Synthetic Model Research Articles

Causal interpretation of the hazard ratio in randomized clinical trials.

Although the hazard ratio has no straightforward causal interpretation, clinical trialists commonly use it as a measure of treatment effect. We review the definition and examples of causal estimands. We discuss the causal interpretation of the hazard ratio from a two-arm randomized clinical trial, and the implications of proportional hazards assumptions in the context of potential outcomes. We illustrate the application of these concepts in a synthetic model and in a model of the time-varying effects of COVID-19 vaccination. We define causal estimands as having either an individual-level or population-level interpretation. Difference-in-expectation estimands are both individual-level and population-level estimands, whereas without strong untestable assumptions the causal rate ratio and hazard ratio have only population-level interpretations. We caution users against making an incorrect individual-level interpretation, emphasizing that in general a hazard ratio does not on average change each individual's hazard by a factor. We discuss a potentially valid interpretation of the constant hazard ratio as a population-level causal effect under the proportional hazards assumption. We conclude that the population-level hazard ratio remains a useful estimand, but one must interpret it with appropriate attention to the underlying causal model. This is especially important for interpreting hazard ratios over time.

The infraacetabular screw versus the antegrade posterior column screw in acetabulum fractures with posterior column involvement: a biomechanical comparison

IntroductionTraditionally, plate osteosynthesis of the anterior column combined with an antegrade posterior column screw is used for fixation of anterior column plus posterior hemitransverse (ACPHT) acetabulum fractures. Replacing the posterior column screw with an infraacetabular screw could improve the straightforwardness of acetabulum surgery, as it can be inserted using less invasive approaches, such as the AIP/Stoppa approach, which is a well-established standard approach. However, the biomechanical stability of a plate osteosynthesis combined with an infraacetabular screw instead of an antegrade posterior column screw is unknown.Material and methodsTwo osteosynthesis constructs were compared in a synthetic hemipelvis model with an ACPHT fracture: Suprapectineal plate + antegrade posterior column screw (APCS group) vs. suprapectineal plate + infraacetabular screw (IAS group). A single-leg stance test protocol with an additional passive muscle force and a cyclic loading of 32,000 cycles with a maximum effective load of 2400 N was applied. Interfragmentary motion and rotation of the three main fracture lines were measured.ResultsAt the posterior hemitransverse fracture line, interfragmentary motion perpendicular to the fracture line (p < 0.001) and shear motion (p < 0.001) and at the high anterior column fracture line, interfragmentary motion longitudinal to the fracture line (p = 0.017) were significantly higher in the IAS group than in the APCS group. On the other hand, interfragmentary motion perpendicular (p = 0.004), longitudinal (p < 0.001) and horizontal to the fracture line (p = 0.004) and shear motion (p < 0.001) were significantly increased at the low anterior column fracture line in the APCS group compared to the IAS group.ConclusionsReplacing the antegrade posterior column screw with an infraacetabular screw is not recommendable as it results in an increased interfragmentary motion, especially at the posterior hemitransverse component of an ACPHT fracture.

BrepMFR: Enhancing machining feature recognition in B-rep models through deep learning and domain adaptation

Feature Recognition (FR) plays a crucial role in modern digital manufacturing, serving as a key technology for integrating Computer-Aided Design (CAD), Computer-Aided Process Planning (CAPP) and Computer-Aided Manufacturing (CAM) systems. The emergence of deep learning methods in recent years offers a new approach to address challenges in recognizing highly intersecting features with complex geometric shapes. However, due to the high cost of labeling real CAD models, neural networks are usually trained on computer-synthesized datasets, resulting in noticeable performance degradation when applied to real-world CAD models. Therefore, we propose a novel deep learning network, BrepMFR, designed for Machining Feature Recognition (MFR) from Boundary Representation (B-rep) models. We transform the original B-rep model into a graph representation as network-friendly input, incorporating local geometric shape and global topological relationships. Leveraging a graph neural network based on Transformer architecture and graph attention mechanism, we extract the feature representation of high-level semantic information to achieve machining feature recognition. Additionally, employing a two-step training strategy under a transfer learning framework, we enhance BrepMFR's generalization ability by adapting synthetic training data to real CAD data. Furthermore, we establish a large-scale synthetic CAD model dataset inclusive of 24 typical machining features, showcasing diversity in geometry that closely mirrors real-world mechanical engineering scenarios. Extensive experiments across various datasets demonstrate that BrepMFR achieves state-of-the-art machining feature recognition accuracy and performs effectively on CAD models of real-world mechanical parts.

