curves was able to distinguish benign from malignant tissues. Moreover, DL analysis of the MRI data could detect clinically significant PC in all cases, demonstrating the feasibility of AI-supported approaches in increasing diagnostic certainty of PSMA-radioligand PET/MRI.

Study on the correlation between the level of insulin resistance and changes in the degree of cerebral atherosclerosis in non-diabetic patients.

Cerebral infarction is a health problem of global concern and brings a particular burden to medical treatment. To analyze the correlation between insulin resistance (IR) levels and changes in cerebral atherosclerosis (AS) degree in non-diabetic patients with cerebral infarction. A total of 134 non-diabetic patients with cerebral infarction who visited the Department of Neurology of our hospital from May 2019 to October 2020 were selected and underwent MRA/CTA (Magnetic resonance angiography/Computed tomography angiography) of cerebral arteries to refine the cerebrovascular imaging data, and according to the results of cerebral AS load, the patients were divided into mild AS group and severe AS group, and the insulin resistance index was calculated with HOMA-IR (homeostasis model assessment of insulin resistance) to evaluate the IR level and HOMA-IR was compared between the two groups. Spearman correlation was used to analyze the correlation between the levels of IR in patients and the changes in cerebral AS load. 54 individuals had severe AS and 80 patients had mild AS, according to an MRA/CTA of the cerebral arteries. There was a significant difference (P< 0.05) in HOMA-IR between the difficult and gentle AS groups. A significant link between HOMA-IR and the severity of cerebral AS in patients was found using Spearman correlation analysis (r= 0.850, P< 0.05). The IR phenomenon was prevalent in non-diabetic patients with cerebral infarction, and the level of IR was closely related to the severity of cerebral AS.

Data analytics for research on complex brain disorders

There exist many research tools for complex brain disorders both proprietary and open source. Those tools are mostly focused on specific use cases and don’t offer a holistic view of the research and diagnostic processes. The MES-CoBraD platform offers a holistic tool that participates in all the phases of the process from planning to execution. In it researchers can create workflows of the diagnostic and research procedures. The Data Analytics module provides the functionality described in these workflows, mainly the analytical functions. The tools are integrated with the rest of the platform in a seamless manner. They are capable of analyzing different categories of data including imaging data, electroencephalogram (EEG)/polysomnography (PSG), medical device data, and consumer technology data. In this paper we present a summary of how the MES-CoBraD platform operates, the role of the data analytics in it, the actual functions implemented and integrated in the Data Analytics module and a number of use cases which show how each specific function can be used during the research and clinical procedures.

The impact of artificial intelligence in the diagnosis and management of acoustic neuroma: A systematic review.

Schwann cell sheaths are the source of benign, slowly expanding tumours known as acoustic neuromas (AN). The diagnostic and treatment approaches for AN must be patient-centered, taking into account unique factors and preferences. The purpose of this study is to investigate how machine learning and artificial intelligence (AI) can revolutionise AN management and diagnostic procedures. A thorough systematic review that included peer-reviewed material from public databases was carried out. Publications on AN, AI, and deep learning up until December 2023 were included in the review's purview. Based on our analysis, AI models for volume estimation, segmentation, tumour type differentiation, and separation from healthy tissues have been developed successfully. Developments in computational biology imply that AI can be used effectively in a variety of fields, including quality of life evaluations, monitoring, robotic-assisted surgery, feature extraction, radiomics, image analysis, clinical decision support systems, and treatment planning. For better AN diagnosis and treatment, a variety of imaging modalities require the development of strong, flexible AI models that can handle heterogeneous imaging data. Subsequent investigations ought to concentrate on reproducing findings in order to standardise AI approaches, which could transform their use in medical environments.

