Outlier Removal Research Articles

Prevalence and management of pulmonary nodules: a systematic review and meta-analysis.

Pulmonary nodules are small, focal lesions often identified via computed tomography (CT) scans. Although the majority are benign, a small percentage of them may be malignant or potentially become malignant, underscoring the importance of early detection and effective management. This study systematically reviews the epidemiology, risk factors, and management strategies for pulmonary nodules, comparing findings across Chinese and non-Chinese populations to better inform the actuarial calculations for predicting the demand of medical services for patients with pulmonary nodules. We performed a systematic analysis of the PubMed and China Knowledge Infrastructure (CNKI) databases for studies reporting the detection rate of pulmonary nodules through CT scans. Both cross-sectional studies and the baseline data from longitudinal studies were included. A modified version of the Newcastle-Ottawa Scale was used to assess the risk of bias and random effect models were used to estimate the overall prevalence. We identified 32 studies and included 24 of them in our meta-analysis. Pooled analysis showed that the overall prevalence of pulmonary nodules was 0.27 (95% confidence interval: 0.25-0.29) after outliers removal. Subgroup analysis showed that there was no significant difference for prevalence between Chinese and non-Chinese populations. Males (0.38) were shown to have slightly higher prevalence compared to females (0.36), but not significant (P=0.88). Age and smoking are the most frequently reported risk factors by studies. Overall, 27% of participants were positive for pulmonary nodules. Advancing age and smoking were consistently identified as a key risk factor for the incidence of pulmonary nodules. Although the management strategies are different across studies, recent guidelines recommend personalized management strategies, prioritizing nodule size, characteristics, and individual risk factors to optimize outcomes.

Nonlinear dynamic analysis of shear- and torsion-free rods using isogeometric discretization and outlier removal

AbstractIn this paper, we present a discrete formulation of nonlinear shear- and torsion-free rods introduced by Gebhardt and Romero (Acta Mechanica 232(10):3825–3847, 2021) that uses isogeometric discretization and robust time integration. Omitting the director as an independent variable field, we reduce the number of degrees of freedom and obtain discrete solutions in multiple copies of the Euclidean space $$\left( \mathbb {R}^3\right) $$ R 3 , which is larger than the corresponding multiple copies of the manifold $$\left( \mathbb {R}^3 \varvec{\times } S^2\right) $$ R 3 × S 2 obtained with standard Hermite finite elements. For implicit time integration, we choose the same integration scheme as Gebhardt and Romero in (2021) that is a hybrid form of the midpoint and the trapezoidal rules. In addition, we apply a recently introduced approach for outlier removal by Hiemstra et al. (Comput Methods Appl Mech Eng 387:114115, 2021) that reduces high-frequency content in the response without affecting the accuracy, ensuring robustness of our nonlinear discrete formulation. We illustrate the efficiency of our nonlinear discrete formulation for static and transient rods under different loading conditions, demonstrating good accuracy in space, time and the frequency domain. Our numerical example coincides with a relevant application case, the simulation of mooring lines.

Human stem cell-derived neurons and astrocytes to detect novel auto-reactive IgG response in immune-mediated neurological diseases.