3D Multiresolution Velocity Model Fusion with Probability Graphical Models

ABSTRACT The variability in spatial resolution of seismic velocity models obtained via tomographic methodologies is attributed to many factors, including inversion strategies, ray-path coverage, and data integrity. Integration of such models, with distinct resolutions, is crucial during the refinement of community models, thereby enhancing the precision of ground-motion simulations. Toward this goal, we introduce the probability graphical model (PGM), combining velocity models with heterogeneous resolutions and nonuniform data point distributions. The PGM integrates data relations across varying resolution subdomains, enhancing detail within low-resolution (LR) domains by utilizing information and prior knowledge from high-resolution (HR) subdomains through a maximum posterior problem. Assessment of efficacy, utilizing both 2D and 3D velocity models—consisting of synthetic checkerboard models and a fault-zone model from Ridgecrest, California—demonstrates noteworthy improvements in accuracy, compared to state-of-the-art fusion techniques. Specifically, we find reductions of 30% and 44% in computed travel-time residuals for 2D and 3D models, respectively, as compared to conventional smoothing techniques. Unlike conventional methods, the PGM’s adaptive weight selection facilitates preserving and learning details from complex, nonuniform HR models and applies the enhancements to the LR background domain.

The Imperial College Storm Model (IRIS) Dataset

Assessing tropical cyclone risk on a global scale given the infrequency of landfalling tropical cyclones (TC) and the short period of reliable observations remains a challenge. Synthetic tropical cyclone datasets can help overcome these problems. Here we present a new global dataset created by IRIS, the ImpeRIal college Storm model. IRIS is novel because, unlike other synthetic TC models, it only simulates the decay from the point of lifetime maximum intensity. This minimises the bias in the dataset. It takes input from 42 years of observed tropical cyclones and creates a 10,000 year synthetic dataset of wind speed which is then validated against the observations. IRIS captures important statistical characteristics of the observed data. The return periods of the landfall maximum wind speed are realistic globally.

Fluoroscopically calibrated 3D-printed patient-specific instruments improve the accuracy of osteotomy during bone tumor resection adjacent to joints

BackgroundInadequate surface matching, variation in the guide design, and soft tissue on the skeletal surface may make it difficult to accurately place the 3D-printed patient-specific instrument (PSI) exactly to the designated site, leading to decreased accuracy, or even errors. Consequently, we developed a novel 3D-printed PSI with fluoroscopy-guided positioning markers to enhance the accuracy of osteotomies in joint-preserving surgery. The current study was to compare whether the fluoroscopically calibrated PSI (FCPSI) can achieve better accuracy compared with freehand resection and conventional PSI (CPSI) resection.MethodsSimulated joint-preserving surgery was conducted using nine synthetic left knee bone models. Osteotomies adjacent to the knee joint were designed to evaluate the accuracy at the epiphysis side. The experiment was divided into three groups: free-hand, conventional PSI (CPSI), and fluoroscopically Calibrated PSI (FCPSI). Post-resection CT scans were quantitatively analyzed. Analysis of variance (ANOVA) was used.ResultFCPSI improved the resection accuracy significantly. The mean location accuracy is 2.66 mm for FCPSI compared to 6.36 mm (P < 0.001) for freehand resection and 4.58 mm (P = 0.012) for CPSI. The mean average distance is 1.27 mm compared to 2.99 mm (p < 0.001) and 2.11 mm (p = 0.049). The mean absolute angle is 2.16° compared to 8.50° (p < 0.001) and 5.54° (p = 0.021). The mean depth angle is 1.41° compared to 8.10° (p < 0.001) and 5.32° (p = 0.012). However, there were no significant differences in the front angle compared to the freehand resection group (P = 0.055) and CPSI (P = 0.599) group. The location accuracy observed with FCPSI was maintained at 4 mm, while CPSI and freehand resection exhibited a maximum deviation of 8 mm.ConclusionThe fluoroscopically calibrated 3D-printed patient-specific instruments improve the accuracy of osteotomy during bone tumor resection adjacent to joint joints compared to conventional PSI and freehand resection. In conclusion, this novel 3D-printed PSI offers significant accuracy improvement in joint preserving surgery with a minimal increase in time and design costs.

