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At least 271 records · Page 15Linked to original sources
PubMed2001

Impact of simple tissue inhomogeneity correction algorithms on conformal radiotherapy of lung tumours.

BACKGROUND AND PURPOSE: Conformal radiotherapy requires accurate dose calculation at the dose specification point, at other points in the planning target volume (PTV) and in organs at risk. To assess the limitations of treatment planning of lung tumours, errors in dose values, calculated by some simple tissue inhomogeneity correction algorithms available in a number of currently applied treatment planning systems, have been quantified. MATERIALS AND METHODS: Single multileaf collimator-shaped photon beams of 6, 8, 15 and 18 MV nominal energy were used to irradiate a 50 mm diameter spherical solid tumour, simulated by polystyrene, which was located centrally inside lung tissue, simulated by cork. The planned dose distribution was made conformal to the PTV, which was a 15 mm three-dimensional expansion of the tumour. Values of both the absolute dose at the International Commission on Radiation Units and Measurement (ICRU) reference point and relative dose distributions inside the PTV and in the lung were calculated using three inhomogeneity correction algorithms. The algorithms investigated in this study are the pencil beam algorithm with one-dimensional corrections, the modified Batho algorithm and the equivalent path length algorithm. The calculated data were compared with measurements for a simple beam set-up using radiographic film and ionization chambers. RESULTS: For this specific configuration, deviations of up to 3.5% between calculated and measured values of the dose at the ICRU reference point were found. Discrepancies between measured and calculated beam fringe values (distance between the 50 and 90% isodose lines) of up to 14 mm have been observed. The differences in beam fringe and penumbra width (20-80%) increase with increasing beam energy. Our results demonstrate that an underdosage of the PTV up to 20% may occur if calculated dose values are used for treatment planning. The three algorithms predict a considerably higher dose in the lung, both along the central beam axis and in the lateral direction, compared with the actual delivered dose values. CONCLUSIONS: The dose at the ICRU reference point of such a tumour in lung geometry is calculated with acceptable accuracy. Differences between calculated and measured dose distributions are primarily due to changes in electron transport in the lung, which are not adequately taken into account by the simple tissue inhomogeneity correction algorithms investigated in this study. Particularly for high photon beam energies, clinically unacceptable errors will be introduced in the choice of field sizes employed for conformal treatments, leading to underdosage of the PTV. In addition, the dose to the lung will be wrongly predicted which may influence the choice of the prescribed dose level in dose-escalation studies.

Algorithms↗
PubMed2002

Evaluation of dose calculation algorithms for dynamic arc treatments of head and neck tumors.

BACKGROUND AND PURPOSE: To investigate if the Pencil Beam (PB) algorithm takes the disturbance of the dose distribution due to tissue inhomogeneities sufficiently into account in dynamic field shaping rotation therapy (called the dynamic arc treatment modality) for fractionated stereotactic radiation therapy of head and neck tumors. MATERIAL AND METHODS: A treatment plan using the dynamic arc treatment modality of an oropharynx lesion on a humanoid phantom was evaluated. The same plan was calculated with three different calculation algorithms: the Clarkson and the PB algorithm (both available on the planning system of the Novalis system used for dynamic arc treatments), and the Collapsed Cone Convolution Superposition (CC) algorithm (used by the Pinnacle planning system). The three resulting plans are compared using isodose distributions and cumulative dose volume histograms (CDVHs). An intercomparison of the results of the three algorithms was performed to investigate how accurately each of them takes the influence of tissue inhomogeneities into account such as bony structures and air cavities often appearing in the head and neck region. Additionally, the resulting plans were compared with absolute and relative dosimetric measurements of the treatment plan on the humanoid phantom with thermoluminescent detectors and radiographic film, respectively. RESULTS: All calculated dose distributions show a good agreement with the measured distribution except in the planning target volume (PTV) in and at the border of the air cavity. All three algorithms overestimate the dose in the PTV at the boundary with the low-density tissue, with 12, 10 and 7% for the Clarkson, the PB and the CC algorithm, respectively. The correspondence between the calculated dose distributions is reflected in the graphs of the CDVHs. They show similar curves for the PTV and the structures except for the left parotic gland and the myelum. CONCLUSIONS: The PB algorithm of the Novalis system calculates a treatment plan for the dynamic arc treatment modality adequately for fractionated stereotactic radiation therapy of head and neck tumors, except in the PTV in and at the border of the air cavity where the actual dose is overestimated. Care needs to be taken in clinical cases where it is critical to irradiate the air-tissue boundary to a sufficient dose.

