Volume Error Research Articles

Partial voxel interpolation to reduce partial volume error of cardiac computed tomography ventricular volumetry in patients with congenital heart disease.

Varying degrees of partial volume error depending on the complexity of the endocardial borders are inevitable in threshold-based cardiac computed tomography (CT) ventricular volumetry. These errors can potentiallybe reduced by using a partial voxel interpolation (PVI) method, but this has not been tested for cardiac CT ventricular volumetry. To evaluate the partial volume error-reducing effects of the PVI method in cardiac CT ventricular volumetry among patients with congenital heart disease (CHD). The cardiac CT ventricular volumetry data were obtained from 55 patients (median age 12.0 years) with CHD. The ventricular and myocardial volumes, ejection fraction and ventricular mass-volume ratio were quantified and compared before and after the PVI method. The correlation between the myocardial volumes in theend-systolic and end-diastolic phases was tested. The effect of the PVI method on the classification of ventricular hypertrophy was evaluated. The indexed ventricular volumes after PVI were significantly smaller (7.4-11.5%) than those before PVI (P<0.001). In contrast, the indexed myocardial volumes were significantly larger (6.2-27.7%) after PVI (P<0.001). The ejection fractions and mass-volume ratios were significantly larger (1.6-2.2% and 19.7-42.5%, respectively) after PVI (P<0.001 and P<0.001, respectively). The indexed myocardial masses showed prominently high correlation between the end-systolic and end-diastolic phases (R, 0.961-0.990; P<0.001). The proportions of no and severe hypertrophy were significantly decreased (P<0.002) and increased (P<0.032), respectively, after the application of the PVI method. The PVI method can reduce partial volume error in cardiac CT ventricular volumetry among patients with CHD.

Assessment of residual geometrical errors of clinical target volumes and their impact on dose accumulation for head and neck radiotherapy

PurposeTo assess the residual geometrical errors (dr) and their impact on the clinical target volumes (CTV) dose coverage for head and neck cancer (HNC) proton therapy patients. MethodsWe analysed 28 HNC patients treated with 70 Gy (RBE) and 54.25 Gy (RBE) to the therapeutic CTV70 and prophylactic CTV54.25, respectively. Daily cone beam CTs were converted to high quality synthetic CTs (sCTs). The CTVs from the nominal CT were propagated to the corresponding sCTs using a hybrid deformable image registration (propagated CTVs) in RayStation 11B. For 11 patients, all propagated CTVs were reviewed by our HNC radiation oncologist (physician corrected CTVs).The residual geometrical error dr was quantified as a function of the daily CTVs volume overlap with the nominal plan CTV. The errors dr(propagated CTVs) and dr(physician corrected CTVs) and the difference in dice similarity coefficients (ΔDSC) were determined. Using clinical plans, dose coverage and the tumor control probability (TCP) for the nominal, accumulated and voxel-wise minimum scenarios were determined. ResultsThe difference in the residual geometrical error dr (propagated CTVs – physician corrected CTVs) and mean DSC (|ΔDSC|mean) were minor: Δdr(CTV70) = 0.16 mm, Δdr(CTV54.25) = 0.26 mm, |ΔDSC|mean < 0.9%. For all 28 patients, dr(CTV70) = 1.91 mm and dr(CTV54.25) = 1.90 mm. However, CTV54.25 above and below the cricoid cartilage differed substantially (1.00 mm c.f. 3.93 mm). The CTV54.25 coverage below the cricoid was then almost always lower, although the TCP of the accumulated dose was higher than the TCP of the voxel-wise minimum dose. ConclusionsSetup uncertainty setting of 2 mm is possible. The feasibility of using propagated CTVs for error determination is demonstrated.

Optimization of Cascade Small Hydropower Station Operation in the Jianhe River Basin Using a One-Dimensional Hydrodynamic Model

