Assessment of accuracy of the structure-factor-size-estimator method in determining red blood cell aggregate size from ultrasound spectral backscatter coefficient.
Ontology highlight
ABSTRACT:
SUBMITTER: Saha RK
PROVIDER: S-EPMC3087397 | biostudies-other | 2011 Apr
REPOSITORIES: biostudies-other
Similar Datasets
Project description:Conventional medical imaging technologies, including ultrasound, have continued to improve over the years. For example, in oncology, medical imaging is characterized by high sensitivity, i.e., the ability to detect anomalous tissue features, but the ability to classify these tissue features from images often lacks specificity. As a result, a large number of biopsies of tissues with suspicious image findings are performed each year with a vast majority of these biopsies resulting in a negative finding. To improve specificity of cancer imaging, quantitative imaging techniques can play an important role. Conventional ultrasound B-mode imaging is mainly qualitative in nature. However, quantitative ultrasound (QUS) imaging can provide specific numbers related to tissue features that can increase the specificity of image findings leading to improvements in diagnostic ultrasound. QUS imaging can encompass a wide variety of techniques including spectral-based parameterization, elastography, shear wave imaging, flow estimation, and envelope statistics. Currently, spectral-based parameterization and envelope statistics are not available on most conventional clinical ultrasound machines. However, in recent years, QUS techniques involving spectral-based parameterization and envelope statistics have demonstrated success in many applications, providing additional diagnostic capabilities. Spectral-based techniques include the estimation of the backscatter coefficient (BSC), estimation of attenuation, and estimation of scatterer properties such as the correlation length associated with an effective scatterer diameter (ESD) and the effective acoustic concentration (EAC) of scatterers. Envelope statistics include the estimation of the number density of scatterers and quantification of coherent to incoherent signals produced from the tissue. Challenges for clinical application include correctly accounting for attenuation effects and transmission losses and implementation of QUS on clinical devices. Successful clinical and preclinical applications demonstrating the ability of QUS to improve medical diagnostics include characterization of the myocardium during the cardiac cycle, cancer detection, classification of solid tumors and lymph nodes, detection and quantification of fatty liver disease, and monitoring and assessment of therapy.
| S-EPMC5551399 | biostudies-other
Project description:Nanoparticles have been claimed to contribute efficiently to e.g. the mechanical strength of composite materials when present as individual particles. However, these particles tend to aggregate. In this paper we prepare nanocrystals from chitin, a product with high potential added value for application in bio-based materials, and investigate the effect of ultrasound on de-aggregation. Chitin nanocrystals with a length ~ 200 nm and a diameter ~ 15 nm, were obtained via acid hydrolysis of crude chitin powder. Freeze drying resulted in severe aggregation and after redispersion sizes up to ~ 200 µm were found. Ultrasound treatment was applied and break up behaviour was investigated using static light scattering, dynamic light scattering, and laser diffraction. Our results suggest that the cumulative energy input was the dominant factor for chitin nanocrystal aggregate breakup. When a critical energy barrier of ~ 100 kJ/g chitin nanocrystals was exceeded, the chitin nanocrystal aggregates broke down to nanometre range. The break up was mostly a result of fragmentation: the aggregation energy of chitin nanocrystal aggregates was quantified to be ~ 370 kJ/g chitin nanocrystals and we hypothesize that mainly van der Waals interactions and hydrogen bonds are responsible for aggregation.
| S-EPMC8390482 | biostudies-literature
Project description:The effect of vehicle active safety systems is subject to the friction force arising from the contact of tires and the road surface. Therefore, an adequate knowledge of the tire-road friction coefficient is of great importance to achieve a good performance of these control systems. This paper presents a tire-road friction coefficient estimation method for an advanced vehicle configuration, four-motorized-wheel electric vehicles, in which the longitudinal tire force is easily obtained. A hierarchical structure is adopted for the proposed estimation design. An upper estimator is developed based on unscented Kalman filter to estimate vehicle state information, while a hybrid estimation method is applied as the lower estimator to identify the tire-road friction coefficient using general regression neural network (GRNN) and Bayes' theorem. GRNN aims at detecting road friction coefficient under small excitations, which are the most common situations in daily driving. GRNN is able to accurately create a mapping from input parameters to the friction coefficient, avoiding storing an entire complex tire model. As for large excitations, the estimation algorithm is based on Bayes' theorem and a simplified "magic formula" tire model. The integrated estimation method is established by the combination of the above-mentioned estimators. Finally, the simulations based on a high-fidelity CarSim vehicle model are carried out on different road surfaces and driving maneuvers to verify the effectiveness of the proposed estimation method.
