Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Algorithms”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 127 records · Page 7Linked to original sources
PubMed1995

Is consensus reproducible? A study of an algorithmic guidelines development process.

The authors evaluated the reproducibility of a clinical algorithm consensus development process across three different physician panels at a health maintenance organization. Physician groups were composed of primary care internists, who were provided with identical selections from the medical literature and first-draft "seed" algorithms on the management of two common clinical problems: acute sinusitis and dyspepsia. Each panel used nominal group process and a modified Delphi method to create final algorithm drafts. To compare the clinical logic in the final algorithms, the authors applied a new qualitative and quantitative comparison method, the Clinical Algorithm Patient Abstraction (CAPA). Dyspepsia algorithms from all physician groups recommended empiric anti-acid therapy for most patients, favored endoscopy over barium swallow, and had very similar indications for endoscopy. The average CAPA comparison score among final physician algorithms was 6.1 on a scale of 0 (different) to 10 (identical). Sinusitis algorithms from all groups proposed empiric antibiotic therapy for most patients. Indications for sinus radiographs were similar between two algorithms (CAPA = 4.9), but differed significantly in the third, resulting in lower CAPA scores (average CAPA = 1.9, P < 0.03). The clinical similarity of the algorithms produced by these physician panels suggests a high level of reproducibility in this consensus-driven algorithm development process. However, the difference among the sinusitis algorithms suggests that physician consensus groups using a consensus process that a health maintenance organization can do with limited resources will produce some guidelines that vary due to differences in interpretation of evidence and physician experience.

Acute Disease↗
PubMed1999

Measuring costs of guideline-driven mental health care: the Texas Medication Algorithm Project.

BACKGROUND: Algorithms describe clinical choices to treat a specific disorder. To many, algorithms serve as important tools helping practitioners make informed choices about how best to treat patients, achieving better outcomes more quickly and at a lower cost. Appearing as flow charts and decision trees, algorithms are developed during consensus conferences by leading experts who explore the latest scientific evidence to describe optimal treatment for each disorder. Despite a focus on "optimal" care, there has been little discussion in the literature concerning how costs should be defined and measured in the context of algorithm-based practices. AIMS OF THE STUDY: This paper describes the strategy to measure costs for the Texas Medication Algorithm project, or TMAP. Launched by the Texas Department of Mental Health and Mental Retardation and the University of Texas Southwestern Medical Center at Dallas, this multi-site study investigates outcomes and costs of medication algorithms for bipolar disorder, schizophrenia and depression. METHODS: To balance costs with outcomes, we turned to cost-effectiveness analyses as a framework to define and measure costs. Alternative strategies (cost-benefit, cost-utility, cost-of-illness) were inappropriate since algorithms are not intended to guide resource allocation across different diseases or between health- and non-health-related commodities. "Costs" are operationalized consistent with the framework presented by the United States Public Health Service Panel on Cost Effectiveness in Medicine. Patient specific costs are calculated by multiplying patient units of use by a unit cost, and summing over all service categories. Outpatient services are counted by procedures. Inpatient services are counted by days classified into diagnosis groups. Utilization information is derived from patient self-reports, medical charts and administrative file sources. Unit costs are computed by payer source. Finally, hierarchical modeling is used to describe how costs and effectiveness differ between algorithm-based and treatment-as-usual practices. DISCUSSION: Cost estimates of algorithm-based practices should (i) measure opportunity costs, (ii) employ structured data collection methods, (iii) profile patient use of both mental health and general medical providers and (iv) reflect costs by payer status in different economic environments. IMPLICATION FOR HEALTH CARE PROVISION AND USE: Algorithms may help guide clinicians, their patients and third party payers to rely on the latest scientific evidence to make treatment choices that balance costs with outcomes. IMPLICATION FOR HEALTH POLICIES: Planners should consider consumer wants and economic costs when developing and testing new clinical algorithms. IMPLICATIONS FOR FURTHER RESEARCH: Future studies may wish to consider similar methods to estimate costs in evaluating algorithm-based practices.

