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PubMedDate not supplied

The clinical algorithm nosology: a method for comparing algorithmic guidelines.

Concern regarding the cost and quality of medical care has led to a proliferation of competing clinical practice guidelines. No technique has been described for determining objectively the degree of similarity between alternative guidelines for the same clinical problem. The authors describe the development of the Clinical Algorithm Nosology (CAN), a new method to compare one form of guideline: the clinical algorithm. The CAN measures overall design complexity independent of algorithm content, qualitatively describes the clinical differences between two alternative algorithms, and then scores the degree of similarity between them. CAN algorithm design-complexity scores correlated highly with clinicians' estimates of complexity on an ordinal scale (r = 0.86). Five pairs of clinical algorithms addressing three topics (gallstone lithotripsy, thyroid nodule, and sinusitis) were selected for interrater reliability testing of the CAN clinical-similarity scoring system. Raters categorized the similarity of algorithm pathways in alternative algorithms as "identical," "similar," or "different." Interrater agreement was achieved on 85/109 scores (80%), weighted kappa statistic, k = 0.73. It is concluded that the CAN is a valid method for determining the structural complexity of clinical algorithms, and a reliable method for describing differences and scoring the similarity between algorithms for the same clinical problem. In the future, the CAN may serve to evaluate the reliability of algorithm development programs, and to support providers and purchasers in choosing among alternative clinical guidelines.

Algorithms↗
PubMed2006

The Texas Children's Medication Algorithm Project: revision of the algorithm for pharmacotherapy of attention-deficit/hyperactivity disorder.

OBJECTIVE: In 1998, the Texas Department of Mental Health and Mental Retardation developed algorithms for medication treatment of attention-deficit/hyperactivity disorder (ADHD). Advances in the psychopharmacology of ADHD and results of a feasibility study of algorithm use in community mental health centers caused the algorithm to be modified and updated. METHOD: We convened a consensus conference of academic clinicians and researchers, practicing clinicians, administrators, consumers, and families to revise the algorithms for the pharmacotherapy of ADHD itself as well as ADHD with specific comorbid disorders. New research was reviewed by national experts, and rationales were provided for proposed changes and additions to the algorithms. The changes to the algorithms were discussed and approved both by the national experts and experienced clinicians from the Texas public mental health system. RESULTS: The panel developed consensually agreed-upon algorithms for ADHD with and without comorbid disorders. The major changes included elimination of pemoline as a treatment option, adding atomoxetine to the algorithm, and refining guidelines for treating ADHD with comorbid depression, aggressive behaviors, and tic disorders. CONCLUSIONS: Medication algorithms for ADHD can be modified to keep abreast of developments in the field. Although these evidence- and consensus-based treatment recommendations may be a useful approach to guide the treatment of ADHD in children, additional research is needed to determine how these algorithms can be used to maximally benefit child outcomes.

Adrenergic Uptake Inhibitors↗
PubMed2004

A new algorithm for haplotype-based association analysis: the Stochastic-EM algorithm.

It is now widely accepted that haplotypic information can be of great interest for investigating the role of a candidate gene in the etiology of complex diseases. In the absence of family data, haplotypes cannot be deduced from genotypes, except for individuals who are homozygous at all loci or heterozygous at only one site. Statistical methodologies are therefore required for inferring haplotypes from genotypic data and testing their association with a phenotype of interest. Two maximum likelihood algorithms are often used in the context of haplotype-based association studies, the Newton-Raphson (NR) and the Expectation-Maximisation (EM) algorithms. In order to circumvent the limitations of both algorithms, including convergence to local minima and saddle points, we here described how a stochastic version of the EM algorithm, referred to as SEM, could be used for testing haplotype-phenotype association. Statistical properties of the SEM algorithm were investigated through a simulation study for a large range of practical situations, including small/large samples and rare/frequent haplotypes, and results were compared to those obtained by use of the standard NR algorithm. Our simulation study indicated that the SEM algorithm provides results similar to those of the NR algorithm, making the SEM algorithm of great interest for haplotype-based association analysis, especially when the number of polymorphisms is quite large.

Algorithms↗
PubMed2002

Discrimination of ventricular tachycardia from supraventricular tachycardia by a downloaded wavelet-transform morphology algorithm: a paradigm for development of implantable cardioverter defibrillator detection algorithms.