Algorithms↗
PubMed2000

The genetic algorithm applied to haplotype data at the LDL receptor locus.

Conventional statistical methods based upon single restriction fragment length polymorphisms often prove inadequate in studies of genetic variation. Cladistic analysis has been suggested as an alternative, but requires basic assumptions that usually cannot be met. We wanted to test whether it could be a workable approach to apply the genetic algorithm, an artificial intelligence method, to haplotype data. The genetic algorithm creates in-computer artificial 'individuals', all having 'genes' coding for solutions to a problem. The individuals are allowed to compete and 'mate', individuals with genes coding for better solutions mating more often. Genes coding for good solutions survive through generations of the genetic algorithm. At the end of the run, the best solutions can be extracted. We applied the genetic algorithm to data consisting of cholesterol values and haplotypes made up of seven restriction sites at the LDL receptor locus. The persons included were 114 FH (familial hypercholesterolemia) patients and 61 normals. The genetic algorithm found the restriction sites 1 (Sph1 in intron 6), 2 (StuI in exon 8), and 7 (ApaLI site in the 3' flanking region) were associated with high cholesterol levels. As a validity check we used runs of the genetic algorithm applied to 'artificial patients', i.e. artificially generated haplotypes linked to artificially generated cholesterol values. This demonstrated the genetic algorithm consistently found the appropriate haplotype. We conclude that the genetic algorithm may be a useful tool for studying genetic variation.

Algorithms↗
PubMed1997

Improved common line-based icosahedral particle image orientation estimation algorithms.

Modifications are described for the center and angular parameter estimation algorithms of common line-based particle image orientation determination which is an essential step in the three-dimensional reconstruction of icosahedral virus particles. The modifications incorporate a variety of image processing, pattern recognition, and statistical tools resulting in objective and automated orientation estimation algorithms. The modified algorithms were tested using electron cryo-microscopic particle images of three different virus specimens, with sizes 400-1250 A in diameter, covering a broad range of defocus values. Evaluation of these modified algorithms shows significant improvement over the previous algorithms. The center and angular parameters were estimated with higher accuracy allowing the identification of a larger number of particle orientations. Usage of the modified estimation algorithms resulted in the identification of particle orientations which could not to be identified using the algorithms before modification. Furthermore, these improvements have resulted in the determination of a better quality and a higher resolution three-dimensional reconstruction. The improved algorithms have been developed into a software package which can be obtained via the world wide web at http://ncmi.bioch.bcm.tmc.edu/pthuman.

Algorithms↗
PubMed1998

A commercial IMRT treatment-planning dose-calculation algorithm.

PURPOSE: The dose-calculation algorithm for a commercial arc-based intensity modulated radiation therapy (IMRT) treatment-planning and delivery system (Peacock, NOMOS Corporation) is described. METHODS AND MATERIALS: The IMRT delivery system uses a dynamically controlled multileaf collimator with 40 leaves that project on our accelerator to either 1.0 x 0.84 cm2 or 1.0 x 1.68 cm2 at isocenter arranged in two banks of 20 leaves each. The dose-calculation algorithm uses tissue-phantom ratios derived from percent depth dose measurements, measured relative output data, and single leaf profiles. Some compromises are made in the algorithm terms to enable more straightforward dosimetry measurements and to reduce dose computation times. The dose calculation algorithm is presented, and consequences of the approximations are investigated using previously published 4 MV photon beam data. RESULTS: Most of the approximations lead to dose errors of a few percent. However, the use of depth-invariant single-leaf profiles results in errors as large as 9% for 4 MV fixed beams. CONCLUSIONS: Large dosimetric errors are possible for small fixed fields using this algorithm. However, the algorithm is designed for tomotherapy dose delivery, where doses are delivered from multiple directions and depths. Investigations of the algorithm in more clinically relevant conditions have been conducted and show that the algorithm accuracy is 1.3% and therefore is clinically acceptable for tomotherapy.

Algorithms↗
PubMed1997

Evaluation of algorithms for the diagnosis of pulmonary embolism.