Hydropower development brings benefits in terms of power generation and flood control, but it also has inevitable ecological impacts. These impacts must be considered and addressed in order to ensure sustainable development and minimize harm to the environment. This study utilized the MIKE 11 HD modeling system to construct a hydrological and hydrodynamic model of the Jianhe River basin. The model incorporates the flow demand of ecologically sensitive targets for scheduling purposes and was calibrated and validated using hydrological data from 2014 to 2022. The hydrodynamic model was then applied to analyze the evolution characteristics of the water level in the main stream of the Jianhe River, identify key areas and periods for hydropower station operation, and calculate the minimum ecological water requirement using verification and estimation methods. Based on these findings, an ecological dispatching scheme for the cascade hydropower stations in the Jianhe River basin was developed. The results demonstrate satisfactory performance of the constructed NAM model for rainfall runoff and the 1D hydrodynamic MIKE 11 HD model for the Jianhe River basin. The deterministic coefficients exceed 0.8, and the relative errors in the total water volume are below 5.5%. The critical time and space interval for hydropower station operation in the main stream of the Jianhe River is identified as December to February of the following year, with the highest risk of flow interruption occurring in January, primarily concentrated between the Duoluo II and Huahai hydropower stations. If the appropriate dispatching scheme is not implemented in the areas prone to flow interruption during critical periods, it will have a negative impact on the ecological environment. These findings provide a scientific basis and decision support for developing multi-objective ecological flow guarantee schemes for rivers.

Drop-on-Demand Inkjet Drop Control With One-Step Look Ahead Estimation of Model Parameters

Applications of drop-on-demand inkjet printing in dosage-matter manufacturing and scalable patterning are attributed to its capacity for producing consistent dosages with high placement accuracy. In practice, with the same drop jetting profile, drop volume and drop jetting velocity are affected by variations in ink properties and environmental conditions. Open-loop calibrations are time-consuming and contribute to frequent line stoppage or unacceptable product variations. In this work, a two-input two-output stochastic drop volume and jetting velocity model is derived based on ink jetting calibration data. A control algorithm using drop-image-based one-step look ahead estimation of process model parameters is developed to regulate drop volume and jetting velocity. Boundedness and convergence of the parameter estimation error and stability of the closed-loop system are provided. Experimental results demonstrate a significant reduction to within 1% relative error in the drop volume and jetting velocity using the proposed control algorithm.

Optimal estimation of injection rate for high-pressure common rail system using the extended Kalman filter

This paper presents an innovative estimation method of the fuel injection rate based on the rail pressure measurement signal for a high-pressure common rail system. A dynamic mathematical model of the injection process is constructed. To meet the requirement of the estimator design, a nonlinear state space model with three state variables is derived. On this basis, an optimal estimation method for the injection rate is proposed based on the extended Kalman filter, and the impact of noise covariance matrices is thoroughly examined. The results demonstrate that the proposed method enables fast convergence of the estimated injection rate. The coefficient R2 values between the estimated and the actual injection rate curves exceed 94%, and the estimated errors of the injection volume are within 5%.

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.

Validation and enhancement of a vocal fold medial surface 3D reconstruction approach for in-vivo application

In laryngeal research, studying the vertical vocal fold oscillation component is often disregarded. However, vocal fold oscillation by its nature is a three-dimensional process. In the past, we have developed an in-vivo experimental protocol to reconstruct the full, three-dimensional vocal fold vibration. The goal of this study is to validate this 3D reconstruction method. We present an in-vivo canine hemilarynx setup using high-speed video recording and a right-angle prism for 3D reconstruction of vocal fold medial surface vibrations. The 3D surface is reconstructed from the split image provided by the prism. For validation, reconstruction error was calculated for objects located at a distance of up to 15 mm away from the prism. The influence of camera angle, changing calibrated volume, and calibration errors were determined. Overall average 3D reconstruction error is low and does not exceed 0.12 mm at 5 mm distance from the prism. Influence of a moderate (5°) and large (10°) deviation in camera angle led to a slight increase in error to 0.16 mm and 0.17 mm, respectively. This procedure is robust towards changes in calibration volume and small calibration errors. This makes this 3D reconstruction approach a useful tool for the reconstruction of accessible and moving tissue surfaces.

A robust workflow for b-rep generation from image masks

A novel approach to generating watertight, manifold boundary representations from noisy binary image masks of MRI or CT scans is presented. The method samples an input segmented image and locally approximates the material boundary. Geometric error metrics between the voxelated boundary and an approximating template surface are minimized, and boundary point/normals are correspondingly generated. Voronoi partitioning is employed to perform surface reconstruction on the resulting oriented point cloud. The method performs competitively against other approaches, both in comparisons of shape and volume error metrics to a canonical image mask, and in qualitative comparisons using noisy image masks from real scans. The framework readily admits enhancements for capturing sharp edges and corners. The approach robustly produces high-quality b-reps that may be inserted into an image-based meshing pipeline for purposes of physics-based simulation.