Deep learning for 3D vascular segmentation in hierarchical phase contrast tomography: a case study on kidney

Automated blood vessel segmentation is critical for biomedical image analysis, as vessel morphology changes are associated with numerous pathologies. Still, precise segmentation is difficult due to the complexity of vascular structures, anatomical variations across patients, the scarcity of annotated public datasets, and the quality of images. Our goal is to provide a foundation on the topic and identify a robust baseline model for application to vascular segmentation using a new imaging modality, Hierarchical Phase-Contrast Tomography (HiP-CT). We begin with an extensive review of current machine-learning approaches for vascular segmentation across various organs. Our work introduces a meticulously curated training dataset, verified by double annotators, consisting of vascular data from three kidneys imaged using HiP-CT as part of the Human Organ Atlas Project. HiP-CT pioneered at the European Synchrotron Radiation Facility in 2020, revolutionizes 3D organ imaging by offering a resolution of around 20 μm/voxel and enabling highly detailed localised zooms up to 1–2 μm/voxel without physical sectioning. We leverage the nnU-Net framework to evaluate model performance on this high-resolution dataset, using both known and novel samples, and implementing metrics tailored for vascular structures. Our comprehensive review and empirical analysis on HiP-CT data sets a new standard for evaluating machine learning models in high-resolution organ imaging. Our three experiments yielded Dice similarity coefficient (DSC) scores of 0.9523, 0.9410, and 0.8585, respectively. Nevertheless, DSC primarily assesses voxel-to-voxel concordance, overlooking several crucial characteristics of the vessels and should not be the sole metric for deciding the performance of vascular segmentation. Our results show that while segmentations yielded reasonably high scores-such as centerline DSC ranging from 0.82 to 0.88, certain errors persisted. Specifically, large vessels that collapsed due to the lack of hydrostatic pressure (HiP-CT is an ex vivo technique) were segmented poorly. Moreover, decreased connectivity in finer vessels and higher segmentation errors at vessel boundaries were observed. Such errors, particularly in significant vessels, obstruct the understanding of the structures by interrupting vascular tree connectivity. Our study establishes the benchmark across various evaluation metrics, for vascular segmentation of HiP-CT imaging data, an imaging technology that has the potential to substantively shift our understanding of human vascular networks.

18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Large-Vessel Vasculitis During Active and Inactive Disease Stages Is Associated with the Metabolic Profile, but Not the Macrophage-Related Cytokines: A Proof-of-Concept Study.

Giant cell arteritis (GCA) is an autoimmune/autoinflammatory disease affecting large vessels in patients over 50 years old. The disease presents as an acute inflammatory response with two phenotypes, cranial GCA and large-vessel vasculitis (LV)-GCA, involving the thoracic aorta and its branches. 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET-CT) is among the imaging techniques contributing to diagnosing patients with systemic disease. However, its association with soluble inflammatory markers is still elusive. This proof-of-concept study aims to identify novel soluble serum biomarkers in PET/CT-positive patients with LV-GCA and associate them with active (0 months) and inactive disease (6 months following treatment), in sequential samples. The most-diseased-segment target-to-background ratio (TBRMDS) was calculated for 13 LV-GCA patients, while 14 cranial GCA and 14 Polymyalgia Rheumatica patients with negative initial PET/CT scans served as disease controls. Serum macrophage-related cytokines were evaluated by cytometric bead array (CBA). Finally, previously published NMR/metabolomics data acquired from the same blood sampling were analyzed along with PET/CT findings. TBRMDS was significantly increased in active versus inactive disease (3.32 vs. 2.65, p = 0.006). The analysis identified nine serum metabolites as more sensitive to change from the active to inactive state. Among them, choline levels were exclusively altered in the LV-GCA group but not in the disease controls. Cytokine levels were not associated with PET/CT activity. Combining CRP, ESR, and TBRMDS with choline levels, a composite index was generated to distinguish active and inactive LV-GCA (20.4 vs. 11.62, p = 0.001). These preliminary results could pave the way for more extensive studies integrating serum metabolomic parameters with PET/CT imaging data to extract sensitive composite disease indexes useful for everyday clinical practice.