The development of new diagnostic tests for diagnosing pulmonary embolism (PE) has not yet resulted in any single algorithm being universally accepted. It is at least partially due to the perception by the clinicians ordering the tests that insufficient weight is given to the actual mortality and morbidity costs of pulmonary angiography in comparison to those associated with PE and its treatment. It also becomes more difficult to intuitively integrate all of the competing factors (e.g., sensitivity, specificity, costs, morbidity, mortality) for the different algorithms to choose the most cost-effective sequence. We evaluated the algorithm recommended by the Prospective Investigation of Pulmonary Embolism Diagnosis (PIOPED) investigators, designated by V, and compared it to pulmonary arteriography (A), MR angiography (M), and CT angiography (C) which directly visualize the pulmonary arteries. Using standard economic approaches to loss of life and morbidity to determine the possible costs, we compared the different algorithms for all possible prevalence values. The sensitivity and specificity rates used in making these comparisons were the average values reported in the literature. We found that the recommended algorithm, V, had the lowest cost, provided that economically reasonable morbidity and mortality costs were used. It seems that this algorithm provides sufficient sensitivity and specificity to compensate for the risks of angiography as compared with the other algorithms that avoid these risks. Neither M nor C can compete with this standard algorithm despite their lower mortality and morbidity costs, and the costs of A alone are too high. In the future, however, the inclusion of venous studies with M and/or C may improve the results sufficiently to become more cost-effective than V.

Algorithms↗
PubMedDate not supplied

A new algorithm for learning in piecewise-linear neural networks.

Piecewise-linear (PWL) neural networks are widely known for their amenability to digital implementation. This paper presents a new algorithm for learning in PWL networks consisting of a single hidden layer. The approach adopted is based upon constructing a continuous PWL error function and developing an efficient algorithm to minimize it. The algorithm consists of two basic stages in searching the weight space. The first stage of the optimization algorithm is used to locate a point in the weight space representing the intersection of N linearly independent hyperplanes, with N being the number of weights in the network. The second stage is then called to use this point as a starting point in order to continue searching by moving along the single-dimension boundaries between the different linear regions of the error function, hopping from one point (representing the intersection of N hyperplanes) to another. The proposed algorithm exhibits significantly accelerated convergence, as compared to standard algorithms such as back-propagation and improved versions of it, such as the conjugate gradient algorithm. In addition, it has the distinct advantage that there are no parameters to adjust, and therefore there is no time-consuming parameters tuning step. The new algorithm is expected to find applications in function approximation, time series prediction and binary classification problems.

Algorithms↗
PubMed2004

Use of genetic algorithms for neural networks to predict community-acquired pneumonia.

BACKGROUND: Genetic algorithms have been used to solve optimization problems for artificial neural networks (ANN) in several domains. We used genetic algorithms to search for optimal hidden-layer architectures, connectivity, and training parameters for ANN for predicting community-acquired pneumonia among patients with respiratory complaints. METHODS: Feed-forward back-propagation ANN were trained on sociodemographic, symptom, sign, comorbidity, and radiographic outcome data among 1044 patients from the University of Illinois (the training cohort), and were applied to 116 patients from the University of Nebraska (the testing cohort). Binary chromosomes with genes representing network attributes, including the number of nodes in the hidden layers, learning rate and momentum parameters, and the presence or absence of implicit within-layer connectivity using a competition algorithm, were operated on by various combinations of crossover, mutation, and probabilistic selection based on network mean-square error (MSE), and separately on average cross entropy (ENT). Predictive accuracy was measured as the area under a receiver-operating characteristic (ROC) curve. RESULTS: Over 50 generations, the baseline genetic algorithm evolved an optimized ANN with nine nodes in the first hidden layer, zero nodes in the second hidden layer, learning rate and momentum parameters of 0.5, and no within-layer competition connectivity. This ANN had an ROC area in the training cohort of 0.872 and in the testing cohort of 0.934 (P-value for difference, 0.181). Algorithms based on cross-generational selection, Gray coding of genes prior to mutation, and crossover recombination at different genetic levels, evolved optimized ANN identical to the baseline genetic strategy. Algorithms based on other strategies, including elite selection within generations (training ROC area 0.819), and inversions of genetic material during recombination (training ROC area 0.812), evolved less accurate ANN. CONCLUSION: ANN optimized by genetic algorithms accurately discriminated pneumonia within a training cohort, and within a testing cohort consisting of cases on which the networks had not been trained. Genetic algorithms can be used to implement efficient search strategies for optimal ANN to predict pneumonia.