Up to 46% of patients with presumed autoimmune limbic encephalitis are seronegative for all currently known central nervous system (CNS) antigens. We developed a cell-based assay (CBA) to screen for novel neural antibodies in serum and cerebrospinal fluid (CSF) using neurons and astrocytes derived from human-induced pluripotent stem cells (hiPSCs). Human iPSC-derived astrocytes or neurons were incubated with serum/CSF from 99 patients [42 with inflammatory neurological diseases (IND) and 57 with non-IND (NIND)]. The IND group included 11 patients with previously established neural antibodies, six with seronegative neuromyelitis optica spectrum disorder (NMOSD), 12 with suspected autoimmune encephalitis/paraneoplastic syndrome (AIE/PNS), and 13 with other IND (OIND). IgG binding to fixed CNS cells was detected using fluorescently-labeled antibodies and analyzed through automated fluorescence measures. IgG neuronal/astrocyte reactivity was further analyzed by flow cytometry. Peripheral blood mononuclear cells (PBMCs) were used as CNS-irrelevant control target cells. Reactivity profile was defined as positive using a Robust regression and Outlier removal test with a false discovery rate at 10% following each individual readout. Using our CBA, we detected antibodies recognizing hiPSC-derived neural cells in 19/99 subjects. Antibodies bound specifically to astrocytes in nine cases, to neurons in eight cases, and to both cell types in two cases, as confirmed by microscopy single-cell analyses. Highlighting the significance of our comprehensive 96-well CBA assay, neural-specific antibody binding was more frequent in IND (15 of 42) than in NIND patients (4 of 57) (Fisher's exact test, p = 0.0005). Two of four AQP4+ NMO and four of seven definite AIE/PNS with intracellular-reactive antibodies [1 GFAP astrocytopathy, 2 Hu+, 1 Ri+ AIE/PNS)], as identified in diagnostic laboratories, were also positive with our CBA. Most interestingly, we showed antibody-reactivity in two of six seronegative NMOSD, six of 12 probable AIE/PNS, and one of 13 OIND. Flow cytometry using hiPSC-derived CNS cells or PBMC-detected antibody binding in 13 versus zero patients, respectively, establishing the specificity of the detected antibodies for neural tissue. Our unique hiPSC-based CBA allows for the testing of novel neuron-/astrocyte-reactive antibodies in patients with suspected immune-mediated neurological syndromes, and negative testing in established routine laboratories, opening new perspectives in establishing a diagnosis of such complex diseases.

Moderation of thyroid hormones for the relationship between amyloid and tau pathology

BackgroundAltered thyroid hormone levels have been associated with increased risk of Alzheimer's disease (AD) dementia and related cognitive decline. However, the neuropathological substrates underlying the link between thyroid hormones and AD dementia are not yet fully understood. We first investigated the association between serum thyroid hormone levels and in vivo AD pathologies including both beta-amyloid (Aβ) and tau deposition measured by positron emission tomography (PET). Given the well-known relationship between Aβ and tau pathology in AD, we additionally examined the moderating effects of thyroid hormone levels on the association between Aβ and tau deposition.MethodsThis cross-sectional study was conducted as part of the Korean Brain Aging Study for Early Diagnosis and Prediction of Alzheimer's Disease (KBASE) cohort. This study included a total of 291 cognitively normal adults aged 55 to 90. All participants received comprehensive clinical assessments, measurements for serum total triiodothyronine (T3), free triiodothyronine (fT3), free thyroxine (fT4), and thyroid-stimulating hormone (TSH), and brain imaging evaluations including [11C]-Pittsburgh compound B (PiB)- PET and [18F] AV-1451 PET.ResultsNo associations were found between either thyroid hormones or TSH and Aβ and tau deposition on PET. However, fT4 (p = 0.002) and fT3 (p = 0.001) exhibited significant interactions with Aβ on tau deposition: The sensitivity analyses conducted after the removal of an outlier showed that the interaction effect between fT4 and Aβ deposition was not significant, whereas the interaction between fT3 and Aβ deposition remained significant. However, further subgroup analyses demonstrated a more pronounced positive relationship between Aβ and tau in both the higher fT4 and fT3 groups compared to the lower group, irrespective of outlier removal. Meanwhile, neither T3 nor TSH had any interaction with Aβ on tau deposition.ConclusionOur findings suggest that serum thyroid hormones may moderate the relationship between cerebral Aβ and tau pathology. Higher levels of serum thyroid hormones could potentially accelerate the Aβ-dependent tau deposition in the brain. Further replication studies in independent samples are needed to verify the current results.

Highly adaptive multi-modal image matching based on tuning-free filtering and enhanced sketch features

The generalizability and adaptiveness of multi-modal image matching (MMIM) techniques are hampered by the nonlinear radiometric differences that vary in a highly non-uniform manner across different modal combinations. A major challenge is the extensive filter tuning required by many filter-based matching methods to mitigate the impact of radiometric aberrations. Additionally, the low correct matching rate resulting from the non-repeatability and low similarity of local or detailed features presents another significant obstacle. In this paper, we introduce a novel MMIM framework, called Adaptive Matching with enhanced Edge Sketches (AMES), for reducing manual interference in filter parameter configuration and improving the correct matching rate of extracted feature points. Specifically, AMES trains a support vector regression model to adaptively predict the optimized filtering parameters for each test image using its internal statistical factors as input. We also propose a sketch feature enhancement method, based on fusing multi-scale moment maps through principal component analysis, for cross-modal adaptive feature point extraction. We generate matching results by applying feature descriptors within the Log-Polar window and performing outlier removal following a coarse-to-fine strategy. In an evaluation involving four datasets comprising 1055 image pairs with 26 different cross-modal combinations, the assessment of 10,599 manually measured checkpoints demonstrates that AMES surpasses the state-of-the-art in terms of success rate, correct matching rate, point coverage, average matching accuracy, and spatial distribution uniformity. Our source code and multi-modal image datasets are publicly available at https://dpcv.whu.edu.cn/zm1/gksjj.htm.