Exploiting the diversity of modeling methods to probe systematic biases in strong lensing analyses

Challenges inherent to high-resolution and high signal-to-noise data as well as model degeneracies can cause systematic biases in analyses of strong lens systems. In the past decade, the number of lens modeling methods has significantly increased, from purely analytical methods, to pixelated and non-parametric ones, or ones based on deep learning. We embraced this diversity by selecting different software packages and use them to blindly model independently simulated Hubble Space Telescope (HST) imaging data. To overcome the difficulties arising from using different codes and conventions, we used the COde-independent Organized LEns STandard (COOLEST) to store, compare, and release all models in a self-consistent and human-readable manner. From an ensemble of six modeling methods, we studied the recovery of the lens potential parameters and properties of the reconstructed source. In particular, we simulated and inferred parameters of an elliptical power-law mass distribution embedded in a shear field for the lens, while each modeling method reconstructs the source differently. We find that, overall, both lens and source properties are recovered reasonably well, but systematic biases arise in all methods. Interestingly, we do not observe that a single method is significantly more accurate than others, and the amount of bias largely depends on the specific lens or source property of interest. By combining posterior distributions from individual methods using equal weights, the maximal systematic biases on lens model parameters inferred from individual models are reduced by a factor of 5.4 on average. We investigated a selection of modeling effects that partly explain the observed biases, such as the cuspy nature of the background source and the accuracy of the point spread function. This work introduces, for the first time, a generic framework to compare and ease the combination of models obtained from different codes and methods, which will be key to retain accuracy in future strong lensing analyses.

Neuroimaging Correlates with Clinical Severity in Wilson Disease: A Multiparametric Quantitative Brain MRI.

Previous studies have reported metal accumulation and microstructure changes in deep gray nuclei (DGN) in Wilson disease (WD). However, there are limited studies that investigate whether there is metal accumulation and microstructure changes in DGN of patients with WD with normal-appearing routine MRI. This study aimed to evaluate multiparametric changes in DGN of WD and whether the findings correlate with clinical severity in patients with WD. The study enrolled 28 patients with WD (19 with neurologic symptoms) and 25 controls. Fractional anisotropy (FA), mean diffusivity (MD), and magnetic susceptibility in globus pallidus, pontine tegmentum, dentate nucleus, red nucleus, head of caudate nucleus, putamen, substantia nigra, and thalamus were extracted. Correlations between imaging data and the Unified Wilson's Disease Rating Scale (UWDRS) neurologic subitems were explored. FA, MD, and susceptibility values were higher in multiple DGN of patients with WD than controls (P < .05). Patients with WD without abnormal signals in DGN on routine MRI also had higher FA, MD, and susceptibility values than controls (P < .017). We found that UWDRS neurologic subscores correlated with FA and susceptibility values of DGN (P < .05). In addition, we also found that FA and susceptibility values in specific structures correlated with specific neurologic symptoms of WD (ie, tremor, parkinsonism, dysarthria, dystonia, and ataxia) (P < .05). Patients with WD have increased FA, MD, and susceptibility values even before the lesion is morphologically apparent on routine MRI. The increased FA and susceptibility values correlate with clinical severity of WD.

Automated breast density assessment for full-field digital mammography and digital breast tomosynthesis.

Mammographic density is a strong risk factor for breast cancer (BC) and is reported clinically as part of Breast Imaging Reporting and Data System (BI-RADS) results issued by radiologists. Automated assessment of density is needed that can be used for both full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT) as both types of exams are acquired in standard clinical practice. We trained a deep learning model to automate the estimation of BI-RADS density from a prospective Washington University (WashU) clinic-based cohort of 9,714 women, entering into the cohort in 2013 with follow-up through, October 31, 2020. The cohort included 27% non-Hispanic Black women. The trained algorithm was assessed in an external validation cohort that included 18,360 women screened at Emory from January 1, 2013 and followed through December 31, 2020 that included 42% non-Hispanic Black women. Our model-estimated BI-RADS density demonstrated substantial agreement with the density as assessed by radiologist. In the external validation, the agreement with radiologists for category B 81% and C 77% for FFDM and B 83% and C 74% for DBT show important distinction for separation of women with dense breast. We obtained a Cohen's κ of 0.72 (95% CI, 0.71, 0.73) in FFDM and 0.71 (95% CI 0.69, 0.73) in DBT. We provided a consistent and fully automated BI-RADS estimation for both FFDM and DBT using a deep learning model. The software can be easily implemented anywhere for clinical use and risk prediction.

