Median Root Mean Square Error Research Articles

Image quality evaluation of intra-irradiation cone-beam computed tomography acquired during one- and two-arc prostate volumetric-modulated arc therapy delivery: A phantom study.

PurposeTo evaluate (a) the effects of megavoltage (MV)‐scatter on concurrent kilovoltage (kV) projections (P MVkV) acquired during rotational delivery, and (b) the image quality of intra‐irradiation cone‐beam computed tomography (ii‐CBCT) images acquired during prostate volumetric‐modulated arc therapy (VMAT) delivery.MethodsExperiment (1): P MVkVs were acquired with various MV beam parameters using a cylindrical phantom: field size (FS), MV energy (6 or 15 MV), dose rate (DR), and gantry speed. The average pixel values were calculated in a region on each P MVkV which were extracted at eight equally spaced gantry angles. Experiment (2): 11 one‐arc and seven two‐arc 15 MV prostate VMAT plans were used along with a pelvis phantom. One plan was selected from each of arc plans and its MV energy was changed to 6 MV. After P MVkVs were acquired, projections consisting of MV‐scatter only (P MVS) were acquired with closing kV blades and subtracted from P MVkV (P MVScorr). Projections by kV beams only were acquired (P kV). The corresponding CBCT images were reconstructed (CBCTMVkV, CBCTMVScorr, and CBCTkV). The root‐mean‐square errors (RMSEs) were calculated in prostate region and 3D gamma analysis was conducted, in which the CBCT‐number was used instead of doses between ii‐CBCT images and CBCTkV (30 HU/1 mm).ResultsExperiment (1): The MV‐scatters were dependent on the FSs, MV energies, and DRs. Experiment (2): The median RMSEs for CBCTMVScorr were decreased by 107.5 HU (1‐arc) and 42.9 HU (2‐arc) compared to those for CBCTMVkV. The median GPRs for CBCTMVScorr were 94.7% (1‐arc) and 93.4% (2‐arc), while those for CBCTMVkV were 61.1% and 79.9%, respectively. GPRs for 6 MV plans were smaller than those for 15 MV plans.ConclusionsThe number of MV‐scatters increased with larger FSs and DRs, and smaller MV energy. The MV‐scatters were corrected on the CBCTMVScorr regardless of the number of arcs.

Regional Infoveillance of COVID-19 Case Rates: Analysis of Search-Engine Query Patterns.

BackgroundTimely allocation of medical resources for coronavirus disease (COVID-19) requires early detection of regional outbreaks. Internet browsing data may predict case outbreaks in local populations that are yet to be confirmed.ObjectiveWe investigated whether search-engine query patterns can help to predict COVID-19 case rates at the state and metropolitan area levels in the United States.MethodsWe used regional confirmed case data from the New York Times and Google Trends results from 50 states and 166 county-based designated market areas (DMA). We identified search terms whose activity precedes and correlates with confirmed case rates at the national level. We used univariate regression to construct a composite explanatory variable based on best-fitting search queries offset by temporal lags. We measured the raw and z-transformed Pearson correlation and root-mean-square error (RMSE) of the explanatory variable with out-of-sample case rate data at the state and DMA levels.ResultsPredictions were highly correlated with confirmed case rates at the state (mean r=0.69, 95% CI 0.51-0.81; median RMSE 1.27, IQR 1.48) and DMA levels (mean r=0.51, 95% CI 0.39-0.61; median RMSE 4.38, IQR 1.80), using search data available up to 10 days prior to confirmed case rates. They fit case-rate activity in 49 of 50 states and in 103 of 166 DMA at a significance level of .05.ConclusionsIdentifiable patterns in search query activity may help to predict emerging regional outbreaks of COVID-19, although they remain vulnerable to stochastic changes in search intensity.

Estimation of Heart Rate Directly from ECG Spectrogram in Neonate Intensive Care Units.