An Integrated Framework for Real-Time Intelligent Traffic Management of Smart Highways

The new generation smart highways (NGSH) have emerged as irresistible trends to enhance the efficiency and safety of transportation systems. An integral component of the NGSH is the automation of the intelligent traffic management system (ITMS). This study investigates an integrated framework for the ITMS that incorporates the fine-grained microscopic simulation and deep learning technologies based on real-time traffic data. The framework commences by performing dynamic corrections based on the license plate, vehicle speed, location, and other information provided by the real-time bayonet data in order to simulate the realistic traffic flow along the highway. A deep learning model based on long short-term memory (LSTM) is then applied to predict the short-term traffic volume on major highway segments. Based on prediction results, a collaborative management method is constructed that combines variable speed limits and ramp metering. The case study on the Shanghai–Hangzhou–Ningbo Highway in China suggests the real-time simulation model can control the average error of the traffic volume on the main segments by 4.58%. The LSTM-based model can accurately predict the short-term traffic volume with a relative error of 85% below 15% in both offline and online modes. Consequently, the proposed collaborative framework improves the average speed and traffic volume of controlled sections by 3.62% and 4.35%, respectively, demonstrating its effectiveness in improving the operation and management of the smart highways.

An End-to-End Physics-Informed Neural Network for Defect Identification and 3-D Reconstruction Using Rotating Alternating Current Field Measurement

Alternating current field measurement (ACFM) technique has been widely used in defect detection of metal structures. However, the identification and reconstruction of defects depend on human experience or simple empirical formulas, which leads to misjudgment of defects and large quantization errors. This paper proposes an end-to-end physics-informed neural network for defect identification and three-dimensional (3-D) reconstruction. The high precision automatic detection system with a specially designed probe is established to detect defects in any direction. The Faster-RCNN network is used to identify and classify defects. The physics-informed Pix2Pix network is constructed to realize 3-D reconstruction of defects with different types. The results show that the established end-to-end physics-informed neural network can realize the identification and 3-D reconstruction of defects, in which the mean average precision is 0.9982, the average length error of cracks is 0.9249 mm, the average depth error of cracks is 0.3402 mm, the average volume error of corrosion is 0.0667, and the average maximum depth error of corrosion is 0.3464 mm.

Full implicit simulator of hydrate (FISH) and analysis on hydrate dissociation in porous media in the cubic hydrate simulator

Natural gas hydrate is a promising strategic energy resource in marine and permafrost sediments. To precisely characterize the coupled Thermal-Hydraulic-Chemical (THC) process in hydrate reservoirs, a bona fide mathematical model with improved key parameters is needed. We developed a Full Implicit Simulator of Hydrate (FISH), a three-phase, four-component mathematical model, to simulate gas recovery from hydrate reservoirs. The experiment of methane hydrate formation and dissociation under depressurization via a single vertical well was executed in the Cubic Hydrate Simulator (CHS). FISH was employed to reproduce the experiment of hydrate dissociation in quartz sands. This study successfully obtains unified parameters that are suitable for the entire hydrate dissociation process. Simulation results of gas and aqueous production profiles and the temperature distribution agreed well with that in the experiment. The Mean Absolute Percentage Error (MAPE) of the simulated volume of gas produced was 2.845%. The mass conservation in the pressure vessel was confirmed by both experimental data and numerical simulation results. This numerical simulator can be applied to predict the behavior of hydrate-bearing-sediments in the laboratory or to evaluate the gas production potential from the marine or permafrost hydrate reservoirs.

Performance of retinal fluid monitoring in OCT imaging by automated deep learning versus human expert grading in neovascular AMD