The biological variation of insulin resistance markers: data from the European Biological Variation Study (EuBIVAS).

An insulin resistant state is characteristic of patients with type 2 diabetes, polycystic ovary syndrome, and metabolic syndrome. Identification of insulin resistance (IR) is most readily achievable using formulae combining plasma insulin and glucose results. In this study, we have used data from the European Biological Variation Study (EuBIVAS) to examine the biological variability (BV) of IR using the Homeostasis Model Assessment for Insulin Resistance (HOMA-IR) and the Quantitative Insulin sensitivity Check Index (QUICKI). Ninety EuBIVAS non-diabetic subjects (52F, 38M) from five countries had fasting HOMA-IR and QUICKI calculated from plasma glucose and insulin samples collected concurrently on 10weekly occasions. The within-subject (CVI) and between-subject (CVG) BV estimates with 95 % CIs were obtained by CV-ANOVA after analysis of trends, variance homogeneity and outlier removal. The CVI of HOMA-IR was 26.7 % (95 % CI 25.5-28.3), driven largely by variability in plasma insulin and the CVI for QUICKI was 4.1 % (95 % CI 3.9-4.3), reflecting this formula's logarithmic transformation of glucose and insulin values. No differences in values or BV components were observed between subgroups of men or women below and above 50 years. The EuBIVAS, by utilising a rigorous experimental protocol, has produced robust BV estimates for two of the most commonly used markers of insulin resistance in non-diabetic subjects. This has shown that HOMA-IR, in particular, is highly variable in the same individual which limits the value of single measurements.

Detecting outliers in local ionospheric model for GNSS precise positioning

Global Navigation Satellite System fast precise positioning can be achieved with accurate ionospheric corrections computed from an adequate number of GNSS stations in a local region. In low-latitude regions, the presence of electron density gradients over short distances can lead to outliers in the map of ionospheric corrections and decrease its accuracy. In this study, we explored outlier detection in ionospheric correction mapping through statistical residuals during a four-month test in 2021. Our findings indicate that the residuals of the local ionospheric model conform to the Laplace distribution. To determine outliers, we use an empirical rule for the Laplace distribution, setting thresholds at μ ± 3b, μ ± 3.5b, and μ ± 5.8b for data retention rates of 95%, 97%, and 99.7%, respectively. Here, μ represents the location parameter, which corresponds to the median of data, and b is the scale parameter, calculated as the medium absolute deviation. We found that while removing outliers can improve model accuracy, it may result in unavailable prediction due to a lack of data in a spare network. For example, applying a μ ± 3.5b threshold for outlier removal led to approximately 2.5% of recording time having no ionospheric corrections map in low-latitude regions, however, the local model has the potential to improve its mean accuracy by up to 50% for both low and mid-latitudes. Therefore, choosing the appropriate percentile threshold depends on the network configuration and the desired accuracy. Removing erroneous satellite data to improve ionospheric accuracy brings positive impacts on precise positioning.

Predicting surface roughness in machining aluminum alloys taking into account material properties

ABSTRACT This study investigates the use of machine learning models to predict surface roughness (Ra) in milling multi-grade aluminum alloys without prior knowledge of optimal cutting parameters. A diverse milling dataset encompassing material properties and cutting parameters from various aluminum alloy grades was compiled from research articles. Four machine learning algorithms, Extreme Gradient Boosting (XGB), Random Forest (RFR), Catalogical Gradient Boosting (CAT), and Gradient Boosting Regression (GBR), were employed to develop the predictive model. The dataset underwent cleaning, imputation, and outlier removal to ensure data quality. Feature engineering incorporated material properties and cutting parameters for model training. Performance metrics such as RMSE, MAPE, and R2 were used to assess the models’ accuracy. The SHapley Additive exPlanations (SHAP) technique was employed to interpret the models and identify influential features. GBR achieved the highest prediction accuracy with an RMSE of 0.2507 µm, MAPE of 23.36%, and R2 of 0.8709. Thermal conductivity, feed rate, and cutting speed were consistently identified as the most influential factors, although their rankings differed slightly. This study successfully developed a GBR model for effective Ra prediction in aluminum alloy milling, supporting advancements in smart manufacturing by enabling accurate surface quality prediction and data-driven process optimization through machine learning.