This paper presents a simple yet novel method to estimate the heart frequency (HF) of neonates directly from the ECG signal, instead of using the RR-interval signals as generally done in clinical practices. From this, the heart rate (HR) can be derived. Thus, we avoid the use of peak detectors and the inherent errors that come with them.Our method leverages the highest Power Spectral Densities (PSD) of the ECG, for the bins around the frequencies related to heart rates for neonates, as they change in time (spectrograms).We tested our approach with the monitoring data of 6 days for 52 patients in a Neonate Intensive Care Unit (NICU) and compared against the HR from a commercial monitor, which produced a sample every second. The comparison showed that 92.4% of the samples have a difference lower than 5bpm. Moreover, we obtained a median MAE (Mean Absolute Error) between subjects equal to 2.28 bpm and a median RMSE (Root Mean Square Error) equal to 5.82 bpm. Although tested for neonates, we hypothesize that this method can also be customized for other populations.Finally, we analyze the failure cases of our method and found a direct co-allocation of errors due to moments with higher PSD in the lower frequencies with the presence of critical alarms related to other physiological systems (e.g. desaturation).

Magnitude Agreement, Occurrence Consistency, and Elevation Dependency of Satellite-Based Precipitation Products over the Tibetan Plateau

Satellite remote sensing is a practical technique to estimate global precipitation with adequate spatiotemporal resolution in ungauged regions. However, the performance of satellite-based precipitation products is variable and uncertain for the Tibetan Plateau (TP) because of its complex terrain and climate conditions. In this study, we evaluated the abilities of nine widely used satellite-based precipitation products over the Eastern Tibetan Plateau (ETP) and quantified precipitation dynamics over the entire TP. The evaluation was carried out from three aspects, i.e., magnitude agreement, occurrence consistency, and elevation dependency, from grid-cell to regional scales. The results show that the nine satellite-based products exhibited different agreement with gauge-based reference data with median correlation coefficients ranging from 0.15 to 0.95. Three products (climate hazards group infrared precipitation with stations (CHIRPS), multi-source weighted-ensemble precipitation (MSWEP), and tropical rainfall measuring mission multi-satellite precipitation analysis (TMPA)) generally presented the best performance with the reference data, even in complex terrain regions, given their root mean square errors (RMSE) of less than 25 mm/mon. The climate prediction center merged analysis of precipitation (CMAP) product has relatively coarse spatial resolution, but it also exhibited good performance with a bias of less than 20% in watershed scale. Two other products (precipitation estimation from remotely sensed information using artificial neural networks-cloud classification system (PER-CCS) and climate prediction center morphing technique-raw (CMORPH-RAW)) overestimated precipitation with median RMSEs of 87 mm/mon and 45 mm/mon, respectively. All the precipitation products generally exhibited better agreement with the reference data for rainy season and lower-elevation regions. All of the products captured precipitation occurrence well, with hit event over 60%, and similar percentages of missed and false event. According to the evaluation, the four products (CHIRPS, MSWEP, TMPA, and CMAP) revealed that the annual precipitation over the TP fluctuated between 333 mm/yr and 488 mm/yr during the period 2003 to 2015. The study indicates the importance of integration of multiple data sources and post-processing (e.g., gauge data fusion and elevation correction) for satellite-based products and have implications for selection of suitable precipitation products for hydrological modeling and water resources assessment for the TP.

Predicting Crop Evapotranspiration by Integrating Ground and Remote Sensors with Air Temperature Forecasts.