Adolescent↗
PubMed1999

Branch-and-terminate: a combinatorial optimization algorithm for protein design.

BACKGROUND: Several deterministic and stochastic combinatorial optimization algorithms have been applied to computational protein design and homology modeling. As structural targets increase in size, however, it has become necessary to find more powerful methods to address the increased combinatorial complexity. RESULTS: We present a new deterministic combinatorial search algorithm called 'Branch-and-Terminate' (B&T), which is derived from the Branch-and-Bound search method. The B&T approach is based on the construction of an efficient but very restrictive bounding expression, which is used for the search of a combinatorial tree representing the protein system. The bounding expression is used both to determine the optimal organization of the tree and to perform a highly effective pruning procedure named 'termination'. For some calculations, the B&T method rivals the current deterministic standard, dead-end elimination (DEE), sometimes finding the solution up to 21 times faster. A more significant feature of the B&T algorithm is that it can provide an efficient way to complete the optimization of problems that have been partially reduced by a DEE algorithm. CONCLUSIONS: The B&T algorithm is an effective optimization algorithm when used alone. Moreover, it can increase the problem size limit of amino acid sidechain placement calculations, such as protein design, by completing DEE optimizations that reach a point at which the DEE criteria become inefficient. Together the two algorithms make it possible to find solutions to problems that are intractable by either algorithm alone.

Algorithms↗
PubMed1999

Interpolation algorithms for digital mammography systems with multiple detectors.

RATIONALE AND OBJECTIVES: In some full-field digital mammography systems, multiple detectors are abutted together, and the physical gaps between adjacent detectors produce seams between the resultant subimages. In this study, a variety of interpolation algorithms for estimating the missing information in the seams were compared, and their effect on image quality was evaluated. MATERIALS AND METHODS: Eight representative interpolation algorithms were selected, including nearest neighbor, one-dimensional and two-dimensional weighting, mean value, one-dimensional and two-dimensional polynomial, and one-dimensional and two-dimensional cubic spline interpolation methods. These methods were applied to digital mammograms and phantom images. The effectiveness of each algorithm was evaluated for accuracy and geometric distortion. RESULTS: These interpolation algorithms offered similar accuracy in estimating missing image information. The weighting, polynomial, and cubic spline interpolation algorithms introduced less geometric distortion than the nearest neighbor and mean value interpolation algorithms. All algorithms were more effective in estimating larger, lower-contrast features (such as breast masses) than in estimating smaller, higher-contrast features (such as breast microcalcifications). Small microcalcifications within the seams cannot be recovered with interpolation. The probability of a microcalcification in a seam is small, however, and the failure to image a few microcalcifications of a cluster generally does not substantially alter diagnostic performance. CONCLUSION: In the development of full-field digital breast imaging systems, appropriate interpolation algorithms can satisfactorily fill in narrow gaps between adjacent detectors. The one-dimensional weighting interpolation method seems an effective and efficient choice.

Algorithms↗
PubMed2004

A novel algorithm for finding interspersed repeat regions.

The analysis of repeats in the DNA sequences is an important subject in bioinformatics. In this paper, we propose a novel projection-assemble algorithm to find unknown interspersed repeats in DNA sequences. The algorithm employs random projection algorithm to obtain a candidate fragment set, and exhaustive search algorithm to search each pair of fragments from the candidate fragment set to find potential linkage, and then assemble them together. The complexity of our projection-assemble algorithm is nearly linear to the length of the genome sequence, and its memory usage is limited by the hardware. We tested our algorithm with both simulated data and real biology data, and the results show that our projection-assemble algorithm is efficient. By means of this algorithm, we found an un-labeled repeat region that occurs five times in Escherichia coli genome, with its length more than 5,000 bp, and a mismatch probability less than 4%.

Algorithms↗
PubMed2006

Application of heuristic optimization techniques and algorithm tuning to multilayered sorptive barrier design.

