Search PubMed⌕ Search

PubMed · 9952081

Computing the mechanical index.

Abstract

A computational nonlinear beam propagation model was used to compute the water path and in situ fields of a phased array transducer operating at 2 MHz. The computational source was matched to the transducer's z = 10 cm focal plane field. Subsequent computed propagations considered this source operating at source amplitudes up to 1.49 MPa in a water medium and in a tissue medium with an attenuation of 0.3 dB cm(-1) MHz(-1). The mechanical index was calculated in three ways based on these computations: extrapolated from one low amplitude water path propagation, extrapolated from a series of water path propagations using the existing Output Display Standard protocol, and directly from a series of tissue path propagations. These computed results suggest that extrapolation from derated measurements of a low level water path field can provide mechanical index estimates which progressively overestimate the in situ values. At the highest source amplitude considered, the linearly extrapolated mechanical index was 29% higher than the mechanical index computed by the tissue path propagations. The Output Display Standard protocol offered improved accuracy but consistently underestimated the in situ values. The maximum error for the Output Display Standard protocol was 8%. A variation of the Output Display Standard protocol in which mechanical index estimates were obtained from the on-axis spatial peak in the derated temporal peak rarefactional curve was also considered. The maximum error for this method was 3%. The results considered here also demonstrated the feasibility of computational investigations of high intensity clinical propagations.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

T Christopher. 1999. Computing the mechanical index.. https://doi.org/10.7863/jum.1999.18.1.63

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

A robust transfer learning approach for high-dimensional linear regression to support integration of multi-source gene expression data.

Transfer learning aims to integrate useful information from multi-source datasets to improve the learning performance of target data. This can be effectively applied in genomics when we learn the gene associations in a target tissue, and data from other tissues can be integrated. However, heavy-tail distribution and outliers are common in genomics data, which poses challenges to the effectiveness of current transfer learning approaches. In this paper, we study the transfer learning problem under high-dimensional linear models with t-distributed error (Trans-PtLR), which aims to improve the estimation and prediction of target data by borrowing information from useful source data and offering robustness to accommodate complex data with heavy tails and outliers. In the oracle case with known transferable source datasets, a transfer learning algorithm based on penalized maximum likelihood and expectation-maximization algorithm is established. To avoid including non-informative sources, we propose to select the transferable sources based on cross-validation. Extensive simulation experiments as well as an application demonstrate that Trans-PtLR demonstrates robustness and better performance of estimation and prediction when heavy-tail and outliers exist compared to transfer learning for linear regression model with normal error distribution. Data integration, Variable selection, T distribution, Expectation maximization algorithm, Genotype-Tissue Expression, Cross validation.

Linear Models↗

Noncompartmentally-based pharmacokinetic modeling.

OBJECTIVE: To present an overview of noncompartmentally-based modeling which is a modeling that makes use of systems analysis, predominantly linear systems analysis (LSA). FINDINGS: Fundamental elements of LSA presented from a linear operational viewpoint have a sound foundation in molecular stochastic independence (MSI). Powerful LSA procedures based on MSI presented such as convolution, deconvolution and disposition decomposition analysis (DDA) enable PK predictions and evaluations of drug input and delivery using models with simple general structures and few verifiable assumptions. DDA nonparametrically differentiates the unit impulse response (UIR) into generalized elimination and distribution functions. DDA applied in a linear and nonlinear context is central to many LSA procedures such as analytically exact direct deconvolution, nonparametric evaluation of drug elimination and distribution, steady state predictions, evaluation of mean time parameters for drug delivery and disposition (mean residence time, mean transit time, mean arrival time), relative tissue affinity (residence time coefficients), nonparametric exact clearance correction of UIR, and time variant convolution and deconvolution. The general response mapping operation procedure of LSA presented provides a powerful rational alternative to problematic structured modeling of multivariate PK systems. CONCLUSION: The wide arsenal of underutilized LSA-based kinetic analysis tools provide a rational, powerful alternative to traditional kinetic modeling.

Linear Models↗

Chain length dependence of lipid partitioning between the air/water interface and its subphase: thermodynamic and structural implications.

We have investigated phosphatidylcholines with the same two saturated hydrocarbon chains of 12, 10 and 8 C-atoms. Langmuir trough data could be evaluated towards even small lipid subphase desorption when applying a novel approach that had recently been developed in our laboratory. The C12 lipid turned out to form a nearly insoluble monolayer with slight desorption only beyond 15 mN/m for an area/volume ratio around 1 cm(-1). Above 22 mN/m micellation in the subphase apparently terminates further accumulation in the interface forcing additionally added lipid to enter the bulk volume. A comparatively substantial increase of solubilization was observed for the C10 monolayer. When turning to the C8 lipid partitioning proved to take place in nearly equal parts. In that case, strong multimeric aggregation is indicated to occur in both the interfacial and the bulk volume domains. All the results are quantitatively discussed in the light of basic thermodynamic and structural considerations.

Linear Models↗