Water use efficiency in agriculture can be improved by implementing advisory systems that support on-farm irrigation scheduling, with reliable forecasts of the actual crop water requirements, where crop evapotranspiration (ETc) is the main component. The development of such advisory systems is highly dependent upon the availability of timely updated crop canopy parameters and weather forecasts several days in advance, at low operational costs. This study presents a methodology for forecasting ETc, based on crop parameters retrieved from multispectral images, data from ground weather sensors, and air temperature forecasts. Crop multispectral images are freely provided by recent satellite missions, with high spatial and temporal resolutions. Meteorological services broadcast air temperature forecasts with lead times of several days, at no subscription costs, and with high accuracy. The performance of the proposed methodology was applied at 18 sites of the Campania region in Italy, by exploiting the data of intensive field campaigns in the years 2014–2015. ETc measurements were forecast with a median bias of 0.2 mm, and a median root mean square error (RMSE) of 0.75 mm at the first day of forecast. At the 5th day of accumulated forecast, the median bias and RMSE become 1 mm and 2.75 mm, respectively. The forecast performances were proved to be as accurate and as precise as those provided with a complete set of forecasted weather variables.

Process‐Guided Deep Learning Predictions of Lake Water Temperature

AbstractThe rapid growth of data in water resources has created new opportunities to accelerate knowledge discovery with the use of advanced deep learning tools. Hybrid models that integrate theory with state‐of‐the art empirical techniques have the potential to improve predictions while remaining true to physical laws. This paper evaluates the Process‐Guided Deep Learning (PGDL) hybrid modeling framework with a use‐case of predicting depth‐specific lake water temperatures. The PGDL model has three primary components: a deep learning model with temporal awareness (long short‐term memory recurrence), theory‐based feedback (model penalties for violating conversation of energy), and model pretraining to initialize the network with synthetic data (water temperature predictions from a process‐based model). In situ water temperatures were used to train the PGDL model, a deep learning (DL) model, and a process‐based (PB) model. Model performance was evaluated in various conditions, including when training data were sparse and when predictions were made outside of the range in the training data set. The PGDL model performance (as measured by root‐mean‐square error (RMSE)) was superior to DL and PB for two detailed study lakes, but only when pretraining data included greater variability than the training period. The PGDL model also performed well when extended to 68 lakes, with a median RMSE of 1.65 °C during the test period (DL: 1.78 °C, PB: 2.03 °C; in a small number of lakes PB or DL models were more accurate). This case‐study demonstrates that integrating scientific knowledge into deep learning tools shows promise for improving predictions of many important environmental variables.

Use of SMAP Soil Moisture and Fitting Methods in Improving GPM Estimation in Near Real Time

Satellite-based precipitation products have been widely used in a variety of fields. However, near real time products still contain substantial biases compared with the ground data. Recent studies showed that surface soil moisture can be utilized in improving rainfall estimation as it reflects recent precipitation. In this study, soil moisture data from Soil Moisture Active Passive (SMAP) satellite and observation-based fitting are used to correct near real time satellite-based precipitation product Global Precipitation Measurement (GPM) in mainland China. The particle filter is adopted to assimilate the SMAP soil moisture into a simple hydrological model, the antecedent precipitation index (API) model; three fitting methods—i.e., linear, nonlinear, and cumulative distribution function (CDF) fitting corrections—both separately and in combination with the SMAP soil moisture data, are then used to correct GPM. The results show that the soil moisture-based correction significantly reduces the root mean square error (RMSE) and mean absolute errors (BIAS) of the original GPM product in most areas of China. The median RMSE value for daily precipitation over China is decreased by approximately 18% from 5.25 mm/day for the GPM estimates to 4.32 mm/day for the soil moisture corrected estimates, and the median BIAS value is decreased by approximately 13% from 2.03 mm/day to 1.76 mm/day. The fitting correction method alone also improves GPM, although to a lesser extent. The best performance is found when the SMAP soil moisture assimilation is combined with the linear fitting of observed precipitation, with a median RMSE of 4.00 mm/day and a BIAS of 1.69 mm/day. Despite significant reductions to the biases of the satellite precipitation product, none of these methods is effective in improving the correlation between the satellite product and observational reference. Leaf area index and the frequency of the SMAP overpasses are among the potential factors influencing the correction effect. This study highlights that combining soil moisture and historical precipitation information can effectively improve satellite-based precipitation products in near real time.