INTRODUCTION: Present implantable cardioverter defibrillators (ICDs) have algorithms that discriminate supraventricular tachycardia (SVT) from ventricular tachycardia (VT). One type of algorithm is based on differences in morphology of ventricular electrograms during VT and SVT. Prior SVT-VT discrimination algorithms have not undergone real-time evaluation in ambulatory patients until they were incorporated permanently into ICDs. This approach may result in incomplete testing of electrogram morphology algorithms because they are influenced by posture, activity, and electrogram maturation. We downloaded software into implanted ICDs to study a novel algorithm that compares morphologies of baseline and tachycardia electrograms based on differences between corresponding coefficients of their wavelet transforms. This comparison is expressed as a match-percent score. METHODS AND RESULTS: In 23 patients, we downloaded the wavelet algorithm into implanted ICDs to assess the temporal and postural stability of baseline electrograms as measured by this algorithm and its accuracy for SVT-VT discrimination. Median follow-up was 6 months. Software was downloaded into all ICDs without altering other device functions. With few exceptions, percent template match in baseline rhythm was stable with changes in body position, rest versus walking, isometric exercise, and over time (1 and 3 months). Using the nominal match-percent threshold of 70%, sensitivity for detection of 38 VTs was 100%. Specificity for rejection of 65 SVTs was 78%. SVTs were rejected for a total of 2.7 hours. Inappropriate detections of SVT as VT were caused by electrogram truncation, myopotential interference with low-amplitude electrograms, waveform alignment error, and rate-dependent aberrancy. The first three accounted for 69% of inappropriate detections and could have been prevented by optimal programming. The optimal match-percent threshold was 60% to 70% based on a receiver-operator characteristic curve. After shocks, the median time for baseline electrogram morphology to normalize was 85 seconds. CONCLUSION: The wavelet morphology algorithm has high sensitivity for VT detection. Inappropriate detections of SVT as VT may be reduced by optimal programming. Downloadable software permits evaluation of new algorithms in implanted ICDs.

Aged↗
PubMed1999

Diagnostic accuracy of physician review, expert algorithms and data-derived algorithms in adult verbal autopsies.

BACKGROUND: The verbal autopsy (VA) is used to collect information on cause-specific mortality from bereaved relatives. A cause of death may be assigned by physician review of the questionnaires, or by an algorithm. We compared the diagnostic accuracy of physician review, an expert algorithm, and data-derived algorithms. METHODS: Data were drawn from a multicentre validation study of 796 adult deaths that occurred in hospitals in Tanzania, Ethiopia, and Ghana. A 'gold standard' cause of death was assigned using hospital records and death certificates. The VA interviews were carried out by trained fieldworkers 1-21 months after the subject's death. A cause of death was assigned by physician review and an expert algorithm. Data-derived algorithms that most accurately estimated the cause-specific mortality fraction (CSMF) for each cause of death were identified using logistic regression. RESULTS: The most common causes of death were tuberculosis/AIDS (CSMF = 18.6%), malaria (CSMF = 10.7%), meningitis (CSMF = 8.3%), and cardiovascular disorders (CSMF = 8.2%). The CSMF obtained using physician review was within +/-20% of the gold standard value for 12 causes of death including the four common causes. The CSMF obtained using the expert algorithm was within +/-20% of the gold standard for eight causes of death, including tuberculosis/AIDS, malaria, and meningitis. The CSMF obtained using the data-derived algorithms was within +/-20% of the gold standard for seven causes of death, including tuberculosis/ AIDS, meningitis, and cardiovascular disorders. All three methods yielded a specificity of at least 80% for all causes of death, and a sensitivity of at least 80% for deaths due to injuries and rabies. CONCLUSIONS: For those settings where physician review is not feasible, expert and data-derived algorithms provide an alternative approach for assigning many causes of death. We recommend that the algorithms proposed herein are validated further.

Acquired Immunodeficiency Syndrome↗
PubMed2004

Using the EM algorithm to train neural networks: misconceptions and a new algorithm for multiclass classification.

The expectation-maximization (EM) algorithm has been of considerable interest in recent years as the basis for various algorithms in application areas of neural networks such as pattern recognition. However, there exists some misconceptions concerning its application to neural networks. In this paper, we clarify these misconceptions and consider how the EM algorithm can be adopted to train multilayer perceptron (MLP) and mixture of experts (ME) networks in applications to multiclass classification. We identify some situations where the application of the EM algorithm to train MLP networks may be of limited value and discuss some ways of handling the difficulties. For ME networks, it is reported in the literature that networks trained by the EM algorithm using iteratively reweighted least squares (IRLS) algorithm in the inner loop of the M-step, often performed poorly in multiclass classification. However, we found that the convergence of the IRLS algorithm is stable and that the log likelihood is monotonic increasing when a learning rate smaller than one is adopted. Also, we propose the use of an expectation-conditional maximization (ECM) algorithm to train ME networks. Its performance is demonstrated to be superior to the IRLS algorithm on some simulated and real data sets.

Algorithms↗
PubMed2002

A novel parallel algorithm for large-scale Fock matrix construction with small locally distributed memory architectures: RT parallel algorithm.