Journal Article↗
PubMed2005

Statistical validation of mutual information calculations: comparison of alternative numerical algorithms.

Given two time series X and Y , their mutual information, I (X,Y) = I (Y,X) , is the average number of bits of X that can be predicted by measuring Y and vice versa. In the analysis of observational data, calculation of mutual information occurs in three contexts: identification of nonlinear correlation, determination of an optimal sampling interval, particularly when embedding data, and in the investigation of causal relationships with directed mutual information. In this contribution a minimum description length argument is used to determine the optimal number of elements to use when characterizing the distributions of X and Y . However, even when using partitions of the X and Y axis indicated by minimum description length, mutual information calculations performed with a uniform partition of the XY plane can give misleading results. This motivated the construction of an algorithm for calculating mutual information that uses an adaptive partition. This algorithm also incorporates an explicit test of the statistical independence of X and Y in a calculation that returns an assessment of the corresponding null hypothesis. The previously published Fraser-Swinney algorithm for calculating mutual information includes a sophisticated procedure for local adaptive control of the partitioning process. When the Fraser and Swinney algorithm and the algorithm constructed here are compared, they give very similar numerical results (less than 4% difference in a typical application). Detailed comparisons are possible when X and Y are correlated jointly Gaussian distributed because an analytic expression for I (X,Y) can be derived for that case. Based on these tests, three conclusions can be drawn. First, the algorithm constructed here has an advantage over the Fraser-Swinney algorithm in providing an explicit calculation of the probability of the null hypothesis that X and Y are independent. Second, the Fraser-Swinney algorithm is marginally the more accurate of the two algorithms when large data sets are used. With smaller data sets, however, the Fraser-Swinney algorithm reports structures that disappear when more data are available. Third, the algorithm constructed here requires about 0.5% of the computation time required by the Fraser-Swinney algorithm.

Journal Article↗
PubMed2004

Influence of calculation algorithm on dose distribution in irradiation of non-small cell lung cancer (NSCLC) collapsed cone versus pencil beam.

PURPOSE: . The influence of two different calculation algorithms ("pencil beam" [PB] versus "collapsed cone" [CC]) on dose distribution, as well as the dose-volume histograms (DVHs) of the planning target volume (PTV) and the organs at risk was analyzed for irradiation of lung cancer. MATERIAL AND METHODS: . Between 10/2001 and 02/2002 three-dimensional treatment planning was done in ten patients with lung cancer (Helax, TMS((R)), V.6.01). The PTV, the ipsilateral lung (IL) and the contralateral lung (CL) were defined in each axial CT slice (slice thickness 1 cm). Dose distributions for three-dimensional multiple-field technique were calculated using a PB and a CC algorithm, respectively. Normalization was in accordance with ICRU 50. The DVHs were analyzed relating the minimum, maximum, median and mean dose to the volumes of interest (VOI). RESULTS: . Median PTV amounted to 774 cm(3). Minimum dose within the PTV was 67.4% for CC and 75.6% for PB algorithm (p = 0.04). Using the CC algorithm, only 76.5% of the PTV was included by the 95% isodose, whereas 90.1% was included when the PB algorithm (p = 0.01) was used. Median volume of IL amounted to 1 953 cm(3). Mean dose to IL was 43.0% for CC and 44.0% for PB algorithm (p = 0.02). Median volume of IL within the 80% isodose was 19.6% for CC and 24.1% for PB algorithm (p < 0.01). Median volume of CL amounted to 1 847 cm(3). Mean dose to CL was 17.4% for CC and 18.1% for PB algorithm (p < 0.01). Volume of CL within the 80% isodose was 3.3% for CC and 4.1% for PB algorithm (p = 0.03). CONCLUSION: . The CC and PB calculation algorithms result in different dose distributions in case of lung tumors. Particularly the minimum dose to the PTV, which may be relevant for tumor control, is significantly lower for CC. Since it is generally accepted that the CC algorithm describes secondary particle transport more exactly than PB models, the use of the latter should be critically evaluated in the treatment planning of lung cancer.