Autonomous and reliable fingerprint map maintenance for indoor positioning system

In WiFi-based crowdsourced fingerprinting indoor positioning systems (IPS), the signal space of WiFi APs changes over time. To autonomously maintain a highly reliable localization accuracy, the fingerprint database needs regular maintenance and updates. However, this process is labor-intensive and time-consuming due to the appearance of faulty access points (APs) in crowdsourced samples received from target clients, which are mainly used for autonomous maintenance and updates. Additionally, if some APs are relocated, removed, or replaced in the indoor environment after fingerprint map construction, it can severely impact IPS accuracy. In this paper, we present Dynamic Online Fingerprint Learning with Altered APs status-aware and updating (DOFLAAU) for IPS. Our systematic methodology involves several key steps. First, we employ the Modified Group Method of Data Handling (GMDH) neural network regression for autonomous AP status monitoring and alteration detection. Next, we perform autonomous APs subset sample selection using a modified Thomson model, followed by outlier feature detection and removal based on the weighted sample density rate. Thirdly, we propose a dynamic online fingerprint map updating model using the cosine similarity distance metric. Lastly, we adopted an effective signal distance-based weighted k-nearest neighbor algorithm to improve IPS accuracy. The experiment results validate the efficiency and effectiveness of our proposed algorithms in terms of better localization accuracy compared to peer algorithms.

The implementation method of point cloud denoising filter based on KinectV2

Abstract In the process of collecting data from point clouds, some factors, such as precision equipment, operator experience, environmental conditions, as well as characteristic diffraction effects of electromagnetic waves, changes in surface characteristics of test objects, and the impact of integration processes, are involved data and records that may display some point noise in the data point cloud. In addition to noise caused by measurement errors, practical applications can be affected by external interference and obstacles, which causes deviating values in the data point cloud to differ from the measurement entity. A 3D scanner, represented by the depth of the camera, is used to collect data. To eliminate these noises and anomalies, it is necessary to filter the initial points of the cloud. In this article, we suggest using statistical filtering algorithms to perform statistical analysis on each point community, remove points that do not meet predetermined standards, and remove outliers. To verify the effectiveness of the algorithm, it is compared with other filtering algorithms. The experimental results show that the filter algorithm proposed in this thesis has better effects on outlier points and can obtain the general shape properties of the point cloud.

DCOR: Dynamic Channel-Wise Outlier Removal to De-Noise LiDAR Data Corrupted by Snow

DCOR: Dynamic Channel-Wise Outlier Removal to De-Noise LiDAR Data Corrupted by Snow

A surgical instrument motion measurement system for skill evaluation in practical laparoscopic surgery training.

This study developed and validated a surgical instrument motion measurement system for skill evaluation during practical laparoscopic surgery training. Owing to the various advantages of laparoscopic surgery including minimal invasiveness, this technique has been widely used. However, expert surgeons have insufficient time for providing training to beginners due to the shortage of surgeons and limited working hours. Skill transfer efficiency has to be improved for which there is an urgent need to develop objective surgical skill evaluation methods. Therefore, a simple motion capture-based surgical instrument motion measurement system that could be easily installed in an operating room for skill assessment during practical surgical training was developed. The tip positions and orientations of the instruments were calculated based on the marker positions attached to the root of the instrument. Because the patterns of these markers are individual, this system can track multiple instruments simultaneously and detect exchanges. However due to the many obstacles in the operating room, the measurement data included noise and outliers. In this study, the effect of this decrease in measurement accuracy on feature calculation was determined. Accuracy verification experiments were conducted during wet-lab training to demonstrate the capability of this system to measure the motion of surgical instruments with practical accuracy. A surgical training experiment on a cadaver was conducted, and the motions of six surgical instruments were measured in 36 cases of laparoscopic radical nephrectomy. Outlier removal and smoothing methods were also developed and applied to remove the noise and outliers in the obtained data. The questionnaire survey conducted during the experiment confirmed that the measurement system did not interfere with the surgical operation. Thus, the proposed system was capable of making reliable measurements with minimal impact on surgery. The system will facilitate surgical education by enabling the evaluation of skill transfer of surgical skills.