We developed a novel parallel algorithm for large-scale Fock matrix calculation with small locally distributed memory architectures, and named it the "RT parallel algorithm." The RT parallel algorithm actively involves the concept of integral screening, which is indispensable for reduction of computing times with large-scale biological molecules. The primary characteristic of this algorithm is parallel efficiency, which is achieved by well-balanced reduction of both communicating and computing volume. Only the density matrix data necessary for Fock matrix calculations are communicated, and the data once communicated are reutilized for calculations as many times as possible. The RT parallel algorithm is a scalable method because required memory volume does not depend on the number of basis functions. This algorithm automatically includes a partial summing technique that is indispensable for maintaining computing accuracy, and can also include some conventional methods to reduce calculation times. In our analysis, the RT parallel algorithm had better performance than other methods for massively parallel processors. The RT parallel algorithm is most suitable for massively parallel and distributed Fock matrix calculations for large-scale biological molecules with more than thousands of basis functions.

Algorithms↗
PubMed1999

The Goldman algorithm revisited: prospective evaluation of a computer-derived algorithm versus unaided physician judgment in suspected acute myocardial infarction.

BACKGROUND: It has been nearly a decade since Goldman's computer-driven algorithm to predict myocardial infarction was validated. Despite the potential to avoid admission of patients without acute myocardial infarction (AMI) to the coronary care unit (CCU), the routine use of computer-generated protocols has not been widely adopted. METHODS: Two hundred consecutive patients admitted to a university-affiliated community hospital with the suspected diagnosis of AMI as determined by physicians without the aid of the Goldman protocol underwent a blinded prospective evaluation to assess the performance of the Goldman algorithm in predicting the presence of AMI. Over the same time period, the Goldman algorithm was applied by retrospective chart review in 762 patients with non-AMI admitting diagnoses. Prospective history, physical examination, and electrocardiographic data were obtained within 24 hours of admission to the CCU by a physician blinded to each patient's clinical course. Retrospective chart reviews were conducted for 762 patients with chest pain given with non-AMI diagnoses. RESULTS: The diagnosis of AMI was confirmed in 68.5% (137/200) of patients with suspected AMI admitted to the CCU. In prospective parallel evaluations the Goldman algorithm predicted the presence of AMI in 167 (83.5%) of these 200 patients. All 137 confirmed patients with AMI were correctly identified by the Goldman algorithm. All major in-hospital complications occurred in the 137 patients who were diagnosed as having AMI. Of the 762 patients with chest pain with non-AMI diagnoses, only 27 (3.5%) sustained an AMI. The Goldman algorithm predicted the presence of AMI in 85% (23/27) of these patients. Adherence to the use of Goldman's algorithm in the triage of chest pain could have prevented 16.5% of CCU admissions for AMI. CONCLUSIONS: Routine adherence to the Goldman algorithm for the evaluation of patients with acute chest pain could have decreased the number of CCU admissions for suspected AMI by 16. 5%. Because major in-hospital complications occurred only in patients with AMI, this strategy would result in significant cost savings to our health care system without jeopardizing patient safety.

Algorithms↗
PubMed2003

A feasibility study of the children's medication algorithm project (CMAP) algorithm for the treatment of ADHD.

OBJECTIVE: To determine whether an algorithm for the treatment of attention-deficit/hyperactivity disorder (ADHD) can be implemented in a community mental health center. METHOD: Fifty child and adolescent patients at Texas community mental health centers who met criteria for ADHD were treated according to an algorithm-based disease management program for ADHD. Psychiatrists were trained in the use of the algorithm, and each subject underwent a baseline assessment consisting of a structured interview and standardized rating scales. Subjects were monitored for 4 months. At the end of treatment, the psychiatrists completed the Clinical Global Impression Scale (CGI) and the baseline rating scales were repeated. The primary variables of interest were psychiatrist and family adherence to the algorithm. To examine impact on treatment outcome, the CGI of the algorithm subjects was compared with CGIs based on chart reviews of 118 historical controls. RESULTS: Psychiatrists implemented the major aspects of the algorithm, but the detailed tactics of the algorithm (use of fixed titration of stimulants) were less well adhered to. CONCLUSIONS: An algorithm for the treatment of ADHD can be implemented in a community mental health center.

Adolescent↗
PubMed2005

The Texas implementation of medication algorithms: update to the algorithms for treatment of bipolar I disorder.