| S-EPMC5298280 | biostudies-literature
Project description:Presented manuscript aimed to describes enhanced near infrared spectral dataset used to improve prediction performances of near infrared models in determining quality parameters of intact mango fruits. The two mentioned quality parameters are total acidity (TA) and vitamin C which corresponds to main inner attributes of fruits. Near infrared (NIR) spectra data were acquired and recorded as absorbance spectral data in wavelength range from 1000 to 2500 nm. These data were then enhanced by means of several algorithms like multiplicative scatter correction (MSC), baseline linear correction (BLC) and combination of them (MSC+BLC). Prediction models, used to determine TA and vitamin C were established using most common approach: partial least square regression (PLS) based on raw and enhanced spectral data respectively. Prediction performances can be evaluated based on prediction accuracy and robustness, by looking statistical indicators presented as coefficient of determination (R2) and correlation (r), root mean square error (RMSE) and residual predictive deviation (RPD). Enhanced NIR spectral dataset can be employed as a rapid, effective and non-destructive method to determine inner quality parameters of intact fruits.
| S-EPMC7200245 | biostudies-literature
Project description:Focused ultrasound (FUS) therapies induce therapeutic effects in localized tissues using either temperature elevations or mechanical stresses caused by an ultrasound wave. During an FUS therapy, it is crucial to continuously monitor the position of the FUS beam in order to correct for tissue motion and keep the focus within the target region. Toward the goal of achieving real-time monitoring for FUS therapies, we have developed a method for the real-time visualization of an FUS beam using ultrasonic backscatter. The intensity field of an FUS beam was reconstructed using backscatter from an FUS pulse received by an imaging array and then overlaid onto a B-mode image captured using the same imaging array. The FUS beam visualization allows one to monitor the position and extent of the FUS beam in the context of the surrounding medium. Variations in the scattering properties of the medium were corrected in the FUS beam reconstruction by normalizing based on the echogenicity of the coaligned B-mode image. On average, normalizing by echogenicity reduced the mean square error between FUS beam reconstructions in nonhomogeneous regions of a phantom and baseline homogeneous regions by 21.61. FUS beam visualizations were achieved, using a single diagnostic imaging array as both an FUS source and an imaging probe, in a tissue-mimicking phantom and a rat tumor in vivo with a frame rate of 25-30 frames/s.
| S-EPMC8081032 | biostudies-literature
Project description:BackgroundThe intracluster correlation coefficient is a key input parameter for sample size determination in cluster-randomised trials. Sample size is very sensitive to small differences in the intracluster correlation coefficient, so it is vital to have a robust intracluster correlation coefficient estimate. This is often problematic because either a relevant intracluster correlation coefficient estimate is not available or the available estimate is imprecise due to being based on small-scale studies with low numbers of clusters. Misspecification may lead to an underpowered or inefficiently large and potentially unethical trial.MethodsWe apply a Bayesian approach to produce an intracluster correlation coefficient estimate and hence propose sample size for a planned cluster-randomised trial of the effectiveness of a systematic voiding programme for post-stroke incontinence. A Bayesian hierarchical model is used to combine intracluster correlation coefficient estimates from other relevant trials making use of the wealth of intracluster correlation coefficient information available in published research. We employ knowledge elicitation process to assess the relevance of each intracluster correlation coefficient estimate to the planned trial setting. The team of expert reviewers assigned relevance weights to each study, and each outcome within the study, hence informing parameters of Bayesian modelling. To measure the performance of experts, agreement and reliability methods were applied.ResultsThe 34 intracluster correlation coefficient estimates extracted from 16 previously published trials were combined in the Bayesian hierarchical model using aggregated relevance weights elicited from the experts. The intracluster correlation coefficients available from external sources were used to construct a posterior distribution of the targeted intracluster correlation coefficient which was summarised as a posterior median with a 95% credible interval informing researchers about the range of plausible sample size values. The estimated intracluster correlation coefficient determined a sample size of between 450 (25 clusters) and 480 (20 clusters), compared to 500-600 from a classical approach. The use of quantiles, and other parameters, from the estimated posterior distribution is illustrated and the impact on sample size described.ConclusionAccounting for uncertainty in an unknown intracluster correlation coefficient, trials can be designed with a more robust sample size. The approach presented provides the possibility of incorporating intracluster correlation coefficients from various cluster-randomised trial settings which can differ from the planned study, with the difference being accounted for in the modelling. By using expert knowledge to elicit relevance weights and synthesising the externally available intracluster correlation coefficient estimates, information is used more efficiently than in a classical approach, where the intracluster correlation coefficient estimates tend to be less robust and overly conservative. The intracluster correlation coefficient estimate constructed is likely to produce a smaller sample size on average than the conventional strategy of choosing a conservative intracluster correlation coefficient estimate. This may therefore result in substantial time and resources savings.