Shaking table test for subway station considering the influence of diaphragm walls

AbstractUnderground diaphragm walls are commonly used as a support system for the construction of subway stations, working together with inner side walls of subway stations to withstand the pressure from surrounding soils. However, the effect of diaphragm walls on the seismic response of subway stations is still not well understood yet, or at least not well considered during design. In this paper, a series of 1 g shaking table tests is designed to investigate the seismic response of a typical two‐story and three‐span subway station considering the influence of underground diaphragm walls. The stratum is simulated by synthetic model soil (a mixture of sand and sawdust), and the model structure and diaphragm walls are made by granular concrete with galvanized steel wires. A test case of the structure without diaphragm walls is also involved and taken as a benchmark comparison to understand the impact of diaphragm walls on the seismic response of subway station. The seismic excitations for the test include actual seismic records with the amplitude of 0.2, 0.4, and 0.8 g, respectively. Based on the test data analysis, a comprehensive discussion is conducted on the influence of diaphragm walls on the seismic design of underground structures. Current misconceptions that ignoring the role of diaphragm walls is a conservative way in seismic design of underground structures are also reviewed. Results show that the presence of underground diaphragm walls would enhance the lateral stiffness of the structure, and thus significantly reduce the lateral deformation of subway stations during earthquakes. Notably, the structure with diaphragm walls also exhibits a significant amplification in acceleration response and experiences greater dynamic earth pressures on the sidewalls, and furthermore the strains at the connection between the sidewalls and diaphragm walls are dramatically amplified during the earthquake. It is worth noting that these adverse effects of the diaphragm walls on the amplification of dynamic earth pressures on the structure as well as the increase of internal forces at the sidewalls end‐diaphragm walls connection should be carefully considered in the seismic design of underground structures.

Deep automatic soil roughness estimation from digital images

ABSTRACT Soil roughness, defined as the irregularities of the soil surface, yields significant information about soil water storage, infiltration and overland flow and, thus, is a key factor in characterizing the quality of the terrain; it is often used as input in many synthetic general agricultural models and in particular in soil moisture estimation models. In this paper, we propose a framework that combines a specific setup for data acquisition with deep convolutional networks for actual estimation. The former relies on projecting a line red laser beam on the analysed soil surface followed by digital color image acquisition. The later, involves two convolutional models that are trained in a supervised manner to predict the soil roughness. The data set was produced in the laboratory both on synthetic and real soil samples. The labels used in the training process are the soil roughness values measured by using a pinboard. The detailed evaluation showed that the error of the automatic precision lies in the range of ground truth deviation, thus validating the proposed procedure.

Synthetic minority over-sampling technique-enhanced machine learning models for predicting recurrence of postoperative chronic subdural hematoma.

Chronic subdural hematoma (CSDH) is a neurological condition with high recurrence rates, primarily observed in the elderly population. Although several risk factors have been identified, predicting CSDH recurrence remains a challenge. Given the potential of machine learning (ML) to extract meaningful insights from complex data sets, our study aims to develop and validate ML models capable of accurately predicting postoperative CSDH recurrence. Data from 447 CSDH patients treated with consecutive burr-hole irrigations at Wenzhou Medical University's First Affiliated Hospital (December 2014-April 2019) were studied. 312 patients formed the development cohort, while 135 comprised the test cohort. The Least Absolute Shrinkage and Selection Operator (LASSO) method was employed to select crucial features associated with recurrence. Eight machine learning algorithms were used to construct prediction models for hematoma recurrence, using demographic, laboratory, and radiological features. The Border-line Synthetic Minority Over-sampling Technique (SMOTE) was applied to address data imbalance, and Shapley Additive Explanation (SHAP) analysis was utilized to improve model visualization and interpretability. Model performance was assessed using metrics such as AUROC, sensitivity, specificity, F1 score, calibration plots, and decision curve analysis (DCA). Our optimized ML models exhibited prediction accuracies ranging from 61.0% to 86.2% for hematoma recurrence in the validation set. Notably, the Random Forest (RF) model surpassed other algorithms, achieving an accuracy of 86.2%. SHAP analysis confirmed these results, highlighting key clinical predictors for CSDH recurrence risk, including age, alanine aminotransferase level, fibrinogen level, thrombin time, and maximum hematoma diameter. The RF model yielded an accuracy of 92.6% with an AUC value of 0.834 in the test dataset. Our findings underscore the efficacy of machine learning algorithms, notably the integration of the RF model with SMOTE, in forecasting the recurrence of postoperative chronic subdural hematoma. Leveraging the RF model, we devised an online calculator that may serve as a pivotal instrument in tailoring therapeutic strategies and implementing timely preventive interventions for high-risk patients.