Although heuristic optimization techniques are increasingly applied in environmental engineering applications, algorithm selection and configuration are often approached in an ad hoc fashion. In this study, the design of a multilayer sorptive barrier system served as a benchmark problem for evaluating several algorithm-tuning procedures, as applied to three global optimization techniques (genetic algorithms, simulated annealing, and particle swarm optimization). Each design problem was configured as a combinatorial optimization in which sorptive materials were selected for inclusion in a landfill liner to minimize the transport of three common organic contaminants. Relative to multilayer sorptive barrier design, study results indicate (i) the binary-coded genetic algorithm is highly efficient and requires minimal tuning, (ii) constraint violations must be carefully integrated to avoid poor algorithm convergence, and (iii) search algorithm performance is strongly influenced by the physical-chemical properties of the organic contaminants of concern. More generally, the results suggest that formal algorithm tuning, which has not been widely applied to environmental engineering optimization, can significantly improve algorithm performance and provide insight into the physical processes that control environmental systems.

Algorithms↗
PubMed2000

Influence of autothreshold sensing and sinus rate on mode switching algorithm behavior.

Mode switching is beneficial to pacemaker patients with paroxysmal atrial tachyarrhythmias. However, the optimal mode switching algorithm is still in evolution. Mode switching algorithms and atrial sensing circuitry can influence mode switching behavior. This study compared the mode switching behavior of four Medtronic, Inc. implantable devices: Thera DR model 7960 pacemaker, Kappa 700 model KDR701 pacemaker, Gem DR model 7271 dual chamber pacing defibrillator, and Jewel AF model 7250 dual chamber pacemaker atrial and ventricular defibrillator. The Thera and Gem DR use the same mean atrial rate mode switch algorithm. The Kappa and Jewel AF use four of seven short atrial intervals and an atrial fibrillation evidence counter algorithm, respectively. The Thera and Kappa devices use fixed gain sensing and the Gem DR and Jewel AF use autothreshold atrial sensing. Digitally recorded atrial electrograms from 52 episodes of human atrial fibrillation were fed into each device with differing simulated sinus rates before and after the atrial fibrillation. The percent of appropriate mode switching was highest for the Kappa 700 (94%) and lowest for the Thera (85%) (P = 0.046). The time to mode switching was significantly longer for the Thera and Gem DR compared to the Kappa 700 or Jewel AF (all P < 0.05). The time to mode switching was shorter for the Gem DR (9.0 +/- 1.6 s) using autothreshold atrial sensing than for the fixed gain Thera (11.1 +/- 2.1 s, P < 0.05). The mean atrial electrogram amplitude and cycle length were not correlated with the time to mode switching for any device. Faster sinus rates shortened the time to mode switching and prolonged the time to resynchronization in the two devices using the mean atrial interval algorithm. In conclusion, (1) mode switching function among these devices is influenced by algorithms and sensing circuitry, (2) the time to mode switching among these devices is influenced by the algorithm and use of autothreshold atrial sensing, and (3) the sinus rate before and after episodes of atrial fibrillation greatly influences the times to mode switching and resynchronization in devices using the mean atrial interval algorithm.

Algorithms↗
PubMed2001

Comparison of two diagnostic algorithms for regular broad complex tachycardia by decision theory analysis.

Sensitivity and specificity are two inversely related properties of a diagnostic test and it is often practically infeasible to secure a high value for both simultaneously. Decision theory analysis shows that the utility of a diagnostic test depends not only on its sensitivity and specificity but also on the prevalence of the intended target disorder: when prevalence is low, a high specificity is more important than a high sensitivity, whereas when prevalence is high, a high sensitivity is more important than a high specificity. The significance of this principle is illustrated by two popular algorithms for the electrocardiographic diagnosis of regular broad complex tachycardia (BCT), of which the two main differential diagnoses are ventricular tachycardia (VT) and supraventricular tachycardia with aberrant conduction (SVTAG). Brugada et al. focused on criteria highly specific for VT and used them to build a four-step algorithm. In contrast, Griffith et al. first selected criteria highly sensitive for VT and then criteria highly specific for VT to build a simple two-step algorithm. It can be objectively demonstrated that the Griffith algorithm is more efficient and effective than the Brugada algorithm in terms of clinching the final diagnosis and improving overall diagnostic accuracy. The main reason for this is that VT is more common than SVTAC as the cause of regular BCT, and the Griffith algorithm adhered to the aforementioned principle governing the choice between sensitivity and specificity according to prevalence in its design. The Griffith algorithm also embodies an additional important principle, namely, it is easier and more efficient to choose alternatively between criteria highly specific and highly sensitive for the intended target disorder than concentrating on just one or the other in designing a multiple-step sequential diagnostic algorithm.