A clustering approach to 4D MRI retrospective sorting for the investigation of different surrogates

PurposeIn retrospective 4-Dimensional Magnetic Resonance Imaging (4D MRI) sorting, respiratory surrogate selection affects the image quality of reconstructed volumes. We propose a method for retrospective 4D MRI sorting based on clustering, which allowed us to compare the performance of single or multiple internal surrogates vs. a conventional external signal. MethodsA k-medoids clustering algorithm was exploited for sorting 2D MRI into 4D MRI, relying on (A) multiple or (B) single automatically tracked internal landmarks or (C) respiratory belt signal. 4D MRI reconstructions for seven liver cancer patients were compared to those of the state-of-the-art mutual information (MI) approach. Sorting artifacts were measured by the root mean square error (RMSE) between the diaphragm profile and a fitted second order curve. Diaphragm and tumor motions were evaluated. ResultsThe median RMSEs ranged 0.97–1.66 mm, 1.24–1.89 mm, 1.43–2.27 mm, 1.74–3.72 mm for the MI, (A), (B) and (C) methods, respectively. Significant differences (Friedman, α = 5%) were found between (C) and all other methods, and between (B) and MI approaches. The discrepancies between (A) and MI approaches ranged 1.1–6.2 mm and 0.7–5.3 mm respectively in diaphragm and tumor motions. Methods (A) and (B) showed similar ranges of motion. ConclusionWith multiple internal points, our method yielded the description of a higher range of motion and similar image quality with respect to the MI approach. The single point method led to more artifacts, suggesting the superior suitability of multiple internal surrogates for retrospective 4D MRI sorting. Considering internal rather than external information favored superior performance.

Robust Features for Elbow Joint Angle Estimation Based on Electromyography

A noisy environment is a major problem which has to be resolved to get a good performance in the estimation. A robust feature is important in order to obtain an accurate position of the elbow joint from the electromyography (EMG) signal. The objective of this research is to modify and assess the time domain features which robust against the white Gaussian noise. In this work, the EMG signal (from biceps) contaminated by artificial white Gaussian noise was extracted using twelve standard time domain features and one modified feature. The threshold of the modified feature (MYOPM) was calculated based on the root mean square (RMS) of the contaminated EMG signal. The linear Kalman filter was used to refine the EMG features and to improve the estimation. The robustness of the features was calculated using the root mean square error (RMSE). Based on the RMSE values, it shows that the proposed feature MYOPM is the most robust feature (the lowest median RMSE of 9º) for the signal to noise ratio (SNR) ranged between 17.96 and 60 dB, compared with the others’ features. The mean RMSE of the MYOPM feature improves by 27.91% from the prior feature (MYOP).

Optimization of training periods for the estimation model of three-dimensional target positions using an external respiratory surrogate

BackgroundDuring therapeutic beam irradiation, an unvisualized three-dimensional (3D) target position should be estimated using an external surrogate with an estimation model. Training periods for the developed model with no additional imaging during beam irradiation were optimized using clinical data.MethodsDual-source 4D-CBCT projection data for 20 lung cancer patients were used for validation. Each patient underwent one to three scans. The actual target positions of each scan were equally divided into two equal parts: one for the modeling and the other for the validating session. A quadratic target position estimation equation was constructed during the modeling session. Various training periods for the session—i.e., modeling periods (TM)—were employed: TM ∈ {5,10,15,25,35} [s]. First, the equation was used to estimate target positions in the validating session of the same scan (intra-scan estimations). Second, the equation was then used to estimate target positions in the validating session of another temporally different scan (inter-scan estimations). The baseline drift of the surrogate and target between scans was corrected. Various training periods for the baseline drift correction—i.e., correction periods (TCs)—were employed: TC ∈ {5,10,15; TC ≤ TM} [s]. Evaluations were conducted with and without the correction. The difference between the actual and estimated target positions was evaluated by the root-mean-square error (RMSE).ResultsThe range of mean respiratory period and 3D motion amplitude of the target was 2.4–13.0 s and 2.8–34.2 mm, respectively. On intra-scan estimation, the median 3D RMSE was within 1.5–2.1 mm, supported by previous studies. On inter-scan estimation, median elapsed time between scans was 10.1 min. All TMs exhibited 75th percentile 3D RMSEs of 5.0–6.4 mm due to baseline drift of the surrogate and the target. After the correction, those for each TMs fell by 1.4–2.3 mm. The median 3D RMSE for both the 10-s TM and the TC period was 2.4 mm, which plateaued when the two training periods exceeded 10 s.ConclusionsA widely-applicable estimation model for the 3D target positions during beam irradiation was developed. The optimal TM and TC for the model were both 10 s, to allow for more than one respiratory cycle.Trial registrationUMIN000014825. Registered: 11 August 2014.