Convolutional-free Malware Image Classification using Self-attention Mechanisms

Malware analysis is a critical aspect of cybersecurity, aiming to identify and differentiate malicious software from benign programmes to protect computer systems from security threats. Despite advancements in cybersecurity measures, malware continues to pose significant risks in cyberspace, necessitating accurate and rapid analysis methods. This paper introduces an innovative approach to malware classification using image analysis, involving three key phases: converting operation codes into RGB image data, employing a Generative Adversarial Network (GAN) for synthetic oversampling, and utilising a simplified Vision Transformer (ViT)-based classifier for image analysis. The method enhances feature richness and explainability through visual imagery data and addresses imbalanced classification using GAN-based oversampling techniques. The proposed framework combines the strengths of convolutional autoencoders, hybrid classifiers, and adapted ViT models to achieve a balance between accuracy and computational efficiency. As shown in the experiments, our convolutional-free approach possesses excellent accuracy and precision compared with convolutional models and outperforms CNN models on two datasets, thanks to the multi-head attention mechanism. On the Big2015 dataset, our model outperforms other CNN models with an accuracy of 0.8369 and an AUC of 0.9791. Specifically, our model reaches an accuracy of 0.9697 and an F1 score of 0.9702 on MALIMG, which is extraordinary.

Emerging MRI biomarkers for the diagnosis of multiple sclerosis.

The need to improve diagnostic precision in multiple sclerosis (MS) is widely recognized. In recent years, several novel magnetic resonance imaging (MRI) biomarkers have been proposed to enhance diagnostic specificity and reduce misdiagnosis. Some of these imaging biomarkers are deemed highly specific for MS and are likely ready to enter the MS diagnostic work-up, while others are still in their exploratory phase. In addition, new synthetic MRI contrasts and artificial intelligence-based diagnostic algorithms are being tested to reduce the time burden related to imaging data acquisition and analysis. In this review, we summarize the most recent advancement in the field, focusing on the adoption of these novel MRI biomarkers-whether used alone or in combination-for the differential diagnosis of MS.

Decoding multi-limb movements from two-photon calcium imaging of neuronal activity using deep learning

Objective.Brain-machine interfaces (BMIs) aim to restore sensorimotor function to individuals suffering from neural injury and disease. A critical step in implementing a BMI is to decode movement intention from recorded neural activity patterns in sensorimotor areas. Optical imaging, including two-photon (2p) calcium imaging, is an attractive approach for recording large-scale neural activity with high spatial resolution using a minimally-invasive technique. However, relating slow two-photon calcium imaging data to fast behaviors is challenging due to the relatively low optical imaging sampling rates. Nevertheless, neural activity recorded with 2p calcium imaging has been used to decode information about stereotyped single-limb movements and to control BMIs. Here, we expand upon prior work by applying deep learning to decode multi-limb movements of running mice from 2p calcium imaging data.Approach.We developed a recurrent encoder-decoder network (LSTM-encdec) in which the output is longer than the input.Main results.LSTM-encdec could accurately decode information about all four limbs (contralateral and ipsilateral front and hind limbs) from calcium imaging data recorded in a single cortical hemisphere.Significance.Our approach provides interpretability measures to validate decoding accuracy and expands the utility of BMIs by establishing the groundwork for control of multiple limbs. Our work contributes to the advancement of neural decoding techniques and the development of next-generation optical BMIs.

