Centroid Location Research Articles

Automated Radiomic Analysis of Vestibular Schwannomas and Inner Ears Using Contrast-Enhanced T1-Weighted and T2-Weighted Magnetic Resonance Imaging Sequences and Artificial Intelligence.

To objectively evaluate vestibular schwannomas (VSs) and their spatial relationships with the ipsilateral inner ear (IE) in magnetic resonance imaging (MRI) using deep learning. Cross-sectional study. A total of 490 adults with VS, high-resolution MRI scans, and no previous neurotologic surgery. MRI studies of VS patients were split into training (390 patients) and test (100 patients) sets. A three-dimensional convolutional neural network model was trained to segment VS and IE structures using contrast-enhanced T1-weighted and T2-weighted sequences, respectively. Manual segmentations were used as ground truths. Model performance was evaluated on the test set and on an external set of 100 VS patients from a public data set (Vestibular-Schwannoma-SEG). Dice score, relative volume error, average symmetric surface distance, 95th-percentile Hausdorff distance, and centroid locations. Dice scores for VS and IE volume segmentations were 0.91 and 0.90, respectively. On the public data set, the model segmented VS tumors with a Dice score of 0.89 ± 0.06 (mean ± standard deviation), relative volume error of 9.8 ± 9.6%, average symmetric surface distance of 0.31 ± 0.22 mm, and 95th-percentile Hausdorff distance of 1.26 ± 0.76 mm. Predicted VS segmentations overlapped with ground truth segmentations in all test subjects. Mean errors of predicted VS volume, VS centroid location, and IE centroid location were 0.05 cm 3 , 0.52 mm, and 0.85 mm, respectively. A deep learning system can segment VS and IE structures in high-resolution MRI scans with excellent accuracy. This technology offers promise to improve the clinical workflow for assessing VS radiomics and enhance the management of VS patients.

Building footprint extraction from aerial photogrammetric point cloud data using its geometric features

Unmanned aerial vehicles (UAVs) can quickly acquire high-resolution datasets. Generally, UAVs or drones have high-resolution optical cameras onboard to obtain aerial images. These images are processed to provide various output products, including point cloud, digital surface model (DSM), digital terrain model (DTM), and ortho-mosaiced image. This study uses point cloud data obtained from UAV data processing to extract buildings automatically. It utilizes the geometric features obtainable from the point cloud data in a defined neighbourhood to classify the point cloud data. Normalized DSM (nDSM) is also used as an input to identify above-ground features more accurately. Random Forest (RF) algorithm has been used to classify the point cloud data into different classes available in the dataset. After classification, buildings are separated from the point cloud data, and K-Means clustering is performed to segregate different building clusters. These clusters are rasterized, and morphological operations are applied to refine the building edges. Then the boundaries of the building clusters are identified to provide the vector data. Accuracy assessment of the automatically extracted shapes is done by comparing their area, perimeter, and centroid location to the reference building polygons generated through the total station survey. The methodology is tested over the dataset acquired through UAV. An open-source GUI (graphical user interface) based tool has been developed in Python to extract the vectorized building shapes from photogrammetric point cloud data and compute areas automatically. It will reduce manual interventions significantly and benefit many users, professionals and researchers.

A Dynamic Export Product Sales Forecasting Model Based on Controllable Relevance Big Data for Cross-Border E-Commerce

Abstract To improve the prediction accuracy of export product sales, this paper constructs a dynamic export product sales prediction model based on controlled relevance big data for cross-border e-commerce to improve sales prediction’s scalability and dynamic evolution. Based on the traditional prediction model, a big data controllable clustering algorithm is used to divide the data into several macro-clusters by data dimensions to determine the number of class clusters and the location of centroids. The K-mean algorithm is used to estimate and categorize the indicators affecting the prediction online, to dig out the key factors affecting the prediction of export product sales, and to establish a dynamic prediction model. The analysis results show that the plausibility measure of the proposed model is 21.9, and the error coefficient is 5.1, which are the smaller values in the reference interval. The average prediction error ratio is 2.25%, the average confidence level is 93.05%, and the error efficiency between predicted sales and actual sales is only 0.98%. Thus, the prediction model proposed in this paper improves the prediction effect of export product sales and has high practical value.