Algorithms↗
PubMed2005

Stability analysis of a three-term backpropagation algorithm.

Efficient learning by the backpropagation (BP) algorithm is required for many practical applications. The BP algorithm calculates the weight changes of artificial neural networks, and a common approach is to use a two-term algorithm consisting of a learning rate (LR) and a momentum factor (MF). The major drawbacks of the two-term BP learning algorithm are the problems of local minima and slow convergence speeds, which limit the scope for real-time applications. Recently the addition of an extra term, called a proportional factor (PF), to the two-term BP algorithm was proposed. The third increases the speed of the BP algorithm. However, the PF term also reduces the convergence of the BP algorithm, and criteria for evaluating convergence are required to facilitate the application of the three terms BP algorithm. This paper analyzes the convergence of the new three-term backpropagation algorithm. If the learning parameters of the three-term BP algorithm satisfy the conditions given in this paper, then it is guaranteed that the system is stable and will converge to a local minimum. It is proved that if at least one of the eigenvalues of matrix F (compose of the Hessian of the cost function and the system Jacobian of the error vector at each iteration) is negative, then the system becomes unstable. Also the paper shows that all the local minima of the three-term BP algorithm cost function are stable. The relationship between the learning parameters are established in this paper such that the stability conditions are met.

Algorithms↗
PubMed1994

The synergy of pathways and algorithms: two tools work better than one.

BACKGROUND: Clinical quality improvement efforts at the Medical Center Hospital of Vermont (MCHV) led to the development of critical pathways, which show the ideal plan of care, and algorithms, which help clinicians make one of many complicated decisions within a plan of care. A synergy appears to develop when pathways and algorithms are used together. DEVELOPMENT OF PATHWAYS AND ALGORITHMS: A steering committee supports and oversees pathway and algorithm efforts. A quarterly tracking report updating progress for all pathways and algorithms is circulated to all nurse managers, medical staff, and administrators. When combining pathways and algorithms, the pathway is created first. Algorithms are developed for trouble spots within a pathway. CORONARY ARTERY BYPASS GRAFT (CABG) PATIENTS: Having developed the pathway for CABG patients, the CABG case management team meets monthly to review outcomes and variances. For example, an algorithm for managing atrial arrhythmias--the chief cause of variance for one month's results--was developed. The combination of pathways and algorithms for CABG patients has resulted in a reduction of 2.5 days for total length of stay (including 1 day on the surgical intensive care unit [SICU]), for a mean cost savings of $3,500. Re-admission to the SICU, reintubation, and mortality rates have all decreased. CONCLUSION: The idea of reaping the benefits of both pathways and algorithms is becoming more popular at MCHV, where teams use algorithms to improve on complicated processes underlying the pathways.

Algorithms↗
PubMed2004

An accurate, robust, and computationally efficient navigator algorithm for measuring diaphragm positions.

PURPOSE: The purpose of this study is to develop an improved algorithm for measuring the position of the diaphragm using navigator echoes. METHODS: This algorithm was applied to navigator echo data acquired from 14 cardiac patients. For each patient, 160 navigator echo profiles were acquired across the right hemi-diaphragm along the superior-inferior direction. RESULTS: The accuracy of the proposed edge-detection algorithm was evaluated together with that of the least-squares and linear phase-shift algorithms. The estimated measurement error of the proposed algorithm was approximately two times smaller than that of the least-squares algorithm (Magn Reson Med, 1996:36: 117-123), and was approximately four times smaller than that of the linear phase-shift algorithm (Magn Reson Med, 1999;42:548-553). The computational efficiency of this algorithm was 7.5 times higher than that of the least-squares algorithm and was comparable with that of the linear phase-shift algorithm. CONCLUSION: The presented algorithm is accurate, robust, and computationally efficient in the measurement of the diaphragm position.