| S-EPMC10262340 | biostudies-literature
Project description:Many problems in genomics are related to variable selection where high-dimensional genomic data are treated as covariates. Such genomic covariates often have certain structures and can be represented as vertices of an undirected graph. Biological processes also vary as functions depending upon some biological state, such as time. High-dimensional variable selection where covariates are graph-structured and underlying model is nonparametric presents an important but largely unaddressed statistical challenge. Motivated by the problem of regression-based motif discovery, we consider the problem of variable selection for high-dimensional nonparametric varying-coefficient models and introduce a sparse structured shrinkage (SSS) estimator based on basis function expansions and a novel smoothed penalty function. We present an efficient algorithm for computing the SSS estimator. Results on model selection consistency and estimation bounds are derived. Moreover, finite-sample performances are studied via simulations, and the effects of high-dimensionality and structural information of the covariates are especially highlighted. We apply our method to motif finding problem using a yeast cell-cycle gene expression dataset and word counts in genes' promoter sequences. Our results demonstrate that the proposed method can result in better variable selection and prediction for high-dimensional regression when the underlying model is nonparametric and covariates are structured. Supplemental materials for the article are available online.
| S-EPMC3419598 | biostudies-literature
Project description:This study aims to test the hypothesis that the effect of excimer laser coronary angioplasty (ELCA) not only vaporizes thrombi and their underlying coronary plaque, it also changes their quality. We performed a series of cross-sectional analyses in 52 lesions in 51 patients before and after ELCA with integrated backscatter-intravascular ultrasound (IB-IVUS). The constituent parts of the plaque can be assessed by IB-IVUS (i.e., calcified, fibrous, lipid) according to integrated backscatter values. Minimum lumen diameter, lumen volume and vessel volume expanded after ELCA, while plaque volume did not significantly decrease. There was also a decrease of 'lipid' component (35.4-30.3%, P < 0.001) and an increase of IB-IVUS-derived 'fibrous' part (34.5-38.3%, P < 0.001). These results may help in understanding plaque change after ELCA. Excimer laser coronary angioplasty seems to contribute to the modification of coronary plaque composition in addition to debulking it.
| S-EPMC8926960 | biostudies-literature
Project description:Orbitraps are high-resolution ion-trap mass spectrometers that are widely used in metabolomics. While the mass accuracy and resolving power of orbitraps have been extensively documented, their spectral accuracy, i.e., accuracy in measuring the abundances of isotopic peaks, remains less studied. In analyzing spectra of unlabeled metabolites, we discovered a systematic under representation of heavier natural isotopic species, especially for high molecular weight metabolites (∼20% under-measurement of [M + 1]/[M + 0] ratio at m/z 600). We hypothesize that these discrepancies arise for metabolites far from the lower limit of the mass scan range, due to the weaker containment in the C-trap that results in suboptimal trajectories inside the Orbitrap analyzer. Consistent with this, spectral fidelity was restored by dividing the mass scan range (initially 75 m/z to 1000 m/z) into two scan events, one for lower molecular weight and the other for higher molecular weight metabolites. Having thus obtained accurate mass spectra at high resolution, we found that natural isotope correction for high-resolution labeling data requires more sophisticated algorithms than typically employed: the correction algorithm must take into account whether isotopologues with the same nominal mass are resolved. We present an algorithm and associated open-source code, named AccuCor, for this purpose. Together, these improvements in instrument parameters and natural isotope correction enable more accurate measurement of metabolite labeling and thus metabolic flux.
| S-EPMC5748891 | biostudies-literature
Project description:A key step toward implementing quantitative ultrasound techniques in a clinical setting is demonstrating that parameters such as the ultrasonic backscatter coefficient (BSC) can be accurately estimated independent of the clinical imaging system used. In previous studies, agreement in BSC estimates for well characterized phantoms was demonstrated across different laboratory systems. The goal of this study was to compare the BSC estimates of a tissue mimicking sample measured using four clinical scanners, each providing RF echo data in the 1-15 MHz frequency range. The sample was previously described and characterized with single-element transducer systems. Using a reference phantom for analysis, excellent quantitative agreement was observed across the four array-based imaging systems for BSC estimates. Additionally, the estimates from data acquired with the clinical systems agreed with theoretical predictions and with estimates from laboratory measurements using single-element transducers.
| S-EPMC3460978 | biostudies-other