Feasibility of 4D HDR brachytherapy source tracking using x-ray tomosynthesis: Monte Carlo investigation.

High dose rate (HDR) brachytherapy rapidly delivers dose to targets with steep dose gradients. This treatment method must adhere to prescribed treatment plans with high spatiotemporal accuracy and precision, as failure to do so may degrade clinical outcomes. One approach to achieving this goal is to develop imaging techniques to track HDR sources in vivo in reference to surrounding anatomy. This work investigates the feasibility of using an isocentric C-arm x-ray imager and tomosynthesis methods to track Ir-192 HDR brachytherapy sources in vivo over time (4D). A tomosynthesis imaging workflow was proposed and its achievable source detectability, localization accuracy, and spatiotemporal resolution were investigated in silico. An anthropomorphic female XCAT phantom was modified to include a vaginal cylinder applicator and Ir-192 HDR source (0.5 × 0.5 × 5.0 mm3 ), and the workflow was carried out using the MC-GPU Monte Carlo image simulation platform. Source detectability was characterized using the reconstructed source signal-difference-to-noise-ratio (SDNR), localization accuracy by the absolute 3D error in its measured centroid location, and spatiotemporal resolution by the full-width-at-half-maximum (FWHM) of line profiles through the source in each spatial dimension considering a maximum C-arm angular velocity of 30° per second. The dependence of these parameters on acquisition angular range (θtot = 0°-90°), number of views, angular increment between views (Δθ = 0°-15°), and volumetric constraints imposed in reconstruction was evaluated. Organ voxel doses were tallied to derive the workflow's attributable effective dose. The HDR source was readily detected and its centroid was accurately localized with the proposed workflow and method (SDNR: 10-40, 3D error: 0-0.144 mm). Tradeoffs were demonstrated for various combinations of image acquisition parameters; namely, increasing the tomosynthesis acquisition angular range improved resolution in the depth-encoded direction, for example from 2.5 mm to 1.2 mm between θtot = 30o and θtot = 90o , at the cost of increasing acquisition time from 1 to 3 s. The best-performing acquisition parameters (θtot = 90o , Δθ = 1°) yielded no centroid localization error, and achieved submillimeter source resolution (0.57 × 1.21 × 5.04 mm3 apparent source dimensions, FWHM). The total effective dose for the workflow was 263 µSv for its required pre-treatment imaging component and 7.59 µSv per mid-treatment acquisition thereafter, which is comparable to common diagnostic radiology exams. A system and method for tracking HDR brachytherapy sources in vivo using C-arm tomosynthesis was proposed and its performance investigated in silico. Tradeoffs in source conspicuity, localization accuracy, spatiotemporal resolution, and dose were determined. The results suggest this approach is feasible for localizing an Ir-192 HDR source in vivo with submillimeter spatial resolution, 1-3 second temporal resolution and minimal additional dose burden.

A force–displacement relation based on the JKR theory for DEM simulations of adhesive particles

Studying the collective behavior of adhesive particles with the discrete element method (DEM) requires well-founded force–displacement relations (force models). While the Johnson–Kendall–Roberts (JKR) theory reliably predicts the dependence of the contact radius a on the force F, it has remained a challenge in this framework to obtain a straightforward force–displacement relation F(δ) with physically meaningful parameters to calculate the force F from the displacement δ. We derive a novel force–displacement relation from the JKR theory as a composition of functions F(δ)=(F∘a∘λ)(δ), with the intermediate functions contact radius a(λ) and effective adhesive contact radius λ(δ). We also analyze contact geometry errors in the Hertz and JKR models, derive the exact contact centroid to accurately calculate contact torques and relative tangential velocities, and propose a smoothed JKR model to avoid discontinuities in force and energy. We find that incorrect torques and relative tangential velocities are obtained when the stiffness quotient is neglected because it affects the exact location of the contact centroid. We also find that contact geometry errors can become non-negligible for nanoparticles due to their large relative contact size. In addition, we show from bouncing ball simulations that the JKR models with a nominal coefficient of restitution derived for the Hertz model result in higher damping and thus reduced actual coefficients of restitution. Our analysis serves as a foundation for contact-mechanics-based force models for DEM simulations of adhesive particles to investigate their collective behavior in the future.