Converted wave tomography based on inverse level set and adjoint formulation

SUMMARY Shear wave velocity (Vs) is a fundamental property of elastic media whose estimation from PS converted waves is challenging and requires modelling the boundary where P to S conversion occurs. This paper presents a PS tomography where seismic wave conversion/reflection points correspond to reflectors modelled with the level-set function set to zero [ϕ(x, z) = 0]. The proposed method aims for stable Vs inversion in a seismic acquisition setting using multicomponent receivers. Synthetic models simulating true Vs, Vp and the location of the geological reflector are used in the study. The inversion starts by locating a flat reflector, ϕ(x, z) = 0, which defines the zone Ω1 between the surface and the reflector, where the initial Vs and Vp fields are also set. To calculate the traveltimes of incident PT (P wave that propagates in Ω1 from source to the reflector), converted PS and reflected PP waves, for both observed and modelled data (forward problem), the methodology proposed by Rawlinson and Sambridge is adopted. This method uses the arrival times of the P waves, Tpt, from the seismic source at each reflector point as secondary sources generating the times Tps and Tpp. These times are calculated as a solution to the eikonal equation by using the Fast Marching method. The PS and PP residual times are minimized by updating Vs, Vp and ϕ(x, z) = 0 through adjoint variables designed from a formulation using Lagrange Multipliers in a variational context. The performance of the algorithm is evaluated for models with synclinal, sinusoidal and monoclinal reflector geometries using numerical tests considering the inversion of: (1) ϕ, given the true values of Vs and Vp; (2) ϕ and Vs, given the true value of Vp; (3) ϕ and Vp, given the true value of Vs and (4) the three parameters ϕ, Vs and Vp, simultaneously. Good results are obtained by inverting Vs and ϕ, given the true value of Vp. The simultaneous inversion of the three parameters exhibits promising results, despite the illumination problems caused by the different distribution of the PS, PP and PT time gradients due to the geometry of the reflectors and the acquisition setting (sources–receivers in the same plane). The proposed tomography estimates Vs and reflector positions which could help in statics corrections and improve the lithological characterization of near surface.

Bayesian joint inversion of seismic and electromagnetic data for reservoir lithofluid facies, including geophysical and petrophysical rock properties

A Bayesian approach is developed to estimate lithofluid facies and other rock properties conditioned on seismic and electromagnetic data for reservoir characterization. Prior distributions are assumed to be facies-related Gaussian modes of geophysical rock properties directly acquired or converted from petrophysical properties by calibrated rock-physics modeling. An original generalization includes two distributions in the same marginalization integral, analytically solved under a linearized Gaussian assumption to provide a facies model likelihood conditioned on geophysical data. Because computing this probability for all possible facies configurations may be impractical, a Markov chain Monte Carlo algorithm efficiently samples models to provide a full posterior distribution. The linearized Gaussian approach allows the computation of the conditional distributions of geophysical and petrophysical rock properties by applying local deterministic inversions over the many sampled facies models. The inversion uses simulated geophysical data from a 1D synthetic model based on the geologic scenario and a well from a selected marine oil field. Two other wells from the same reservoir are used to gather prior distributions. Data from the well, calibration of the rock-physics modeling, and facies matching between the priors and the synthetic model are presented and discussed. Numerical tests validate nonlinear forward-modeling adaptations based on the assumed linearized Gaussian approach. The simulated stand-alone and joint geophysical data sets are then inverted for lithofluid facies models under different prior inputs. Two challenging geoelectric scenarios also are tested, one with lower resistivity contrasts and another with a misguided background model. All results demonstrate a gain in precision and accuracy when associating these geophysical signals to estimate the oil column. Facies-conditioned inversions for the rock properties also indicate potential for quantitative reservoir interpretations.

Comprehensive analysis of synthetic learning applied to neonatal brain MRI segmentation.