Digital sand patch: Using laser scanning and discrete element simulation for rapider pavement texture depth measurement

The sand patch method is one of the most basic methods for measuring pavement texture depth (TD), which is an important reference index for pavement anti-skid performance evaluation and road maintenance. However, manual sand patch method heavily relies on the experience of the operators. The emergence of laser 3D scanning and simulation has made it possible to conduct digital sand patch. This study introduces a digital sand patch framework based on 3D scanning and discrete element simulation for precise TD measurement. We developed a 3D scanner with an accuracy of 0.04 mm to obtain the pavement texture data, along with algorithms for outlier removal and slope correction in point cloud data. The method of 3D reconstruction of triangular meshes was adopted to transform discrete point clouds into continuous 3D models to build 3D pavement models. In addition, the virtual process of rotation-spreading-measurement of the digital sand patch method was constructed by discrete element simulation with reference to the physical principles of the manual sand patch method. At the same time, an adaptive dynamic feedback control logic was proposed to accurately determine the termination conditions of the sand patching process. To validate our method, we collected 126 samples from over 20 km of pavement surfaces with various gradations. The results demonstrated an average relative error of 2.92 % compared to the manual sand patch method, and a correlation coefficient of 0.9926 with the laser scanning Mean Profile Depth (MPD) algorithm. Stability and accuracy tests, including scanning downsampling and volatility assessments, confirmed the robustness of our method. This innovative technique for rapid and precise TD measurement not only offers immediate practical benefits but also sets the stage for future research on pavement skid resistance.

A Prediction of Heart Disease using IoT based ThingSpeak Basis and Deep Learning Method

Developing an IoT-based system with enhanced deep learning to predict heart disease aims to revolutionize healthcare. The system, by continuously monitoring vital signs, utilizes advanced machine learning for early detection of potential heart issues, emphasizing innovation for timely interventions and potentially life-saving outcomes. Select appropriate sensors for monitoring vital signs such as heart rate, blood pressure, ECG (Electrocardiogram), oxygen saturation, and temperature. Connect these sensors to an IoT platform using wireless communication protocols like Wi-Fi. Predicting heart disease using an IoT-based ThingSpeak framework is a practical and efficient way to monitor and predict heart-related issues in real-time. ThingSpeak is an IoT platform that allows you to collect, store, analyse, and visualize data from IoT sensors. Here's a step-by-step guide on how to develop a heart disease prediction system using ThingSpeak: Deploy the trained deep learning model to make real-time predictions based on the IoT sensor data. Implement strong security regulations. The input dataset is pre-processed approach for resolving error data and missing values. On ThingSpeak, we can apply preprocessing functions to your data. This might include data smoothing, outlier removal, or missing data handling. Pre-processed data values features are then classified into normal and abnormal by Convolution Neural Network-CNN. A 10-fold cross-validation tactic was employed during the model development procedure. In the experimental result comparisons of MCC calculation, the MD2N model attained the 0.86201 and DCNN perfect reached as 0.84111 and then DBN reached as 0.91157 and then AE model rate as 0.88662 and then proposed model reaches as 0.93291 correspondingly.

Deep learning-based automated detection and segmentation of bone and traumatic bone marrow lesions from MRI following an acute ACL tear