PurposeTo evaluate the reliability of automated fluid detection in identifying retinal fluid activity in OCT scans of patients treated with anti-VEGF therapy for neovascular age-related macular degeneration by correlating human expert and automated measurements with central retinal subfield thickness (CSFT) and fluid volume values.MethodsWe utilized an automated deep learning approach to quantify macular fluid in SD-OCT volumes (Cirrus, Spectralis, Topcon) from patients of HAWK and HARRIER Studies. Three-dimensional volumes for IRF and SRF were measured at baseline and under therapy in the central millimeter and compared to fluid gradings, CSFT and foveal centerpoint thickness (CPT) values measured by the Vienna Reading Center.Results41.906 SD-OCT volume scans were included into the analysis. Concordance between human expert grading and automated algorithm performance reached AUC values of 0.93/0.85 for IRF and 0.87 for SRF in HARRIER/HAWK in the central millimeter. IRF volumes showed a moderate correlation with CSFT at baseline (HAWK: r = 0.54; HARRIER: r = 0.62) and weaker correlation under therapy (HAWK: r = 0.44; HARRIER: r = 0.34). SRF and CSFT correlations were low at baseline (HAWK: r = 0.29; HARRIER: r = 0.22) and under therapy (HAWK: r = 0.38; HARRIER: r = 0.45). The residual standard error (IRF: 75.90 µm; SRF: 95.26 µm) and marginal residual standard deviations (IRF: 46.35 µm; SRF: 44.19 µm) of fluid volume were high compared to the range of CSFT values.ConclusionDeep learning-based segmentation of retinal fluid performs reliably on OCT images. CSFT values are weak indicators for fluid activity in nAMD. Automated quantification of fluid types, highlight the potential of deep learning-based approaches to objectively monitor anti-VEGF therapy.

Foodopedia: A Convolutional Neural Network Based Food Calorie Estimation

The number of calories consumed determines how healthy a body is in the modern world; therefore, it's important to watch your calorie intake to be healthy. People must keep track of their caloric intake in order to become in shape or maintain a healthy weight. The suggested model uses a deep learning algorithm to offer a novel method of calorie measurement. In the medical area, the estimation of dietary calories is crucial. This measurement is derived from the representation of food in various objects, such as fruits and vegetables. The neural network is used to take this measurement. This technique uses a convolutional neural network to determine the calories in food. An image of food is used as the input for this calculated model. The suggested CNN model uses food object identification to calculate the calorie content of the food. Volume error estimation serves as the primary parameter for the outcome, while calorie error estimation serves as the secondary parameter.

A comprehensive array of gray matter labels for the MIITRA atlas: Interoperability with complementary atlases

AbstractBackgroundA comprehensive array of gyral‐based,cytoarchitecture‐based,and functional connectivity‐based gray matter labels were constructed in the Multichannel Illinois Institute of Technology &amp; Rush university Aging (MIITRA) atlas space in order to enhance the functionality of the atlas and its interoperability with complementary atlases.MethodT1w images from the 400 older adults included in the construction of the MIITRA atlas (50%male;64.9‐98.9 years of age) were processed with Freesurfer’s standard recon‐all pipeline. The Freesurfer outputs for all images were manually edited. Parcellations from Yeo,Desikan‐Killiany,HCP‐MMP,Brainnetome,Brodmann,Destrieux,Campbell,Flechsig,Mindboggle,Kleist,Smith,and EconomoCT atlases were projected onto the surface of each image and then converted to volumetric labels. The T1w images were also nonlinearly registered using ANTs‐SyN to the MNI152‐6thgen,MNI‐Colin,and the MNI‐2009b templates, and volumetric labels from Harvard‐Oxford,Julich,AAL3,Buckner,CoBrALab,CAREN,Striatum‐subdivision and Hippocampal‐subfields atlases were warped to the space of each image. The ANTs‐derived transformations applied on individual T1w images to build the MIITRA T1w template were used to map the corresponding gray matter labels from raw space to exact physical locations in the final MIITRA space. The performance of the MIITRA gray matter labels in segmentation of the gray matter of a separate group of 100 older adult individuals was evaluated in terms of label overlap, geometry, and dissimilarity when comparing the MIITRA labels warped to each individual’s space, to the respective reference labels from the source atlases.ResultExamples of the gray matter labels in MIITRA space are shown in Figure1. The measures of overlap between the warped and reference labels for each set of labels were generally high, with an average Dice coefficient of 0.78±0.3, average Jaccard coefficient of 0.59±0.27(Fig.2), sensitivity of 0.74±0.1, and specificity of 0.86±0.35(Fig.3). The label geometry showed a high correlation both in terms of the average volume of each label(correlation coefficient = 0.997,p‐value&lt;10−10) and in terms of the average surface area of each label(correlation coefficient = 0.991,p‐value&lt;10−10)(Fig.4). The values of dissimilarity were low, with an average volume error of 0.38±0.2(Fig.5).ConclusionThe gray matter labels in the MIITRA, in combination with the high‐resolution T1w template of the atlas, allowed segmentation of the gray matter of older adults that is in good agreement with the reference labels from the source atlases.