Brain segmentation from neonatal MRI images is a very challenging task due to large changes in the shape of cerebral structures and variations in signal intensities reflecting the gestational process. In this context, there is a clear need for segmentation techniques that are robust to variations in image contrast and to the spatial configuration of anatomical structures. In this work, we evaluate the potential of synthetic learning, a contrast-independent model trained using synthetic images generated from the ground truth labels of very few subjects. We base our experiments on the dataset released by the developmental Human Connectome Project, for which high-quality images are available for more than 700 babies aged between 26 and 45 weeks postconception. First, we confirm the impressive performance of a standard UNet trained on a few volumes, but also confirm that such models learn intensity-related features specific to the training domain. We then confirm the robustness of the synthetic learning approach to variations in image contrast. However, we observe a clear influence of the age of the baby on the predictions. We improve the performance of this model by enriching the synthetic training set with realistic motion artifacts and over-segmentation of the white matter. Based on extensive visual assessment, we argue that the better performance of the model trained on real T2w data may be due to systematic errors in the ground truth. We propose an original experiment allowing us to show that learning from real data will reproduce any systematic bias affecting the training set, while synthetic models can avoid this limitation. Overall, our experiments confirm that synthetic learning is an effective solution for segmenting neonatal brain MRI. Our adapted synthetic learning approach combines key features that will be instrumental for large multisite studies and clinical applications.

Assess the Effectiveness of Multichannel Analysis of Surface Waves Method in Mapping Ancient Structures in Ultrashallow Aquatic Environments: The Case of Agioi Theodoroi, Greece

ABSTRACTThe present geophysical research aims to evaluate the applicability of multichannel analysis of surface waves (MASW) on mapping ultrashallow underwater ancient masonry remnants. The work presents the analysis from a single seismic line using MASW and seismic refraction tomography (SRT) methods and its corresponding electrical resistivity tomography (ERT) section surveyed at the submerged prehistoric site of Agioi Theodoroi area located 10 km eastern of Heraklion, Crete, Greece. The 2D MASW velocity model exhibits significant correspondence with the resistivity structure extracted from the ERT data, showing lateral S‐wave velocity (Vs) variations at the positions where the high resistivity anomalies exist. The analysis of synthetic seismic data calculated from a respective model reproduced a comparable S‐wave velocity pseudo‐section with the real data. However, the investigated targets (submerged buried masonry) appear shallower and wider in MASW sections than in the real world and the corresponding synthetic models, due to insufficient vertical and horizontal resolution of this method. Surface waves travelling through the seafloor sediments (Scholte‐waves) demonstrate very low velocity values. This makes them suitable for the detection of shallow and relatively large (&gt; 0.5 m) underwater manmade structures, providing the enhancement of MASW method resolution, by utilizing a high frequency (&gt; 100 Hz) seismic source, recording short Scholte wavelengths (≤ 1 m) and using shorter (≤ 0.5 m) receiver spacing and array length. Consequently, the results of this work demonstrate the potential in employing conventional seismic techniques in the delineation of underwater antiquities and the revealing of the cultural dynamics in very shallow off‐shore archaeological sites.

Study on Soil Total Nitrogen Content Prediction Method Based on Synthetic Neural Network Model

Rational utilization of soil total nitrogen is one of the keys to achieving sustainable agricultural development. By accurately measuring the content of total nitrogen in the soil, the utilization efficiency of nitrogen in the soil can be improved, and the scientific use of chemical fertilizers can reduce the pressure of agriculture on natural resources and realize the sustainable development of agriculture. In order to measure soil total nitrogen content simply and accurately, combined with the method of artificial olfactory systems, a new method of soil total nitrogen content detection based on convolutional noise reduction autoencoder (CDAE)–whale optimization algorithm (WOA)–deep residual shrinkage network (DSRN) is proposed. In order to obtain more salient features for fusion, the channel mechanism of the DSRN is improved by adding global Max pooling. The model uses a CDAE for the first filtering stage to automatically obtain data that filters simple noise and uses the WOA to automatically optimize hyperparameters. Finally, the optimized hyperparameters were used to train the DRSN for secondary filtering and predict the soil total nitrogen content. Experimental results show that the R2 of CAE-WOA-DSRN test set is 0.968, which is significantly better than the R2 of a traditional algorithm (0.873) and a simple BP network (0.877), and it can more accurately measure soil total nitrogen content.