IntroductionTraumatic bone marrow lesions (BML) are frequently identified on knee MRI scans in patients following an acute full-thickness, complete ACL tear. BMLs coincide with regions of elevated localized bone loss, and studies suggest these may act as a precursor to the development of post-traumatic osteoarthritis. This study addresses the labour-intensive manual assessment of BMLs by using a 3D U-Net for automated identification and segmentation from MRI scans. MethodsA multi-task learning approach was used to segment both bone and BML from T2 fat‐suppressed (FS) fast spin echo (FSE) MRI sequences for BML assessment. Training and testing utilized datasets from individuals with complete ACL tears, employing a five-fold cross-validation approach and pre-processing involved image intensity normalization and data augmentation. A post-processing algorithm was developed to improve segmentation and remove outliers. Training and testing datasets were acquired from different studies with similar imaging protocol to assess the model's performance robustness across different populations and acquisition conditions. ResultsThe 3D U-Net model exhibited effectiveness in semantic segmentation, while post-processing enhanced segmentation accuracy and precision through morphological operations. The trained model with post-processing achieved a Dice similarity coefficient (DSC) of 0.75 ± 0.08 (mean ± std) and a precision of 0.87 ± 0.07 for BML segmentation on testing data. Additionally, the trained model with post-processing achieved a DSC of 0.93 ± 0.02 and a precision of 0.92 ± 0.02 for bone segmentation on testing data. This demonstrates the approach's high accuracy for capturing true positives and effectively minimizing false positives in the identification and segmentation of bone structures. ConclusionAutomated segmentation methods are a valuable tool for clinicians and researchers, streamlining the assessment of BMLs and allowing for longitudinal assessments. This study presents a model with promising clinical efficacy and provides a quantitative approach for bone-related pathology research and diagnostics.

Predictive modelling of residual stress in turning of hard materials using radial basis function network enhanced with principal component analysis

This study proposes a hybrid machine learning (ML) model that combines a radial basis function network (RBFN) with principal component analysis (PCA) to predict residual stress (RS) in the machining process. Higher temperatures and plastic deformation can generate RS conditions in the machining of hard materials, significantly influencing the quality of machined parts, particularly their surface integrity. It is crucial to evaluate and guarantee machining conditions that ensure reliable surface integrity that yields compressive RS conditions. Incorrect parameter settings can lead to poor surface quality, resulting in tensile RS conditions that impact both the quality and lifetime of manufactured products. The methodology involved experimental machining trials for data acquisition, with conditions selected for hard material machining tests conducted using a computer numeric control (CNC) lathe under dry cutting conditions. The cutting force components were measured, and RS was evaluated using X-ray diffraction. A total of 60 trials were conducted. Data preprocessing, including normalization and removal of outliers, was executed before constructing the ML algorithm, leaving 59 valid tests for model training and testing. Separate datasets for training (70 % of the data) and testing (30 %) were randomly selected from the valid experimental tests. PCA enhanced the RBFN model’s generalization by reducing data dimensionality and providing the hidden units parameter. The eigenvectors obtained through PCA served as an efficient initial reference for RBF centroids, highlighting principal directions of variation. The PCA-RBFN model, using a multiquadric radial function, exhibited robust performance in capturing underlying patterns during training and demonstrated balanced results in the tests. In the PCA-RBFN model training stage, it exhibited a coefficient of determination (R2) of 76.66 %, a mean average error (MAE) of 72.15 MPa, and a root mean square error (RMSE) of 84.94 MPa. In the testing stage, the model showed increased metrics, indicating its ability to predict RS values accurately, with an R2 of 80.54 %, MAE of 51.25 MPa, and RMSE of 70.36 MPa. The evaluation between the training and testing stages highlighted the model’s coherence in performance, demonstrating its ability to generalize well to new data. A second comparative analysis was conducted with a conventional RBFN approach using stochastic method for centroid determination. The hybrid PCA-RBFN method exhibited lower computational effort, requiring only three iterations compared to 221,923 iterations in the conventional approach. Despite the conventional approach yielding better metrics in the training stage, it exhibited signs of overfitting, a common concern in machine learning models. The hybrid PCA-RBFN consistently outperformed it regarding generalization and fit during testing. The proposed methodology holds practical implications for the manufacturing industry, allowing the integration of a pre-trained predictive model into machine controllers or smart sensors. The hybrid PCA-RBFN’s computationally lightweight approach also supports incorporating real-time adaptive algorithms into production systems, enabling continuous real-time adjustments to the model factors and weights, thereby improving automated control.

A Reliable DBH Estimation Method Using Terrestrial LiDAR Points through Polar Coordinate Transformation and Progressive Outlier Removal

A Reliable DBH Estimation Method Using Terrestrial LiDAR Points through Polar Coordinate Transformation and Progressive Outlier Removal

A preliminary investigation into the feasibility of laser ablation U-Pb isotope ratio measurement via all-faraday cup detection with 1011- and 1013-Ω amplifiers on the Neoma multicollector-inductively coupled plasma-mass spectrometer.