Evaluation of a multi-staged bias correction approach on CHIRP and CHIRPS rainfall product: a case study of the Lake Hawassa watershed

Abstract A promising future development area to improve the accuracy of satellite rainfall estimates (SREs) is accessing merits from different sources of data through combining algorithms. The main objective of this study is to assess the accuracy and importance of the fused multistage approach of bias correction. Accordingly, two versions of resampled and spatially bias-corrected Climate Hazards Group Infrared Precipitation (CHIRP) estimates were merged with ground measurements using a conditional merging procedure. Results of applied performance measures (i.e. seven) on corrected and merged CHIRP SREs show that the Percent of Detection (POD) and Percent Volume Error (PVE) have improved. Depending on the combination of coupled stations for validation, up to 70 and 50% PVE improvement was achieved at some stations for wet and dry periods, respectively. Moreover, the bias-corrected and conditionally merged CHIRP SREs have outperformed the estimates by resampling CHIRP with station dataset (CHIRPS) over the sparsely populated western part of the watershed. However, the devised method was limited in considering dry-day events during bias correction, which in turn has affected the performance of the bias correction of the CHIRPS product. Finally, future research should concentrate on such methods of fusing to understand the benefits of various approaches and produce more precise rainfall records.

The novel extraordinary separation and preconcentration approach for Cd2+: “Hydrophobic Immiscible Chelating Fluid” based micro-extraction

The novel, extraordinary and highly selective separation and preconcentration approach based on hydrophobic immiscible chelating fluid (HICF) having superior features (simultaneous chelation, extraction and masking) and combined with automatized syringe discharge system by peristaltic pump was established firstly for simultaneous chelation and micro-extraction of Cd2+. Mixture of tetra butyl ammonium iodide, tetra butyl ammonium fluoride, n-pentanol (1:1:10) and dithizone were used to formulate HICF. The parameters including pH, chelator concentration on both characteristic of HICF and extraction of Cd2+, time of vortex mixing, amount of HICF and sample volume were optimized and investigated in detail. The optimized method has permitted to obtain 25 preconcentration factor (PF) and detect the lowest cadmium concentration having 2.1 µg L−1 (LOD) in the aqueous solution. Interference effect of defiant metal cations was investigated in detail. The dynamic range of the method (LDR) was obtained linearly between 7.2 and 1500 µg L−1. Relative standard deviations (% RSD) were below than % 4.2 throughout the experiments. The envisaged possible extraction mechanisms were explained. The proposed novel method was validated with applications of multiple analyte-addition recovery tests in water samples (tap, lake and natural spring water) and analysis of standard reference material (UME EnvCRM 03 Elements in Soil). Quantitative satisfactory recovery values changing between 98 and 106 % were obtained. Validated method was also applied to determine Cd contents of sediment, mushroom and raw tobacco samples. The main advantage of HICF is that it contains masking and chelating agents and extraction solvent in only one liquid. Therefore, possible concentration and volume errors were minimized by single micropipette injection that ensured time saving and simplicity for applicability of the method by the analysts. Moreover, automated syringe discharge system has provided repeatable and precision extraction results.

A WRF/WRF-Hydro Coupled Forecasting System with Real-Time Precipitation–Runoff Updating Based on 3Dvar Data Assimilation and Deep Learning

This study established a WRF/WRF-Hydro coupled forecasting system for precipitation–runoff forecasting in the Daqing River basin in northern China. To fully enhance the forecasting skill of the coupled system, real-time updating was performed for both the WRF precipitation forecast and WRF-Hydro forecasted runoff. Three-dimensional variational (3Dvar) multi-source data assimilation was implemented using the WRF model by incorporating hourly weather radar reflectivity and conventional meteorological observations to improve the accuracy of the forecasted precipitation. A deep learning approach, i.e., long short-term memory (LSTM) networks, was adopted to improve the accuracy of the WRF-Hydro forecasted flow. The results showed that hourly data assimilation had a positive impact on the range and trends of the WRF precipitation forecasts. The quality of the WRF precipitation outputs had a significant impact on the performance of WRF-Hydro in forecasting the flow at the catchment outlet. With the runoff driven by precipitation forecasts being updated by 3Dvar data assimilation, the error of flood peak flow was decreased by 3.02–57.42%, the error of flood volume was decreased by 6.34–39.30%, and the Nash efficiency coefficient was increased by 0.15–0.52. The implementation of LSTM can effectively reduce the forecasting errors of the coupled system, particularly those of the time-to-peak and peak flow volumes.