Adult↗
PubMed1999

An optimal algorithm for automatic genotype elimination.

In an effort to accelerate likelihood computations on pedigrees, Lange and Goradia defined a genotype-elimination algorithm that aims to identify those genotypes that need not be considered during the likelihood computation. For pedigrees without loops, they showed that their algorithm was optimal, in the sense that it identified all genotypes that lead to a Mendelian inconsistency. Their algorithm, however, is not optimal for pedigrees with loops, which continue to pose daunting computational challenges. We present here a simple extension of the Lange-Goradia algorithm that we prove is optimal on pedigrees with loops, and we give examples of how our new algorithm can be used to detect genotyping errors. We also introduce a more efficient and faster algorithm for carrying out the fundamental step in the Lange-Goradia algorithm-namely, genotype elimination within a nuclear family. Finally, we improve a common algorithm for computing the likelihood of a pedigree with multiple loops. This algorithm breaks each loop by duplicating a person in that loop and then carrying out a separate likelihood calculation for each vector of possible genotypes of the loop breakers. This algorithm, however, does unnecessary computations when the loop-breaker vector is inconsistent. In this paper we present a new recursive loop breaker-elimination algorithm that solves this problem and illustrate its effectiveness on a pedigree with six loops.

Algorithms↗
PubMed1999

Ordered subsets algorithms for transmission tomography.

The ordered subsets EM (OSEM) algorithm has enjoyed considerable interest for emission image reconstruction due to its acceleration of the original EM algorithm and ease of programming. The transmission EM reconstruction algorithm converges very slowly and is not used in practice. In this paper, we introduce a simultaneous update algorithm called separable paraboloidal surrogates (SPS) that converges much faster than the transmission EM algorithm. Furthermore, unlike the 'convex algorithm' for transmission tomography, the proposed algorithm is monotonic even with nonzero background counts. We demonstrate that the ordered subsets principle can also be applied to the new SPS algorithm for transmission tomography to accelerate 'convergence', albeit with similar sacrifice of global convergence properties as for OSEM. We implemented and evaluated this ordered subsets transmission (OSTR) algorithm. The results indicate that the OSTR algorithm speeds up the increase in the objective function by roughly the number of subsets in the early iterates when compared to the ordinary SPS algorithm. We compute mean square errors and segmentation errors for different methods and show that OSTR is superior to OSEM applied to the logarithm of the transmission data. However, penalized-likelihood reconstructions yield the best quality images among all other methods tested.

Algorithms↗
PubMed2005

A three-dimensional weighted cone beam filtered backprojection (CB-FBP) algorithm for image reconstruction in volumetric CT under a circular source trajectory.

The original FDK algorithm proposed for cone beam (CB) image reconstruction under a circular source trajectory has been extensively employed in medical and industrial imaging applications. With increasing cone angle, CB artefacts in images reconstructed by the original FDK algorithm deteriorate, since the circular trajectory does not satisfy the so-called data sufficiency condition (DSC). A few 'circular plus' trajectories have been proposed in the past to help the original FDK algorithm to reduce CB artefacts by meeting the DSC. However, the circular trajectory has distinct advantages over other scanning trajectories in practical CT imaging, such as head imaging, breast imaging, cardiac, vascular and perfusion applications. In addition to looking into the DSC, another insight into the CB artefacts existing in the original FDK algorithm is the inconsistency between conjugate rays that are 180 degrees apart in view angle (namely conjugate ray inconsistency). The conjugate ray inconsistency is pixel dependent, varying dramatically over pixels within the image plane to be reconstructed. However, the original FDK algorithm treats all conjugate rays equally, resulting in CB artefacts that can be avoided if appropriate weighting strategies are exercised. Along with an experimental evaluation and verification, a three-dimensional (3D) weighted axial cone beam filtered backprojection (CB-FBP) algorithm is proposed in this paper for image reconstruction in volumetric CT under a circular source trajectory. Without extra trajectories supplemental to the circular trajectory, the proposed algorithm applies 3D weighting on projection data before 3D backprojection to reduce conjugate ray inconsistency by suppressing the contribution from one of the conjugate rays with a larger cone angle. Furthermore, the 3D weighting is dependent on the distance between the reconstruction plane and the central plane determined by the circular trajectory. The proposed 3D weighted axial CB-FBP algorithm can be implemented in either the native CB geometry or the so-called cone-parallel geometry. By taking the cone-parallel geometry as an example, the experimental evaluation shows that, up to a moderate cone angle corresponding to a detector dimension of 64 x 0.625 mm, the CB artefacts can be substantially suppressed by the proposed algorithm, while advantages of the original FDK algorithm, such as the filtered backprojection algorithm structure, 1D ramp filtering and data manipulation efficiency, are maintained.