Gradient filtering regularization for 3-D MT inversion based on unstructured tetrahedral discretization

SUMMARY We propose a novel smoothing regularization scheme for 3-D magnetotelluric (MT) inversion based on unstructured tetrahedral discretization. Different from conventional methods that explicitly add smoothing constraints to model parameters, we choose to do the gradient filtering to smooth the model updates in an implicit way. By transforming the model into a constraint domain, the gradient of the objective function for the parameters in the new domain can be taken as a product of transpose of inverse transformation operator and the conventional gradient. Since the transpose of inverse transformation is designed to be an inverse distance interpolation operator for each tetrahedron, the data fitting term in the gradient can be smoothed in a filtering-like process. We compare our new strategy with the conventional explicit smoothing ones by testing on synthetic data for different noise levels, initial models and regularization factors. The numerical results show that suffering from the unequal volume and random centroid location of adjacent tetrahedrons, the inversion results of conventional methods often demonstrate scattered structures in slices. In contrast, our new method recovers the model in a smooth way and the convergence speed is largely improved. Finally, we adopt the USArray data for further testing and find that comparing to conventional inversion methods, our new strategy can provide more reliable underground structures with better data fitting.

Grid Connected PV Systems Fault Detection using K-Means Clustering Algorithm

—Efficiency in photovoltaic (PV) energy production is significantly influenced by various electrical, environmental, and manufacturing-related factors. These variables often lead to a range of PV generator faults, compromising the system's performance and the overall grid's safety. The current fault detection methods can be complex and resource-intensive. In this paper, we propose a novel and efficient grid-connected PV system fault detection mechanism using the k-means clustering algorithm. Our approach categorizes the possible faults based on clustering the output PV and grid powers under healthy and faulty conditions. A comparison between centroid locations of both conditions leads to fault categorization. The findings demonstrate the efficacy of the proposed technique for addressing localized faults in grid-tied PV systems without the need for complicated calculations. The technique is both cost-effective and accurate, with a straightforward application that can be easily adopted by all stakeholders. This method enables users to safeguard their PV system's health and ensure the more comprehensive grid's safety.

On the Validity of the Normal Force Model for Steadily Revolving Wings: An Experimental Investigation

Aerodynamic characteristics of revolving wing models were investigated to assess the validity of the normal force model. Aerodynamic force and torque measurements were conducted for six wing planforms (with aspect ratios of 2 and 3, and area centroid locations at 40%, 50%, and 60% of the wing length) at three different Reynolds numbers (0.5 × 104, 1 × 104, and 1.5 × 104) and three thickness-to-chord ratios (3%, 4%, and 5%). Both early and steady phase measurements were extracted for a range of angles of attack relevant to insect flight. It was shown that the so-called “normal force” model conveniently captures the variation of the lift and drag coefficients along the first quadrant of angles of attack for all cases tested. A least squares best fit model for the obtained experimental measurements was used to estimate the key parameters of the normal force model, namely the lift curve slope, the zero-lift drag coefficient, and the peak drag coefficient. It was shown that the knowledge of only the lift curve slope and the zero-lift drag coefficient is sufficient to fully describe the model, and that clear trends of these two parameters exist. Notably, both parameters decreased with the increase in area centroid location. For instance, for steady measurements and on average, the lift curve slope for a wing with an area centroid location at 40% span was 15.6% higher compared to an area centroid location at 60% span. However, the increase in the zero-lift drag coefficient for wings with a lower area centroid location had a detrimental effect on aerodynamic efficiency assessed via glide ratio. Wings with a lower area centroid location consistently led to a lower glide ratio regardless of the change in aspect ratio, thickness-to-chord ratio, or Reynolds number. Increasing the aspect ratio decreased the zero-lift drag coefficient but generally had a slighter increasing effect on the lift curve slope. Increasing the Reynolds number within the range experimented decreased both the lift curve slope and the zero-lift drag coefficient. Finally, the effect of the thickness-to-chord ratio was mainly pronounced in its effect on the zero-lift drag coefficient.

Sieve Search Centroiding Algorithm for Star Sensors.