Unveiling Urban River Visual Features Through Immersive Virtual Reality: Analyzing Youth Perceptions with UAV Panoramic Imagery

The visual evaluation and characteristic analysis of urban rivers are pivotal for advancing our understanding of urban waterscapes and their surrounding environments. Unmanned aerial vehicles (UAVs) offer significant advantages over traditional satellite remote sensing, including flexible aerial surveying, diverse perspectives, and high-resolution imagery. This study centers on the Haihe River, South Canal, and North Canal in Tianjin China, employing UAVs to capture continuous panoramic image data. Through immersive virtual reality (VR) technology, visual evaluations of these panoramic images were obtained from a cohort of young participants. These evaluations encompassed assessments of scenic beauty, color richness, vitality, and historical sense. Subsequently, computer vision techniques were utilized to quantitatively analyze the proportions of various landscape elements (e.g., trees, grass, buildings) within the images. Clustering analysis of visual evaluation results and semantic segmentation outcomes from different study points facilitated the effective identification and grouping of river visual features. The findings reveal significant differences in scenic beauty, color richness, and vitality among the Haihe River, South Canal, and North Canal, whereas the South and North Canals exhibited a limited sense of history. Six landscape elements—water bodies, buildings, trees, etc.—comprised over 90% of the images, forming the primary visual characteristics of the three rivers. Nonetheless, the uneven spatial distribution of these elements resulted in notable variations in the visual features of the rivers. This study demonstrates that the visual feature analysis method based on UAV panoramic images can achieve a quantitative evaluation of multi-scene urban 3D landscapes, thereby providing a robust scientific foundation for the optimization of urban river environments.

Generative 3D Reconstruction of Martian Surfaces using Monocular Images

Abstract. High-resolution digital terrain models (DEM) are crucial for Mars scientific exploration and engineering demands, such as mission route planning, landing site selection, and topography research. However, DEMs generated based on photogrammetry or photometry suffer from limited stereo coverage and complex generative processes. Therefore, we propose a GAN-based monocular 3D reconstruction method, which utilizes high-resolution monocular orbital images to achieve pixel-level Martian terrain reconstruction. We preprocess the image data and then invert the elevation using the trained generative model. Finally, we recover the absolute scale through post-processing. In this work, we use HiRISE orthorectified images and DEMs with a resolution of 2 m and 0.25 m to validate the effectiveness of our method. We evaluated our method in four areas with different landforms and found that the predicted DEM and HiRISE DEM have height consistency with the root mean square error of about 2 m. This indicates that the proposed GAN-based method has certain effectiveness and generalization and great potential in high-resolution monocular Martian DEM reconstruction.

Simultaneous Implantation of Maxillary Fibroskeletal Lesions: A Case Report and Literature Review.

To investigate a clinical case of simultaneous implantation therapy for a patient with fibroskeletal disease of the jaw, and to provide a clinical basis for the treatment of such patients by clinical prosthetists. The clinical data of a patient with fibroskeletal disease of the jaw undergoing implant treatment were collected, and the clinical repair plan for this patient was discussed by reviewing the literature. The patient, a female, 49 years old, right upper posterior tooth missing for 1 year, through the maxillofacial surgery consultation, according to the imaging data, the maxillary fibroskeletal lesions were considered. The tumor was removed, bone regeneration was guided, and microimplants were implanted at the same time. Cone Beam Computed Tomography was performed at 3, 9, and 20 months postoperatively, and according to clinical evaluation, the patient did not show any signs of recurrence. At the same time, a literature search was conducted to summarize the data on simultaneous implantation therapy for fibroskeletal lesions of the jaw. The treatment plan of tumor removal → guided bone regeneration → simultaneous implantation of microimplants can effectively achieve implant repair and reduce the failure rate of implants in patients with jaw fibroskeletal lesions.