Algorithms↗
PubMed2006

A three-dimensional-weighted cone beam filtered backprojection (CB-FBP) algorithm for image reconstruction in volumetric CT-helical scanning.

Based on the structure of the original helical FDK algorithm, a three-dimensional (3D)-weighted cone beam filtered backprojection (CB-FBP) algorithm is proposed for image reconstruction in volumetric CT under helical source trajectory. In addition to its dependence on view and fan angles, the 3D weighting utilizes the cone angle dependency of a ray to improve reconstruction accuracy. The 3D weighting is ray-dependent and the underlying mechanism is to give a favourable weight to the ray with the smaller cone angle out of a pair of conjugate rays but an unfavourable weight to the ray with the larger cone angle out of the conjugate ray pair. The proposed 3D-weighted helical CB-FBP reconstruction algorithm is implemented in the cone-parallel geometry that can improve noise uniformity and image generation speed significantly. Under the cone-parallel geometry, the filtering is naturally carried out along the tangential direction of the helical source trajectory. By exploring the 3D weighting's dependence on cone angle, the proposed helical 3D-weighted CB-FBP reconstruction algorithm can provide significantly improved reconstruction accuracy at moderate cone angle and high helical pitches. The 3D-weighted CB-FBP algorithm is experimentally evaluated by computer-simulated phantoms and phantoms scanned by a diagnostic volumetric CT system with a detector dimension of 64 x 0.625 mm over various helical pitches. The computer simulation study shows that the 3D weighting enables the proposed algorithm to reach reconstruction accuracy comparable to that of exact CB reconstruction algorithms, such as the Katsevich algorithm, under a moderate cone angle (4 degrees) and various helical pitches. Meanwhile, the experimental evaluation using the phantoms scanned by a volumetric CT system shows that the spatial resolution along the z-direction and noise characteristics of the proposed 3D-weighted helical CB-FBP reconstruction algorithm are maintained very well in comparison to the FDK-type algorithms. Moreover, the experimental evaluation by clinical data verifies that the proposed 3D-weighted CB-FBP algorithm for image reconstruction in volumetric CT under helical source trajectory meets the challenges posed by diagnostic applications of volumetric CT imaging.

Algorithms↗
PubMed2005

Comparison of HIV type 1 protease inhibitor susceptibility results in viral samples analyzed by phenotypic drug resistance assays and by six resistance algorithms: an analysis of a subpopulation of the CHORUS cohort.

