Search PubMed⌕ Search
Home/Archive/PubMed

PubMed · 10076971

Multileaf collimator interleaf transmission.

J P Balog·T R Mackie·D L Wenman·M Glass·G Fang·D Pearson
View original source ↗Export metadata ↓

Abstract

Multileaf collimators (MLCs) have advanced past their original design purpose as a replacement for field shaping cerrobend blocks. Typically, MLCs incorporate an interlocking tongue-and-groove design between adjacent leaves to minimize leakage between leaves. They are beginning to be used to provide intensity modulation for conformal three-dimensional radiation therapy. It is possible that a critical target volume may receive an underdose due to the region of overlap if adjacent leaves are allowed to alternate between the open and closed positions, as they might if intensity modulation is employed. This work demonstrates the magnitude of that effect for a commercially available one-dimensional temporally modulated MLC. The magnitude of the transmission between leaves as a function of leaf separation was also studied, as well as the transmission as a function of leaf rotation away from the source. The results of this work were used for the design of a tomotherapy MLC. The radiation leakage considerations for a tomotherapy MLC are discussed.

Explore related subjects

Computer SimulationDose-Response Relationship, RadiationHumansMonte Carlo MethodRadiation MonitoringRadiotherapy DosageRadiotherapy, ConformalRadiotherapy, High-EnergyJournal ArticleResearch Support, Non-U.S. Gov'tResearch Support, U.S. Gov't, P.H.S.

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

J P Balog, T R Mackie, D L Wenman, M Glass, G Fang, D Pearson. 1999. Multileaf collimator interleaf transmission.. https://doi.org/10.1118/1.598501

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

KEEP EXPLORING

Related citations

PubMed2025

Generating correlated data for omics simulation.

Simulation of realistic omics data is a key input for benchmarking studies that help users obtain optimal computational pipelines. Omics data involves large numbers of measured features on each sample and these measures are generally correlated with each other. However, simulation too often ignores these correlations, perhaps due to computational and statistical hurdles of doing so. To alleviate this, we describe three approaches for generating omics-scale data with correlated measures which mimic real datasets. These approaches are all based on a Gaussian copula approach with a covariance matrix that decomposes into a diagonal part and a low-rank part. This decomposition allows for extremely efficient simulation, overcoming a hurdle for adoption of past methods. We use these approaches to demonstrate the importance of including correlation in two benchmarking applications. First, we show that variance of results from the popular DESeq2 method increases when dependence is included. Second, we demonstrate that CYCLOPS, a method for inferring circadian time of collection from transcriptomics, improves in performance when given gene-gene dependencies in some circumstances. We provide an R package, dependentsimr, that has efficient implementations of these methods and can generate dependent data with arbitrary marginal distributions, including discrete (binary, ordered categorical, Poisson, negative binomial), continuous (normal), or with an empirical distribution.

Computer Simulation↗
PubMed2017

Addressing current challenges in cancer immunotherapy with mathematical and computational modelling.

The goal of cancer immunotherapy is to boost a patient's immune response to a tumour. Yet, the design of an effective immunotherapy is complicated by various factors, including a potentially immunosuppressive tumour microenvironment, immune-modulating effects of conventional treatments and therapy-related toxicities. These complexities can be incorporated into mathematical and computational models of cancer immunotherapy that can then be used to aid in rational therapy design. In this review, we survey modelling approaches under the umbrella of the major challenges facing immunotherapy development, which encompass tumour classification, optimal treatment scheduling and combination therapy design. Although overlapping, each challenge has presented unique opportunities for modellers to make contributions using analytical and numerical analysis of model outcomes, as well as optimization algorithms. We discuss several examples of models that have grown in complexity as more biological information has become available, showcasing how model development is a dynamic process interlinked with the rapid advances in tumour-immune biology. We conclude the review with recommendations for modellers both with respect to methodology and biological direction that might help keep modellers at the forefront of cancer immunotherapy development.

Computer Simulation↗
PubMed2001

Quantification of the hydrophobic interaction by simulations of the aggregation of small hydrophobic solutes in water.

The hydrophobic interaction, the tendency for nonpolar molecules to aggregate in solution, is a major driving force in biology. In a direct approach to the physical basis of the hydrophobic effect, nanosecond molecular dynamics simulations were performed on increasing numbers of hydrocarbon solute molecules in water-filled boxes of different sizes. The intermittent formation of solute clusters gives a free energy that is proportional to the loss in exposed molecular surface area with a constant of proportionality of 45 +/- 6 cal/mol A(2). The molecular surface area is the envelope of the solute cluster that is impenetrable by solvent and is somewhat smaller than the more traditional solvent-accessible surface area, which is the area transcribed by the radius of a solvent molecule rolled over the surface of the cluster. When we apply a factor relating molecular surface area to solvent-accessible surface area, we obtain 24 cal/mol A(2). Ours is the first direct calculation, to our knowledge, of the hydrophobic interaction from molecular dynamics simulations; the excellent qualitative and quantitative agreement with experiment proves that simple van der Waals interactions and atomic point-charge electrostatics account for the most important driving force in biology.

Computer Simulation↗