A motion-tolerant approach for monitoring SpO2 and heart rate using photoplethysmography signal with dual frame length processing and multi-classifier fusion

Monitoring pulse oxygen saturation (SpO2) and heart rate (HR) using photoplethysmography (PPG) signal contaminated by a motion artifact (MA) remains a difficult problem, especially when the oximeter is not equipped with a 3-axis accelerometer for adaptive noise cancellation. In this paper, we report a pioneering investigation on the impact of altering the frame length of Molgedey and Schuster independent component analysis (ICAMS) on performance, design a multi-classifier fusion strategy for selecting the PPG correlated signal component, and propose a novel approach to extract SpO2 and HR readings from PPG signal contaminated by strong MA interference. The algorithm comprises multiple stages, including dual frame length ICAMS, a multi-classifier-based PPG correlated component selector, line spectral analysis, tree-based HR monitoring, and post-processing. Our approach is evaluated by multi-subject tests. The root mean square error (RMSE) is calculated for each trial. Three statistical metrics are selected as performance evaluation criteria: mean RMSE, median RMSE and the standard deviation (SD) of RMSE. The experimental results demonstrate that a shorter ICAMS analysis window probably results in better performance in SpO2 estimation. Notably, the designed multi-classifier signal component selector achieved satisfactory performance. The subject tests indicate that our algorithm outperforms other baseline methods regarding accuracy under most criteria. The proposed work can contribute to improving the performance of current pulse oximetry and personal wearable monitoring devices.

Evaluation and environmental correction of ambient CO2 measurements from a low-cost NDIR sensor.

Non-dispersive infrared (NDIR) sensors are a low-cost way to observe carbon dioxide concentrations in air, but their specified accuracy and precision are not sufficient for some scientific applications. An initial evaluation of six SenseAir K30 carbon dioxide NDIR sensors in a lab setting showed that without any calibration or correction, the sensors have an individual root mean square error (RMSE) between ~5 and 21 parts per million (ppm) compared to a research-grade greenhouse gas analyzer using cavity enhanced laser absorption spectroscopy. Through further evaluation, after correcting for environmental variables with coefficients determined through a multivariate linear regression analysis, the calculated difference between the each of six individual K30 NDIR sensors and the higher-precision instrument had an RMSE of between 1.7 and 4.3 ppm for 1 min data. The median RMSE improved from 9.6 for off-the-shelf sensors to 1.9 ppm after correction and calibration, demonstrating the potential to provide useful information for ambient air monitoring.

An EEMD-PCA approach to extract heart rate, respiratory rate and respiratory activity from PPG signal.