Drug resistance testing is increasingly used to guide treatment decisions in patients infected with HIV-1. A number of rules-based algorithms have been designed to predict drug resistance profiles based on the HIV-1 genotypic data. Drug-resistance mutations in 206 viral samples from protease inhibitor (PI)-experienced subjects with HIV-1 infection were assessed, and the level of susceptibility of the samples predicted using seven unique algorithms. kappa scores were used to compare agreement of results obtained using each of the predictive algorithms with the phenotypic assay results. Good overall agreement between the different algorithms and the phenotypic results was observed. Good or excellent agreement was observed between the results obtained by the predictive algorithms and the phenotypic assay results for ritonavir, indinavir, saquinavir, and nelfinavir. For amprenavir and lopinavir, there were marked differences between the different algorithms, with poor agreement (kappa < 0.40) obtained with four of the seven algorithms for amprenavir. For lopinavir, poor agreement was obtained with three of seven algorithms using the 2.5-fold biological cut-off and four of seven with the clinical cut-off of 10. Atazanavir susceptibility was evaluated for concordance among six algorithms, with a range of 23-50% of the samples maintaining susceptibility. Although this cohort of patients included many who were highly antiretroviral experienced, predictive algorithms demonstrated good agreement with phenotype for several Pls. For those where discordance among algorithms existed, further improvement will likely occur as drug resistance pathways for the more recently approved PIs are elucidated.

Adult↗
PubMed2000

The Texas Children's Medication Algorithm Project: Report of the Texas Consensus Conference Panel on Medication Treatment of Childhood Attention-Deficit/Hyperactivity Disorder. Part I. Attention-Deficit/Hyperactivity Disorder.

OBJECTIVES: Expert consensus methodology was used to develop evidence-based, consensually agreed-upon medication treatment algorithms for attention-deficit/hyperactivity disorder (ADHD) in the public mental health sector. Although treatment algorithms for adult mental disorders have been developed, this represents one of the first attempts to develop similar algorithms for childhood mental disorders. Although these algorithms were developed initially for the public sector, the goals of this approach are to increase the uniformity of treatment and improve the clinical outcomes of children and adolescents with ADHD in a variety of treatment settings. METHOD: A consensus conference of academic clinicians and researchers, practicing clinicians, administrators, consumers, and families was convened to develop evidence-based consensus algorithms for the pharmacotherapy of childhood ADHD. After a series of presentations of current research evidence and panel discussion, the consensus panel met and drafted the algorithms along with guidelines for implementation. RESULTS: The panel developed consensually agreed-upon algorithms for ADHD with and without specific comorbid disorders. The algorithms consist of systematic strategies for psychopharmacological interventions and tactics to ensure successful implementation of the strategies. While the algorithms focused on the medication management of ADHD, the conference emphasized that psychosocial treatments are often a critical component of the overall management of ADHD. CONCLUSIONS: Medication algorithms for ADHD can be developed with consensus. A companion article will discuss the implementation of these algorithms.

Adrenergic alpha-Agonists↗
PubMed1999

Prospective evidence of the superiority of a sonography-based algorithm in the assessment of blunt abdominal injury.

BACKGROUND: Although the routine use of FAST (focused assessment with sonography for trauma) in the evaluation of trauma victims is increasing, to our knowledge, a prospective comparison of contemporary adult trauma victims managed with and without FAST has not been reported in North America. METHODS: Adult victims of blunt trauma for whom there was a suspicion of abdominal injury were managed with one of two diagnostic algorithms, FAST or no-FAST. The two algorithms were compared for diagnostic accuracy, cost, time, and delayed diagnoses. RESULTS: Among 706 patients (mean Injury Severity Score, 23), 460 were managed with FAST and 246 with no-FAST. The two groups were similar with respect to age, Injury Severity Score, prehospital time, and mortality (p = not significant). There were 3 of 460 (0.7%) delayed diagnoses in the FAST group and 4 of 246 (1.6%) in the no-FAST group (p = not significant). The diagnostic accuracy for the FAST and no-FAST algorithms was 99% and 98%, respectfully. The FAST and no-FAST algorithms led to similar rates of laparotomy, 13% and 14%, respectfully, but nonoperative management was more common in the no-FAST group (p < 0.01). The mean diagnostic cost for the FAST algorithm was $156, compared with $540 with the no-FAST algorithm (p < 0.0001) and the mean time required for diagnostic work-up was 53 minutes with the FAST algorithm, compared with 151 minutes with the no-FAST algorithm (p < 0.0001). CONCLUSION: This study has provided prospective evidence that a FAST-based algorithm for blunt abdominal injury was more rapid, less expensive, and as accurate as an algorithm that used computed tomography or diagnostic peritoneal lavage only. Trauma centers are encouraged to incorporate a FAST-based algorithm into their initial management of blunt trauma victims.