Signal detection for uranium-lead (U-Pb) dating of zircon is typically performed via ion counters. Here, we develop a preliminary understanding of the strengths and limitations of faraday-cup-based detection. A suite of zircon reference materials and the NIST-610 glass were sampled using laser ablation followed by U-Pb isotope ratio measurement on a Neoma multicollector-inductively coupled plasma-mass spectrometer. We were able to produce geologically accurate 207Pb/206Pb, 206Pb/238U, and 207Pb/235U ratios for the NIST-610 glass and the zircon standards, with ages ranging from ~2.5Ga to ~337 Ma (TanBrown A, Oracle, 91550, Mud Tank, Temora, and Plešovice). Two of the younger zircon standards examined (94-35, ~55.6Ma, and Fish Canyon, 28.6Ma) yielded accurate 206Pb/238U but not 207Pb/235U or 207Pb/206Pb ratios, whereas the youngest zircon standard (Penglai, ~4.4Ma) failed for all three ratios of interest. The accuracy and precision of the all-faraday method are directly tied to signal intensity, with reliable data capable of being produced even when both isotopes in a ratio have signals below ~0.001 V (equivalent to ~62 500 cps on an ion counter). The all-faraday cup multicollection method provides sufficient sensitivity to obtain geologically meaningful U-Pb data, with possible advantages being that laser pit depth-dependent changes in the observed interelemental fractionation behavior may be easier to correct using a static collector configuration compared to when the ion beam is swept across a single detector while also removing the need for an interdetector-type calibration. Further work is needed to refine the all-faraday cup method (e.g., application of background subtraction and common Pb corrections, outlier removal, and interelement as well as down-hole fractionation corrections), but our initial results demonstrate that the faraday detector method has sufficient sensitivity to warrant further study.

Cardio-respiratory motion compensation for coronary roadmapping in fluoroscopic imaging.

Inferring the shape and position of coronary artery poses challenges when using fluoroscopic image guidance during percutaneous coronary intervention (PCI) procedure. Although angiography enables coronary artery visualization, the use of injected contrast agent raises concerns about radiation exposure and the risk of contrast-induced nephropathy. To address these issues, dynamic coronary roadmapping overlaid on fluoroscopic images can provide coronary visual feedback without contrast injection. This paper proposes a novel cardio-respiratory motion compensation method that utilizes cardiac state synchronization and catheter motion estimation to achieve coronary roadmapping in fluoroscopic images. For more accurate cardiac state synchronization, video frame interpolation is applied to increase the frame rate of the original limited angiographic images, resulting in higher framerate and more adequate roadmaps. The proposed method also incorporates a multi-length cross-correlation based adaptive electrocardiogram (ECG)matching to address irregular cardiac motion situation. Furthermore, a shape-constrained path searching method is proposed to extract catheter structure from both fluoroscopic and angiographic image. Then catheter motion is estimated using a cascaded matching approach with an outlier removal strategy, leading to a final corrected roadmap. Evaluation of the proposed method on clinical x-ray images demonstrates its effectiveness, achieving a 92.8% F1 score for catheter extraction on 589 fluoroscopic and angiographic images. Additionally, the method achieves a 5.6-pixel distance error of the coronary roadmap on 164 intraoperative fluoroscopic images. Overall, the proposed method achieves accurate coronary roadmapping in fluoroscopic images and shows potential to overlay accurate coronary roadmap on fluoroscopic image in assisting PCI.

Effective Denoising Algorithms for Converting Indoor Blueprints Using a 3D Laser Scanner

This paper focuses on converting complex 3D maps created by LiDAR and SLAM technology into simple 2D maps to make them easier to understand. While 3D maps provide a lot of useful details for robots and computer programs, they can be difficult to read for humans who are used to flat maps. We developed a new system to clean up these 3D maps and convert them into intuitive and accurate 2D maps. The system uses three steps designed to correct different kinds of errors found in 3D LiDAR scan data: clustering-based denoising, height-based denoising, and Statistical Outlier Removal. In particular, height-based denoising is the method we propose in this paper, an algorithm that leaves only indoor structures such as walls. The paper proposes an algorithm that considers the entire range of the point cloud, rather than just the points near the ceiling, as is the case with existing methods, to make denoising more effective. This makes the final 2D map easy to understand and useful for building planning or emergency preparedness. Our main goal is to map the interior of buildings faster and more effectively, creating 2D drawings that reflect accurate and current information. We want to make it easier to use LiDAR and SLAM data in our daily work and increase productivity.