The localization of the center of the star image formed on a sensor array directly affects attitude estimation accuracy. This paper proposes an intuitive self-evolving centroiding algorithm, termed the sieve search algorithm (SSA), which employs the structural properties of the point spread function. This method maps the gray-scale distribution of the star image spot into a matrix. This matrix is further segmented into contiguous sub-matrices, referred to as sieves. Sieves comprise a finite number of pixels. These sieves are evaluated and ranked based on their degree of symmetry and magnitude. Every pixel in the image spot carries the accumulated score of the sieves associated with it, and the centroid is its weighted average. The performance evaluation of this algorithm is carried out using star images of varied brightness, spread radius, noise level, and centroid location. In addition, test cases are designed around particular scenarios, like non-uniform point spread function, stuck-pixel noise, and optical double stars. The proposed algorithm is compared with various long-standing and state-of-the-art centroiding algorithms. The numerical simulation results validated the effectiveness of SSA, which is suitable for small satellites with limited computational resources. The proposed algorithm is found to have precision comparable with that of fitting algorithms. As for computational overhead, the algorithm requires only basic math and simple matrix operations, resulting in a visible decrease in execution time. These attributes make SSA a fair compromise between prevailing gray-scale and fitting algorithms concerning precision, robustness, and processing time.

Classifying urban areas from learned acoustic and seismic data

In this work, a deep transfer learning algorithm is used to classify different urban areas from acoustic and seismic data. The deep transfer learning model combines Google’s deep learning model AlexNet. Measurements of acoustic and seismic ambient noise were conducted in an urban environment. The urban acoustic and seismic measurements are heavily influenced by traffic noise but exhibit more variation with respect to urban location due to the influence of subsurface conditions. A K-means clustering analysis is employed on the acoustic and seismic spectrogram to classify the urban areas. The results demonstrate that seismic and acoustic data can have similar cluster centroid locations in the frequency band of 4 Hz–10 Hz. The transfer learning results will be presented and discussed.

Perspicacious Fuzzy Clustering Franchised by Grasshopper Optimization Dexterity for Robust Resource Scheduling with Effective Load Balancing in Cloud Precedent

Utilizing cloud resources become promising in encroachment of internet technology, countenancing everyone to use resources for little or no cost. It will be very important to have task scheduling for sharing resources in cloud environment. To maintain effective resource usage, cloud technology equally divides workload among shareable resources when it receives task requests. Machine learning and metaheuristic algorithms afford dynamic component for equitable task distribution in cloud paradigm. The current state-of-art unsupervised models-based load balancing arbitrarily selects centroid locations and struggles to achieve incorrect task requests. Using an optimization technique that takes inspiration from behavioral science, study aims to build well-balanced clustering model-based task scheduling system. In order to efficiently schedule tasks among virtual servers in cloud environment, this proposed work styles aids of perspicacious fuzzy and Grass Hopper algorithms. The results showed that PFC-GOD upsurges cloud resources usage while lowering make-span, execution time, and high balance load scheduling.

Automatic Segmentation of Water Bodies Using RGB Data: A Physically Based Approach

A novel method is proposed to automatically segment water extent using optical data. The key features of this approach are (i) the development of a simple physically based model that utilises only RGB data for water extent segmentation; (ii) the achievement of high accuracy in the results, particularly in the estimation of water surface area and perimeter; (iii) the avoidance of any data training process; (iv) the requirement of minimal computational resources; and (v) the release of an open-source software package that provides both command-line codes and a user-friendly graphical interface, making it accessible for various applications, research, and educational purposes. The physically based model integrates reflectance of the water surface with spectral and quantum interpretation of light. The algorithm was tested on 27 rivers and compared to manually-based delimitation, with a resulting robust segmentation procedure. Quantified errors were RMSE = 11.91 (m2) for surface area, RMSE = 12.25 (m) for perimeter, and RMSE in x: 52 (px), RMSE in y: 93 (px) for centroid location. Processing time was faster for automatic segmentation than manual delimitation, with a time reduction of 40% (case-by-case analysis) and 65% (using all case studies together in one run). Shadows, light spots, and natural and non-natural elements in the field of view may affect the accuracy of results.