Abdominal Injuries↗
PubMed2004

Accuracy, efficiency and robustness of four algorithms allowing full sibship reconstruction from DNA marker data.

In the problem of reconstructing full sib pedigrees from DNA marker data, three existing algorithms and one new algorithm are compared in terms of accuracy, efficiency and robustness using real and simulated data sets. An algorithm based on the exclusion principle and another based on a maximization of the Simpson index were very accurate at reconstructing data sets comprising a few large families but had problems with data sets with limited family structure, while a Markov Chain Monte Carlo (MCMC) algorithm based on the maximization of a partition score had the opposite behaviour. An MCMC algorithm based on maximizing the full joint likelihood performed best in small data sets comprising several medium-sized families but did not work well under most other conditions. It appears that the likelihood surface may be rough and presents challenges for the MCMC algorithm to find the global maximum. This likelihood algorithm also exhibited problems in reconstructing large family groups, due possibly to limits in computational precision. The accuracy of each algorithm improved with an increasing amount of information in the data set, and was very high with eight loci with eight alleles each. All four algorithms were quite robust to deviation from an idealized uniform allelic distribution, to departures from idealized Mendelian inheritance in simulated data sets and to the presence of null alleles. In contrast, none of the algorithms were very robust to the probable presence of error/mutation in the data. Depending upon the type of mutation or errors and the algorithm used, between 70 and 98% of the affected individuals were classified improperly on average.

Algorithms↗
PubMed2004

A filtered backprojection algorithm for cone beam reconstruction using rotational filtering under helical source trajectory.

With the evolution from multi-detector-row CT to cone beam (CB) volumetric CT, maintaining reconstruction accuracy becomes more challenging. To combat the severe artifacts caused by a large cone angle in CB volumetric CT, three-dimensional reconstruction algorithms have to be utilized. In practice, filtered backprojection (FBP) reconstruction algorithms are more desirable due to their computational structure and image generation efficiency. One of the CB-FBP reconstruction algorithms is the well-known FDK algorithm that was originally derived for a circular x-ray source trajectory by heuristically extending its two-dimensional (2-D) counterpart. Later on, a general CB-FBP reconstruction algorithm was derived for noncircular, such as helical, source trajectories. It has been recognized that a filtering operation in the projection data along the tangential direction of a helical x-ray source trajectory can significantly improve the reconstruction accuracy of helical CB volumetric CT. However, the tangential filtering encounters latitudinal data truncation, resulting in degraded noise characteristics or data manipulation inefficiency. A CB-FBP reconstruction algorithm using one-dimensional rotational filtering across detector rows (namely CB-RFBP) is proposed in this paper. Although the proposed CB-RFBP reconstruction algorithm is approximate, it approaches the reconstruction accuracy that can be achieved by exact helical CB-FBP reconstruction algorithms for moderate cone angles. Unlike most exact CB-FBP reconstruction algorithms in which the redundant data are usually discarded, the proposed CB-RFBP reconstruction algorithm make use of all available projection data, resulting in significantly improved noise characteristics and dose efficiency. Moreover, the rotational filtering across detector rows not only survives the so-called long object problem, but also avoids latitudinal data truncation existing in other helical CB-FBP reconstruction algorithm in which a tangential filtering is carried out, providing better noise characteristics, dose efficiency and data manipulation efficiency.

Algorithms↗
PubMed2005

Comparison of inhomogeneity correction algorithms in small photon fields.