The pulse oximeter's photoplethysmographic (PPG) signals, measure the local variations of blood volume in tissues, reflecting the peripheral pulse modulated by cardiac activity, respiration and other physiological effects. Therefore, PPG can be used to extract the vital cardiorespiratory signals like heart rate (HR), respiratory rate (RR) and respiratory activity (RA) and this will reduce the number of sensors connected to the patient's body for recording vital signs. In this paper, we propose an algorithm based on ensemble empirical mode decomposition with principal component analysis (EEMD-PCA) as a novel approach to estimate HR, RR and RA simultaneously from PPG signal. To examine the performance of the proposed algorithm, we used 45 epochs of PPG, electrocardiogram (ECG) and respiratory signal extracted from the MIMIC database (Physionet ATM data bank). The ECG and capnograph based respiratory signal were used as the ground truth and several metrics such as magnitude squared coherence (MSC), correlation coefficients (CC) and root mean square (RMS) error were used to compare the performance of EEMD-PCA algorithm with most of the existing methods in the literature. Results of EEMD-PCA based extraction of HR, RR and RA from PPG signal showed that the median RMS error (quartiles) obtained for RR was 0 (0, 0.89) breaths/min, for HR was 0.62 (0.56, 0.66) beats/min and for RA the average value of MSC and CC was 0.95 and 0.89 respectively. These results illustrated that the proposed EEMD-PCA approach is more accurate in estimating HR, RR and RA than other existing methods.

Estimating instantaneous respiratory rate from the photoplethysmogram.

The photoplethysmogram (PPG) obtained from pulse oximetry shows the local changes of blood volume in tissues. Respiration induces variation in the PPG baseline due to the variation in venous blood return during each breathing cycle. We have proposed an algorithm based on the synchrosqueezing transform (SST) to estimate instantaneous respiratory rate (IRR) from the PPG. The SST is a combination of wavelet analysis and a reallocation method which aims to sharpen the time-frequency representation of the signal and can provide an accurate estimation of instantaneous frequency. In this application, the SST was applied to the PPG and IRR was detected as the predominant ridge in the respiratory band (0.1 Hz - 1 Hz) in the SST plane. The algorithm was tested against the Capnobase benchmark dataset that contains PPG, capnography, and expert labelled reference respiratory rate from 42 subjects. The IRR estimation accuracy was assessed using the root mean square (RMS) error and Bland-Altman plot. The median RMS error was 0.39 breaths/min for all subjects which ranged from the lowest error of 0.18 breaths/min to the highest error of 13.86 breaths/min. A Bland-Altman plot showed an agreement between the IRR obtained from PPG and reference respiratory rate with a bias of -0.32 and limits agreement of -7.72 to 7.07. Extracting IRR from PPG expands the functionality of pulse oximeters and provides additional diagnostic power to this non-invasive monitoring tool.

What portion of the soft tissue artefact requires compensation when estimating joint kinematics?

When joint kinematics is analyzed using noninvasive stereophotogrammetry, movements of the skin markers relative to the underlying bone are regarded as artefacts (soft tissue artefact (STA)). Recent literature suggests that an appropriate estimation of joint kinematics may be obtained by compensating for only a portion of the STA, but no evidence for this case has been reported, and which portion of the STA should be selected remains an issue. The aim of this study was to fill this gap. A modal approach was used to represent the STA. This resulted in a series of additive components (modes) and in the possibility to select a subset of them. The following STA definitions were used: individual skin marker displacement (MD), marker-cluster geometrical transformation (GT), and skin envelope shape variation (SV). An STA approximation for each of the three definitions was obtained by ordering modes on the basis of their contribution to the total STA energy and truncating the relevant series at 90% of it. A fourth approximation was obtained when the GT definition was used, by selecting the modes that represented the marker-cluster rigid transformation (i.e., three translation and three rotation modes). The different STA approximations were compared using data obtained during the stance phase of running of three volunteers carrying both pin and skin markers. The STA was measured and knee joint kinematics estimated using four skin marker datasets compensated for the above-mentioned STA approximations. Accuracy was assessed by comparing results to the reference kinematics obtained using pin markers. The different approximations resulted in different numbers of modes. For joint angles, the compensation efficiency across the STA approximations based on an energy threshold was almost equivalent. The median root mean square errors (RMSEs) were below 1 deg for flexion/extension and 2 deg for both abduction/adduction and internal/external rotation. For the joint displacements, the compensation efficiency depended on the STA approximation. Median RMSEs for anterior/posterior displacement ranged from 1 to 4 mm using either MD, GT, or SV truncated series. The RMSEs were virtually null when the STA was approximated using only the GT rigid modes. This result, together with the limited number of modes used by this approximation (i.e., three translations and three rotations of the marker-cluster), makes the STA rigid component and a good candidate for designing an STA model to be incorporated in an enhanced bone pose estimator.