Subduction transition and relation to upper plate faults revealed by the 2019 Mw 6.0 and 6.2 Costa Rica-Panama border earthquakes

We study the May 12, 2019, Mw 6.0 (hereafter Laurel earthquake) and a June 26, 2019, Mw 6.2 (hereafter Manaca earthquake) earthquakes located in a complex strike-slip to subduction transition zone in the Costa Rica-Panama border using a dense local network of broadband and strong-motion instruments, concluding that the Laurel mainshock data (hypocenter, centroid, and focal mechanism), concurrently with its aftershock distribution, indicate an almost vertical north-south oriented, dextral strike-slip fault. This fault ruptured at depths between 10 and 30 km in the lower crust of Panama Microplate and the geometry implies a shallow influence of the Panama fracture zone. Meanwhile, for the Manaca earthquake, the aftershocks are concentrated on a 25 km long NW-SE trend, with a depth between 20 and 45 km. One of the nodal planes of the focal mechanism of the Manaca earthquake, as well as the centroid location coincides with the aftershock locations trend, supporting the hypothesis that a nearby steeply dipping sinistral strike-slip fault originated this event. Earthquake locations and focal mechanism analyses of collected data from global and local earthquake catalogs suggest that the strong coupling caused by the subduction of the Cocos Ridge and Nazca plate is largely accommodated by outer arc crustal block migration with a combination of NW-SE sinistral and N-S dextral strike-slip faults deforming the overriding Panama Microplate. Sinistral motion of crustal blocks, on the Costa Rica side, are consistent with observed motions along with the Azuero-Sona fault system and Coiba Faults on the Panamanian side.

Source characteristics of icequakes caused by surface crevasses on Urumqi Glacier No. 1, Tianshan, China

SUMMARYIcequakes associated with crevassing provide key information on stress fields and water flow pathways inside glaciers, conditions which are sensitive to temperature and precipitation. In this study, we analyse seismic data from 321 days recorded by five broad-band seismometers on Urumqi Glacier No. 1, Tianshan, China, to investigate the source characteristics of icequakes. We first apply a multidimensional autoregressive maximum-likelihood algorithm to detect 12 template events from 62 days of continuous waveform data. The centroid locations of the 12 template events are located using a grid-search approach. We then obtain a catalogue of 41 895 icequakes by compiling all seismic data through a template-matching method. The local magnitudes of all icequakes range between −3.3 and 0.5, with a completeness magnitude of −2.95 and Gutenberg–Richter b-value of 1.08. The particle motion and frequency contents of the 12 template events show that the dominant phases on the vertical component are Rayleigh waves at 5–40 Hz, suggesting that these icequakes were generated by near-surface crevasses. The average daily icequake number for each month and diurnal icequake variations show clear seasonal features. The icequakes may be related to growth of the surface crevasses due to additional melting water and precipitation in summer and to the thermal contraction of the ice body in winter.

Modified far-upwind reconstruction methods for the implementation of NVSF schemes on unstructured grids

Based on the analysis of the existing far-upwind reconstruction methods, three modified far-upwind reconstruction methods, named as NM-1, NM-2 and NM-3, are developed for the implementation of Normalized Variable and Space Formulation (NVSF) schemes on arbitrary unstructured grids. In the modified reconstruction methods, the locations of the cell centroids around the upwind cell are considered for the position reconstruction. And three interpolation techniques, including the nearest point interpolation, the inverse distance weighted interpolation and the gradient-considered inverse distance weighted interpolation are adopted for the value reconstruction. Moreover, a modified boundedness limitation is proposed to constrain the reconstructed value of the far-upwind node within a more reasonable scope. The pure advection tests demonstrate that NM-3 leads to a better overall performance in terms of accuracy and convergence comparing with the previous and the other modified reconstruction methods for seven classic NVSF schemes. Such advantage of NM-3 retains in the applications of multiphase flow (dam-break flow) and incompressible fluid flow (backward step flow and lid-driven cavity flow). Theoretical analysis implies that the good performance of NM-3 mainly attributes to that its position reconstruction better represents the ‘average position’ of the adjacent cell centroids around the upwind cell and its value reconstruction combines the advantages of the nearest point interpolation and the inverse distance weighted interpolation simultaneously.