A realistic aneurysm clipping simulation combining 3D-printed and placenta-based models-how I do it.

Neurovascular surgery, particularly aneurysm clipping, is a critical skill for aspiring neurosurgeons. However, hands-on training opportunities are limited, especially with the growing popularity of endovascular techniques. To address this challenge, we present a novel neurovascular surgical training station that combines synthetic 3D-printed models with placental vascular structures to create a semi-realistic surgical field. Our model consists of three components: a 3D-printed skull replica with anatomical landmarks, a malleable silicone parenchyma with a Sylvian fissure, and vascular layers (placenta). The placental vascular layer is catheterized and perfused to replicate pulsatile flow, offering a realistic aneurysm simulation. This innovative training station provides a cost-effective solution (approximately 200 USD once) without ethical constraints. Surgeons can practice essential skills such as Sylvian fissure dissection, managing anatomical constraints like bone, and achieving proximal vascular control. The model's realism allows for training in various scenarios, including clipping with different hand orientations and handling ruptures realistically. Our neurovascular surgical station bridges the gap between existing training models, offering affordability, ecological considerations, and minimal ethical concerns. It empowers neurosurgery residents to refine their skills in handling both emergencies and elective cases under close-to-real surgical conditions, with the potential for independent practice and senior supervision.

Sustentaculum fracture fixation with lateral plate or medial screw fixation are equivalent

BackgroundFixation of sustentaculum tali fractures is important to maintain the biomechanical function of the subtalar joint. A common method of fixation is securing the sustentacular fragment by way of a laterally based locking plate (LP). A medial approach with a single screw (MS) has been proposed as an alternative method of fixation. MethodsFive pairs of formalin-preserved cadaveric ankles with the subtalar joint and interosseous ligaments intact (“osseous cadavers”) and four pairs of fresh-frozen cadaveric ankles with soft-tissue preserved dissected from mid-tibia down (“soft tissue cadavers”) were used in the study. The left ankle was randomly assigned to one of the two fixation methods (LP or MS), while the right ankle was the opposite. These same steps for fixation were repeated for six synthetic ankle models. All models were loaded with a body mass of 80 kg. Statistical differences between LP and MS stiffness were determined using a paired t-test in cadavers and un-paired t-tests in synthetic ankles. ResultsFor osseous cadaveric ankles, LP demonstrated a mean stiffness of 232.95(SD: 59.96) N/mm, while MS was 239.72(SD:131.09) N/mm (p = 0.9293). For soft tissue cadaveric ankles, LP mean stiffness was 133.58(SD:37.84) N/mm, while MS was 134.88(SD:20.75) N/mm (p = 0.9578). For synthetic ankles, LP mean stiffness was 220.40(SD:81.93) N/mm, while MS was 261.50(SD:100.21) N/mm (p = 0.6116). ConclusionsAcross all three models, there was no significant difference between LP and MS methods. Retrospective observational studies are recommended to assess patient outcomes from each of the methods.

RSUIGM: Realistic Synthetic Underwater Image Generation with Image Formation Model

In this paper, we propose to synthesize realistic underwater images with a novel image formation model, considering both downwelling depth and line of sight (LOS) distance as cue and call it as Realistic Synthetic Underwater Image Generation Model, RSUIGM. The light interaction in the ocean is a complex process and demands specific modeling of direct and backscattering phenomenon to capture the degradations. Most of the image formation models rely on complex radiative transfer models and in-situ measurements for synthesizing and restoration of underwater images. Typical image formation models consider only line of sight distance z and ignore downwelling depth d in the estimation of effect of direct light scattering. We derive the dependencies of downwelling irradiance in direct light estimation for generation of synthetic underwater images unlike state-of-the-art image formation models. We propose to incorporate the derived downwelling irradiance in estimation of direct light scattering for modeling the image formation process and generate realistic synthetic underwater images with the proposed RSUIGM, and name it as RSUIGM dataset . We demonstrate the effectiveness of the proposed RSUIGM by using RSUIGM dataset in training deep learning based restoration methods. We compare the quality of restored images with state-of-the-art methods using benchmark real underwater image datasets and achieve improved results. In addition, we validate the distribution of realistic synthetic underwater images versus real underwater images both qualitatively and quantitatively. The proposed RSUIGM dataset is available here.