CNN‐based fully automatic wrist cartilage volume quantification in MR images: A comparative analysis between different CNN architectures

Automatic measurement of wrist cartilage volume in MR images. We assessed the performance of four manually optimized variants of the U-Net architecture, nnU-Net and Mask R-CNN frameworks for the segmentation of wrist cartilage. The results were compared to those from a patch-based convolutional neural network (CNN) we previously designed. The segmentation quality was assessed on the basis of a comparative analysis with manual segmentation. The best networks were compared using a cross-validation approach on a dataset of 33 3D VIBE images of mostly healthy volunteers. Influence of some image parameters on the segmentation reproducibility was assessed. The U-Net-based networks outperformed the patch-based CNN in terms of segmentation homogeneity and quality, while Mask R-CNN did not show an acceptable performance. The median 3D DSC value computed with the U-Net_AL (0.817) was significantly larger than DSC values computed with the other networks. In addition, the U-Net_AL provided the lowest mean volume error (17%) and the highest Pearson correlation coefficient (0.765) with respect to the ground truth values. Of interest, the reproducibility computed using U-Net_AL was larger than the reproducibility of the manual segmentation. Moreover, the results indicate that the MRI-based wrist cartilage volume is strongly affected by the image resolution. U-Net CNN with attention layers provided the best wrist cartilage segmentation performance. In order to be used in clinical conditions, the trained network can be fine-tuned on a dataset representing a group of specific patients. The error of cartilage volume measurement should be assessed independently using a non-MRI method.

Calibration of hydrological models for ungauged catchments by automatic clustering using a differential evolution algorithm: The Gorganrood river basin case study

Abstract The hydrological model calibration is a challenging task, especially in ungauged catchments. The regionalization calibration methods can be used to estimate the parameters of the model in ungauged sub-catchments. In this article, the model of ungauged sub-catchments is calibrated by a regionalization approach based on automatic clustering. Under the clustering procedure, gauged and ungauged sub-catchments are grouped based on their physical characteristics and similarity. The optimal number of clusters is determined using an automatic differential evolution algorithm-based clustering. Considering obtained five clusters, the value of the silhouette measure is equal to 0.56, which is an acceptable value for goodness of clustering. The calibration process is conducted according to minimizing errors in simulated peak flow and total flow volume. The Storm Water Management Model is applied to calibrate a set of 53 sub-catchments in the Gorganrood river basin. Comparing graphically and statistically simulated and observed runoff values and also calculating the value of the silhouette coefficient demonstrate that the proposed methodology is a promising approach for hydrological model calibration in ungauged catchments.

Assessment of Setup Errors in Gynecological Malignancies Treated With Radiotherapy Using Onboard Imaging.

Introduction Radiotherapy plays a vital role in the management of gynecological malignancies. However, maintaining patient position poses a challenge during daily radiotherapy treatment of these patients. This study identifies and calculates setup errors in interfraction radiotherapy and optimum clinical target volume-planning target volume (CTV-PTV) margins in patients with gynecological malignancies. Material and methods A total of 38 patients with gynecological malignancies were included in the study. They were treated with a dose of 50 Gy in 25 fractions for five weeks, followed by brachytherapy. All patients were immobilized using a 4-point thermoplastic cast. Anteroposterior and lateral images were taken thrice weekly for five weeks. Setup verification was done using kilovoltage images obtained using Varian On-board Imager (Varian Medical System, Inc., Palo Alto, CA). Manual matching was done utilizing bony landmarks such as the widest portion of the pelvic brim, anterior border of S1 vertebrae, and pubic symphysis in the X, Y, and Z axes, respectively. Results A total of 1140 images were taken. The individual systematic errors ranged from -0.24 to 0.17 cm (LR), -0.15 to 0.19 cm (AP) and -0.36 to 0.29 cm (CC) while the individual random errors ranged from 0.04 to 0.36 cm (LR), 0.06 to 0.33 cm (AP) and 0.10 to 0.29 cm (CC). The calculated CTV-PTV margins in LR, AP and CC directions were 0.17, 0.18, and 0.25 cm (ICRU-62); 0.28, 0.31 and 0.47 cm in LR, AP and CC directions (Stroom's), and 0.32, 0.36 and 0.55 cm (Van Herk) respectively. Conclusion Based on this study, the calculated CTV-PTV margin is 6 mm in gynecological malignancies, and the present protocol of 7 mm of PTV margin is optimum.