A Cautionary Tale: examples of the mis-location of small earthquakes beneath the Tibetan plateau by routine approaches

SUMMARYEarthquake moment tensors and centroid locations in the catalogue of the Global CMT (gCMT) project, formerly the Harvard CMT project, have become an essential resource for studying active global tectonics, used by many solid-Earth researchers. The catalogue’s quality, long duration (1976–present), ease of access and global coverage of earthquakes larger than about Mw 5.5 have transformed our ability to study regional patterns of earthquake locations and focal mechanisms. It also allows researchers to easily identify earthquakes with anomalous mechanisms and depths that stand out from the global or regional patterns, some of which require us to look more closely at accepted interpretations of geodynamics, tectonics or rheology. But, as in all catalogues that are, to some extent and necessarily, produced in a semi-routine fashion, the catalogue may contain anomalies that are in fact errors. Thus, before re-assessing geodynamic, tectonic or rheological understanding on the basis of anomalous earthquake locations or mechanisms in the gCMT catalogue, it is first prudent to check those anomalies are real. The purpose of this paper is to illustrate that necessity in the eastern Himalayas and SE Tibet, where two earthquakes that would otherwise require a radical revision of current geodynamic understanding are shown, in fact, to have gCMT depths (and, in one case, also focal mechanism) that are incorrect.

Static Stability and Swim Bladder Volume in the Bluegill Sunfish (Lepomis macrochirus).

Static stability is a property inherent to every organism. More stable bodies benefit from a lower energy cost associated with maintaining a desired orientation, while less stable bodies can be more maneuverable. The static stability of a fish is determined by the relative locations of its center of mass (COM) and center of buoyancy (COB), which may change with changes in swim bladder volume. We hypothesized, however, that fish would benefit from consistent static stability, and predicted that changes in swim bladder volume would not alter the overall pattern of COM and COB locations. We used micro-computed tomography to estimate the locations of the COM and COB in bluegill sunfish (Lepomis macrochirus). Using this technique, we were able to find a small but significant difference between the location of the COM and COB for a given orientation. We found that the swim bladder can change shape within the body cavity, changing relative locations of the COM and COB. At one extreme, the COB is located 0.441±0.007 BL from the snout and 0.190±0.010 BL from the ventral surface of the pelvic girdle, and that the COM is 0.0030±0.0020 BL posterior and 0.0006±0.0005 BL ventral to the COB, a pattern that causes a nose-up pitching torque. At the other extreme, the COM is anterior and dorsal to the COB, a pattern that causes the opposite torque. These changes in location seems to be caused by changes in the shape and centroid location of the swim bladder within the body: The centroid of the swim bladder is located significantly more posteriorly in fish oriented head-down. The air in the bladder "rises" while heavier tissues "sink," driving a change in tissue distribution and changing the location of the COM relative to the COB. Supporting our hypothesis, we found no correlation between swim bladder volume and the distance between the COM and COB. We conclude that bluegill are statically unstable, requiring them to expend energy constantly to maintain their normal orientation, but that the pitch angle of the body could alter the relative locations of COM and COB, changing their static stability.

Using homosoils to enrich sparse soil data infrastructure: An example from Mali

Many areas in the world suffer from relatively sparse soil data availability. This results in inefficient implementation of soil-related studies and inadequate recommendations for improving soil management strategies. Commonly, this problem is tackled by collecting new soil data to update legacy soil surveys. New soil data collection, however, is usually costly. In this paper, we demonstrate how to find homosoils with the objective of obtaining new soil data for a study area. Homosoils are soils that can be geographically distant but share similar soil-forming factors. We cluster the study area into homogenouse areas, and identify a homosoil to each area using distance metrics calculated in the character space spanned by the environmental covariates. In a case study in Mali, we found that large areas in India, Australia and America have similar soil-forming factors to the African Sahelian zone. We collected available soil data for these areas from the WoSIS database. Statistical analysis on the relationship between the homosoils corresponding to different areas of Mali and three soil properties (clay, sand, pH) displayed the unique variability captured by homosoils. The homosoils could explain 8% of the variation found in the soil datasets. There was a strong association between pH and homosoils corresponding to the semi-arid conditions and sedimentary parent material of Mali, whereas homosoils corresponding to other areas of Mali showed moderate association either with clay or sand. The location and spread of the group centroids were significantly different between depth-specific homosoils for the three soil properties. The approach developed in this paper shows the opportunity for identifying areas in the world with similar soils to populate areas with relatively low soil data density. The concept of homosoils is promising and we envision future applications such as transfer of soil models and agronomic experimental results between areas.