River Temperature Modelling by Strahler Order at the Regional Scale in the Loire River Basin, France

Daily water temperature was simulated at a regional scale during the summer period using a simplified model based on the equilibrium temperature concept. The factors considered were heat exchanges at the water/atmosphere interface and groundwater inputs. The selected study area was the Loire River basin (110 000 km2), which displays contrasted meteorological, hydrological and geomorphological features. To capture the intra-basin variability of relevant physical factors driving the hydrological and thermal response of the system, the modelling approach combined a semi-distributed hydrological model, simulating the daily discharge at the outlet of 68 subwatersheds (drainage area between 100 and 3700 km2), and a thermal model, simulating the average daily water temperature for each Strahler order in each subwatershed. Simulations at 67 measurement stations revealed a median root mean square error (RMSE) of 1.9°C in summer between 2000 and 2006. Water temperature at stations located more than 100 km from their headwater was adequately simulated (median RMSE < 1.5°C; −0.5°C < median biases < 0.5°C). However, performance for rivers closer to their source varied because of the averaging of geomorphological and hydrological features across all the tributaries with the same Strahler order in a subwatershed, which tended to mask the specific features of the tributaries. In particular, this increased the difficulty of simulating the thermal response of groundwater-fed rivers during the hot spells of 2003. This modelling by coupling subwatershed and Strahler order for temperature simulations is less time-consuming and has proven to be extremely consistent for large rivers, where the addition of streambed inputs is adequate to describe the effect of groundwater inputs on their thermal regime. Copyright © 2015 John Wiley & Sons, Ltd.

Estimating respiratory and heart rates from the correntropy spectral density of the photoplethysmogram.

The photoplethysmogram (PPG) obtained from pulse oximetry measures local variations of blood volume in tissues, reflecting the peripheral pulse modulated by heart activity, respiration and other physiological effects. We propose an algorithm based on the correntropy spectral density (CSD) as a novel way to estimate respiratory rate (RR) and heart rate (HR) from the PPG. Time-varying CSD, a technique particularly well-suited for modulated signal patterns, is applied to the PPG. The respiratory and cardiac frequency peaks detected at extended respiratory (8 to 60 breaths/min) and cardiac (30 to 180 beats/min) frequency bands provide RR and HR estimations. The CSD-based algorithm was tested against the Capnobase benchmark dataset, a dataset from 42 subjects containing PPG and capnometric signals and expert labeled reference RR and HR. The RR and HR estimation accuracy was assessed using the unnormalized root mean square (RMS) error. We investigated two window sizes (60 and 120 s) on the Capnobase calibration dataset to explore the time resolution of the CSD-based algorithm. A longer window decreases the RR error, for 120-s windows, the median RMS error (quartiles) obtained for RR was 0.95 (0.27, 6.20) breaths/min and for HR was 0.76 (0.34, 1.45) beats/min. Our experiments show that in addition to a high degree of accuracy and robustness, the CSD facilitates simultaneous and efficient estimation of RR and HR. Providing RR every minute, expands the functionality of pulse oximeters and provides additional diagnostic power to this non-invasive monitoring tool.