Validation of Dynamic 3D MRI for Urodynamics Assessment Using an Anatomically Realistic In Vitro Model of the Bladder.

Lowery urinary tract symptoms (LUTS) affect a large majority of the aging population. 3D Dynamic MRI shows promise as a noninvasive diagnostic tool that can assess bladder anatomy and function (urodynamics) while overcoming challenges associated with current urodynamic assessment methods. However, validation of this technique remains an unmet need. In this study, an anatomically realistic, bladder-mimicking in vitro flow model was created and used to systematically benchmark 3D dynamic MRI performance using a highly controllable syringe pump. Time-resolved volumes of the synthetic bladder model were obtained during simulated filling and voiding events and used to calculate volumetric flowrate. During MRI acquisitions, pressure during each event was recorded and used to create PV loops for work assessment. Error between control and MRI-derived volume for voiding and filling events exhibited 3.36% and 4.66% differences, respectively. A slight increase in average error was observed for MRI-derived flowrate when compared to the control flowrate (4.90% and 7.67% for voiding and filling, respectively). Overall, average error in segmented volumes increased with decreasing volume flowrate. Pressure drops were observed during voiding. Pressure increased during filling. Enhanced validation of novel 3D MRI urodynamics is achieved by using high-resolution PIV for visualizing and quantifying velocity inside the bladder model, which is not currently possible with 3D Dynamic MRI.

Petrophysical parameter estimation using Biot-poroelastic full-waveform inversion

SUMMARY The increasing need for petrophysical parameter inversion has spurred the advancement of full-waveform inversion (FWI). This study introduces a new inversion method that can directly estimate petrophysical parameters from seismic waveform data. Specifically, we modified the regular elastic wave equation by including Biot poroelastic parameters and designed an inversion workflow based on the framework of elastic FWI. The newly introduced inversion algorithms have demonstrated their effectiveness and accuracy using the Marmousi model and a synthetic model related to the gas hydrate deposits based on the Shenhu area of the South China Sea. Our work demonstrates the feasibility of directly inverting petrophysical parameters and assessing if a deposit contains gas hydrate.

Geoelectrical image of the Sabalan geothermal reservoir from magnetotelluric studies

The magnetotelluric (MT) survey of the Sabalan geothermal zone, which includes 22 stations along three traverse profiles across the main prospect zone, was conducted in 2007. The geophysical investigation utilized sharp boundary inversion of processed MT data due to the high contrast of electrical resistivity between embedded layers. This method was preferred to suppress the smeared-out impact of the smooth inversion algorithm on retrieving geological layering. The analysis of MT data led to the use of a 2D inversion algorithm to visualize the geothermal reservoir. Additionally, the geoelectrical strike of the main geothermal target was determined, showing some inconsistency across the profiles. The simulation of synthetic numerical models is conducted to evaluate the effectiveness of sharp boundary inversion compared to a smooth algorithm in sharp gradient mediums, specifically in terms of electrical characteristics, for plausible geothermal scenarios. The Finite Element method (FEM) was employed to construct these models, followed by running the 2D forward and inversion processes. The meshing and initial parameters of the modeling were set based on information obtained from prior geological and geophysical investigations. A sharp boundary inversion can be accurately implemented by introducing layering constraints along each profile surrounding the probable sought geothermal reservoir target. These layering constraints encompass the conductive cap, topsoil, and basement rocks, which are the key components of a plausible geothermal configuration. The results of the comparison indicate that the sharp boundary inversion technique effectively restored the boundary between neighboring layers that had significant differences in electrical resistivity. This was achieved when the smoothing algorithm failed to produce an accurate representation of the layered model. Ultimately, conceptual geological models were developed to illustrate the plausible structures corresponding to each profile, focusing on the sought geothermal reservoir targets. It can be deduced that many misunderstandings in the smoothing inversion algorithm will be omitted if the sharp boundary algorithm is used in highly electrical gradient structures. Based on the outcomes, a trachyandesite, andesite, and altered andesite reservoir exists at an elevation of 1600 to −2000 m. The reservoirs were recovered precisely with sharp and discriminable interfaces. Therefore, accurate interfaces led us to determine the conductive cap, heat source, and topsoil stacking as the main components of the geothermal system.