Bayesian Seismic Source Inversion With a 3‐D Earth Model of the Japanese Islands

AbstractWe present probabilistic centroid‐moment tensor solutions inferred from the combination of Hamiltonian Monte Carlo sampling and a 3‐D full‐waveform inversion Earth model of the Japanese islands. While the former provides complete posterior probability densities, the latter allows us to exploit waveform data with periods as low as 15 s. For the computation of Green's functions, we employ spectral‐element simulations through the radially anisotropic and visco‐elastic model, leading to substantial improvements of data fit compared to layered models. Focusing on 13 Mw 4.8–Mw 5.3 offshore earthquakes with a significant non‐double‐couple (non‐DC) component, we simultaneously infer the centroid location, time and moment tensor without any a priori constraints on the faulting mechanism. Furthermore, we perform the inversions across several period bands, varying the minimum period between 15 and 50 s. Accounting for 3‐D Earth structure at shorter periods can increase the double‐couple (DC) component of an event, compared to the GCMT solution, by tens of percent. This suggests that non‐DC events in the GCMT catalog may result from unmodeled Earth structure and the related limitation to longer‐period data. We also observe that significant changes in source parameters, and the DC component in particular, may be related to only small waveform changes, thereby accentuating the importance of a reliable Earth model. Posterior probability density distributions become increasingly multimodal for shorter‐period data that provide tighter constraints on source parameters. This implies, in our specific case, that stochastic approaches to the source inversion problem are required for periods below ∼20 s to avoid trapping in local minima.

PHSPP02 Presentation Time: 2:40 PM: Automated Channel Identification for Efficient Brachytherapy Treatment Planning

<h3>Purpose</h3> For interstitial brachytherapy, channel reconstruction during the process of brachytherapy treatment planning can be an arduous and time-consuming task, which becomes more difficult and error-prone when 20 to 30 different catheters with curvatures are involved and overlap in 3D in the patient anatomy. The goal of this project is to develop an artificial intelligence-based approach for automated channel identification using patient CT scans, reducing both the time and potential for human error in brachytherapy treatment planning. <h3>Materials and Methods</h3> Three sets of CT scans were used as well as a baseline model for training, which were acquired with a set of binary-coded "dummy wires" markers <i>in situ</i>. Each of the 12 available dummy wires was designed with a unique pattern of radio-opaque markers and/or spacing strands which was mapped to a channel identification array. An identification algorithm was developed which involved first manually specifying the 3D DICOM coordinates of the centroids of each of the marker strands within the dummy wires. A nearest neighbor search was performed using a k-d tree method for each centroid acting as a potential starting point within a channel. Using the knowledge from the baseline model of each of the 12 potential dummy wire markers, the system predicted the most probable trajectory through the centroids. Then, to assign the proper channel number, the identified pattern of the applicable neighboring points was correlated with the channel identification array defined previously. This process was repeated until the entire configuration of channel identities was determined. Finally, a process of elimination was used to output the correct channel labels and locations. <h3>Results</h3> In this feasibility study, the developed algorithm was able to suitably provide channel identities and return the locations to the user when the proper centroid locations were input into the model. The model ran within seconds and required no training data other than the layout and spacing of the strands within the coded dummy-wire marker system. The final output was able to be graphically represented as labeled reconstructions of the catheters in a 3D space. <h3>Conclusions</h3> The outcomes of this preliminary work suggested that there is a future for automated aid in the channel identification process based on coded dummy-wire markers for more efficient treatment planning in brachytherapy. Using only the centroids of the markers, the channels could be accurately reconstructed. Future work will involve creating a 3D CT scan-based image detection algorithm to automate the process of defining the centroid locations without any <i>a priori</i> knowledge.