Algorithms such as convolution superposition, Batho, and equivalent pathlength which were originally developed and validated for conventional treatments under conditions of electronic equilibrium using relatively large fields greater than 5 x 5 cm2 are routinely employed for inhomogeneity corrections. Modern day treatments using intensity modulated radiation therapy employ small beamlets characterized by the resolution of the multileaf collimator. These beamlets, in general, do not provide electronic equilibrium even in a homogeneous medium, and these effects are exaggerated in media with inhomogenieties. Monte Carlo simulations are becoming a tool of choice in understanding the dosimetry of small photon fields as they encounter low density media. In this study, depth dose data from the Monte Carlo simulations are compared to the results of the convolution superposition, Batho, and equivalent pathlength algorithms. The central axis dose within the low-density inhomogeneity as calculated by Monte Carlo simulation and convolution superposition decreases for small field sizes whereas it increases using the Batho and equivalent pathlength algorithms. The dose perturbation factor (DPF) is defined as the ratio of dose to a point within the inhomogeneity to the same point in a homogeneous phantom. The dose correction factor is defined as the ratio of dose calculated by an algorithm at a point to the Monte Carlo derived dose at the same point, respectively. DPF is noted to be significant for small fields and low density for all algorithms. Comparisons of the algorithms with Monte Carlo simulations is reflected in the DCF, which is close to 1.0 for the convolution-superposition algorithm. The Batho and equivalent pathlength algorithms differ significantly from Monte Carlo simulation for most field sizes and densities. Convolution superposition shows better agreement with Monte Carlo data versus the Batho or equivalent pathlength corrections. As the field size increases the DCF's for all algorithms converge toward 1.0. The largest differences in DCF are at the interface where changes in electron transport are greatest. For a 6 MV photon beam, electronic equilibrium is restored at field sizes above 3 cm diameter and all of the algorithms predict dose in and beyond the inhomogeneous region equally well. For accurate dosimetry of small fields within and near inhomogeneities, however, simple algorithms such as Batho and equivalent pathlength should be avoided.

Algorithms↗
PubMed2006

Matrix factorization algorithms for the identification of muscle synergies: evaluation on simulated and experimental data sets.

Several recent studies have used matrix factorization algorithms to assess the hypothesis that behaviors might be produced through the combination of a small number of muscle synergies. Although generally agreeing in their basic conclusions, these studies have used a range of different algorithms, making their interpretation and integration difficult. We therefore compared the performance of these different algorithms on both simulated and experimental data sets. We focused on the ability of these algorithms to identify the set of synergies underlying a data set. All data sets consisted of nonnegative values, reflecting the nonnegative data of muscle activation patterns. We found that the performance of principal component analysis (PCA) was generally lower than that of the other algorithms in identifying muscle synergies. Factor analysis (FA) with varimax rotation was better than PCA, and was generally at the same levels as independent component analysis (ICA) and nonnegative matrix factorization (NMF). ICA performed very well on data sets corrupted by constant variance Gaussian noise, but was impaired on data sets with signal-dependent noise and when synergy activation coefficients were correlated. Nonnegative matrix factorization (NMF) performed similarly to ICA and FA on data sets with signal-dependent noise and was generally robust across data sets. The best algorithms were ICA applied to the subspace defined by PCA (ICAPCA) and a version of probabilistic ICA with nonnegativity constraints (pICA). We also evaluated some commonly used criteria to identify the number of synergies underlying a data set, finding that only likelihood ratios based on factor analysis identified the correct number of synergies for data sets with signal-dependent noise in some cases. We then proposed an ad hoc procedure, finding that it was able to identify the correct number in a larger number of cases. Finally, we applied these methods to an experimentally obtained data set. The best performing algorithms (FA, ICA, NMF, ICAPCA, pICA) identified synergies very similar to one another. Based on these results, we discuss guidelines for using factorization algorithms to analyze muscle activation patterns. More generally, the ability of several algorithms to identify the correct muscle synergies and activation coefficients in simulated data, combined with their consistency when applied to physiological data sets, suggests that the muscle synergies found by a particular algorithm are not an artifact of that algorithm, but reflect basic aspects of the organization of muscle activation patterns underlying behaviors.

Algorithms↗