Algorithms↗
PubMed2006

A tunable algorithm for collective decision-making.

Complex biological systems are increasingly understood in terms of the algorithms that guide the behavior of system components and the information pathways that link them. Much attention has been given to robust algorithms, or those that allow a system to maintain its functions in the face of internal or external perturbations. At the same time, environmental variation imposes a complementary need for algorithm versatility, or the ability to alter system function adaptively as external circumstances change. An important goal of systems biology is thus the identification of biological algorithms that can meet multiple challenges rather than being narrowly specified to particular problems. Here we show that emigrating colonies of the ant Temnothorax curvispinosus tune the parameters of a single decision algorithm to respond adaptively to two distinct problems: rapid abandonment of their old nest in a crisis and deliberative selection of the best available new home when their old nest is still intact. The algorithm uses a stepwise commitment scheme and a quorum rule to integrate information gathered by numerous individual ants visiting several candidate homes. By varying the rates at which they search for and accept these candidates, the ants yield a colony-level response that adaptively emphasizes either speed or accuracy. We propose such general but tunable algorithms as a design feature of complex systems, each algorithm providing elegant solutions to a wide range of problems.

Algorithms↗
PubMed1989

Construction of clinical algorithms for educational programs.

Clinical algorithms have been used successfully in a variety of health care settings to assist health care professionals in the diagnosis and management of medical problems. In addition to their clinical applications, algorithms also serve as an instructional resource by themselves and when used in conjunction with other educational methodologies. A recommended algorithm development process is described for cancer educators who wish to take advantage of the unique contribution clinical algorithms can offer for their educational programs. Algorithm design conventions are reviewed and specific writing suggestions are offered for the guidance of educators who want to design their own clinical algorithms. Objections to clinical algorithms can often be attributed to a misunderstanding of their proper role, which is to facilitate, not dictate, the decision process and guide the application of management logic. Clinical algorithms are a valuable instructional resource that can be used in a wide range of educational settings from self-instruction units to the design of lecture presentations.

Algorithms↗
PubMed1993

A comparison of colour flow imaging algorithms.

In this paper we will present and explain coherently the two main algorithms which are currently used in commercial colour flow imaging systems, namely the frequency domain based autocorrelation algorithm and the time domain based cross correlation algorithm. The autocorrelation algorithm is a robust optimum mean frequency baseband estimator, for both high and low signal to noise ratio (SNR). Its main disadvantage is its aliasing problem. However the cross correlation algorithm does not suffer from this problem, and this has been one of the main reasons for its development. The mathematics behind these two algorithms and the general data processing steps used for their implementation will be also presented. The subject of clutter removal filtering will be discussed and valuable points will be made. A practical relevant approach on how to discriminate between wanted blood flow related estimates and unwanted false estimates due to noise will be presented. On the aliasing problem of the autocorrelation algorithm, a promising solution known as spatial tracking of estimated mean frequency does exist, which will be also introduced in detail. Towards the end of this paper other algorithms, which are currently the subject of research and have potential for development, will be discussed.

Algorithms↗
PubMed1994

A comparison of three inverse treatment planning algorithms.

Three published inverse treatment planning algorithms for physical optimization of external beam radiotherapy are compared. All three algorithms attempt to minimize a quadratic objective function of the dose distribution. It is shown that the algorithms are based on the common framework of Newton's method of multi-dimensional function minimization. The approximations used within this framework to obtain the different algorithms are described. The use of these algorithms requires that the number of weights of elemental dose distributions be equal to the number of sample points taken in the dose volume. The primary factor in determining how the algorithms are implemented is the dose computation model. Two of the algorithms use pencil beam dose models and therefore directly optimize individual pencil beam weights, whereas the third algorithm is implemented to optimize groups of pencil beams, each group converging upon a common point. All dose computation models assume that the irradiated medium is homogeneous. It is shown that the two different implementations produce similar results for the simple optimization problem of conforming dose to a convex target shape. Complex optimization problems consisting of non-convex target shapes and dose limiting structures are shown to require a pencil beam optimization method.

Algorithms↗