Potential of SPOT Multispectral Satellite Images for Mapping Topsoil Organic Carbon Content over Peri-Urban Croplands

This study aims at identifying the potential of SPOT satellite images for predicting the topsoil soil organic carbon (SOC) content of bare cultivated soils over a large peri-urban area (221 km2) with both contrasted soils and SOC contents. Predictions were made from either field reflectance spectra, SPOT-simulated field reflectance spectra, or atmospherically corrected multispectral SPOT 2.5- and 20-m images. Field reflectance spectra were related to topsoil SOC contents by means of either partial least squares regression (PLSR) or multiple linear regression (MLR). Regression robustness was evaluated through a series of 1000 bootstrap data sets of calibration-validation samples generated among a total of 128 sampled sites. For satellite images, SOC contents were estimated from MLR bootstrap modeling on a smaller sample of pixels (∼30) that were bare soils at the time of acquisition. Field-based models obtained from SPOT-simulated spectra of regional sample sets composed of varied soils resulted in median validation root-mean-square errors (RMSE) of ∼4.6 to 4.9 g kg−1, while image-based models resulted in median validation RMSE of 4.8 g kg−1 but higher bias range and uncertainty. Postvalidation of SOC maps through an additional set of bare pixels led to RMSE values of ∼4.6 to 6.0 g kg−1. Although the resulting maps of SOC contents cannot deliver as accurate predictions as field spectra, they may enable prediction of rough classes of SOC contents with accuracies up to 60 to 70% when derived from image models, in possible agreement with the need to spatially monitor SOC classes over regional territories.

Automatic Segmentation and Online virtualCT in Head-and-Neck Adaptive Radiation Therapy

The purpose of this work was to develop and validate an efficient and automatic strategy to generate online virtual computed tomography (CT) scans for adaptive radiation therapy (ART) in head-and-neck (HN) cancer treatment. We retrospectively analyzed 20 patients, treated with intensity modulated radiation therapy (IMRT), for an HN malignancy. Different anatomical structures were considered: mandible, parotid glands, and nodal gross tumor volume (nGTV). We generated 28 virtualCT scans by means of nonrigid registration of simulation computed tomography (CTsim) and cone beam CT images (CBCTs), acquired for patient setup. We validated our approach by considering the real replanning CT (CTrepl) as ground truth. We computed the Dice coefficient (DSC), center of mass (COM) distance, and root mean square error (RMSE) between correspondent points located on the automatically segmented structures on CBCT and virtualCT. Residual deformation between CTrepl and CBCT was below one voxel. Median DSC was around 0.8 for mandible and parotid glands, but only 0.55 for nGTV, because of the fairly homogeneous surrounding soft tissues and of its small volume. Median COM distance and RMSE were comparable with image resolution. No significant correlation between RMSE and initial or final deformation was found. The analysis provides evidence that deformable image registration may contribute significantly in reducing the need of full CT-based replanning in HN radiation therapy by supporting swift and objective decision-making in clinical practice. Further work is needed to strengthen algorithm potential in nGTV localization.

Estimating particle‐size fractions of soil dominated by silicate minerals from geochemistry

SummaryMeasuring particle‐size distribution (psd) is so time‐consuming and expensive that it is rarely affordable for systematic survey. In young soil dominated by silicate minerals the size fractions have characteristic elemental compositions, and so they might be predicted from geochemical analysis. We tested the feasibility of predicting the psd from concentrations of elements in samples of topsoil taken in surveys of two large regions in eastern England and on which the psd had been determined. Of the 35 elements measured we chose eight (a) from our general knowledge of mineral composition, (b) after a principal component analysis to avoid redundancy and (c) experience elsewhere. Of the eight elements, five were used (Al, Fe, Ni, Ti and Zr) to build multiple linear regression models for the predictions. The equations were assessed by their coefficients of determination, R2, and the effectiveness of their predictions, expressed as root‐mean‐square errors (RMSEs) on validation sets of data of known psd. The models accounted on average for 89% of the variance in the clay size‐fraction and 82% for sand. The corresponding median RMSEs were 4.9% and 8.8% on medians of 17% and 58%, respectively. The silt size‐fraction was less well predicted; R2 was only 0.58, and the median RMSE was 10.6% on a median of 22%. We judge our approach, in which the regression models may be regarded as pedotransfer functions, to have been moderately successful and to merit attention in similar circumstances in other regions.