BACKGROUND: A panel consisting of academic psychiatrists and pharmacist administrators of the Texas Department of State Health Services (formerly Texas Department of Mental Health and Mental Retardation), community mental health physicians, advocates, and consumers met in May 2004 to review new evidence in the pharmacologic treatment of bipolar I disorder (BDI). The goal of the consensus conference was to update and revise the current treatment algorithm for BDI as part of the Texas Implementation of Medication Algorithms, a statewide quality assurance program for the treatment of major psychiatric illness. The guidelines for evaluating possible medications, the criteria for selection and ranking, and the updated algorithms are described. METHOD: Principles from previous consensus conferences were reviewed and amended. Medication algorithms for the acute treatment of hypomanic/manic or mixed and depressive episodes in BDI were developed after examining recent efficacy and safety and tolerability data. Recommendations for maintenance treatments were developed. RESULTS: The panel updated the 2 primary algorithms (hypomanic/manic/mixed and depressive) based on clinical evidence for efficacy, tolerability, and safety developed since 2000. Expert consensus was utilized where clinical evidence was limited. Prevention of new episodes or prophylaxis treatment recommendations were developed based on recent data from longer-term trials. Maintenance recommendations are provided as levels versus a specified staged algorithm, as for acute treatment, due to the relatively limited database to inform treatment. CONCLUSIONS: These algorithms for the treatment of BDI represent the recommendations based on the most recent evidence available. These recommendations are meant to provide a framework for clinical decision making, not to replace clinical judgment. As with any algorithm, treatment practices will evolve beyond the recommendations of this consensus conference as new evidence and additional medications become available.

Algorithms↗
PubMed1999

Between-algorithm, between-individual differences in normal perimetric sensitivity: full threshold, FASTPAC, and SITA. Swedish Interactive Threshold algorithm.

PURPOSE: To determine the between-algorithm differences in perimetric sensitivity for the Swedish Interactive Threshold algorithm (SITA) Standard, SITA Fast, FASTPAC, and Full Threshold algorithms; to determine the between-subject, between-algorithm differences in the magnitude of the normal variation in sensitivity. METHODS: The sample comprised 50 normal subjects (mean age, 52.9 +/- 18.5 years) experienced in automated perimetry. One randomly assigned eye was examined at three visits with Program 30-2 of the Humphrey Field Analyzer (HFA). The first visit was a familiarization session. A two-period crossover design with order randomization within visits was used over the second and third visits. SITA Standard, SITA Fast, and HFA 640 Full Threshold were administered during one visit. FASTPAC and HFA 750 Full Threshold were administered during the remaining visit. RESULTS: Group mean Mean Sensitivity was 0.8 dB higher for SITA Standard than for Full Threshold (P < 0.001) and 1.3 dB higher for SITA Fast than for Full Threshold (P < 0.001). A similar trend was found between SITA and FASTPAC. The group mean Mean Sensitivity for SITA Fast was 0.5 dB higher than for SITA Standard (P < 0.001). The pointwise between-algorithm difference in sensitivity was similar for all algorithms. The pointwise between-algorithm, between-subject variability was lower for SITA. The examination durations for SITA Fast and SITA Standard were half those for FASTPAC and Full Threshold; SITA Fast was 41% that of SITA Standard (P < 0.001). CONCLUSIONS: SITA produced marginally higher mean mean sensitivity compared with that of existing algorithms and markedly reduced examination duration. The reduced between-subject variability of SITA should result in narrower confidence limits for definition of normality.

Adult↗
PubMed2005

Cell-competition algorithm: a new segmentation algorithm for multiple objects with irregular boundaries in ultrasound images.

Segmentation of multiple objects with irregular contours and surrounding sporadic spots is a common practice in ultrasound image analysis. A new region-based approach, called cell-competition algorithm, is proposed for simultaneous segmentation of multiple objects in a sonogram. The algorithm is composed of two essential ideas. One is simultaneous cell-based deformation of regions and the other is cell competition. The cells are generated by two-pass watershed transformations. The cell-competition algorithm has been validated with 13 synthetic images of different contrast-to-noise ratios and 71 breast sonograms. Three assessments have been carried out and the results show that the boundaries derived by the cell-competition algorithm are reasonably comparable to those delineated manually. Moreover, the cell-competition algorithm is robust to the variation of regions-of-interest and a range of thresholds required for the second-pass watershed transformation. The proposed algorithm is also shown to be superior to the region-competition algorithm for both types of images.

Algorithms↗
PubMed2006

A new magnetic resonance electrical impedance tomography (MREIT) algorithm: the RSM-MREIT algorithm with applications to estimation of human head conductivity.

We have developed a new magnetic resonance electrical impedance tomography (MREIT) algorithm, the RSM-MREIT algorithm, for noninvasive imaging of the electrical conductivity distribution using only one component of magnetic flux density. The proposed RSM-MREIT algorithm uses the response surface methodology (RSM) algorithm for optimizing the conductivity distribution through minimizing the errors between the measured and calculated magnetic flux densities. A series of computer simulations has been conducted to assess the performance of the proposed RSM-MREIT algorithm to estimate electrical conductivity values of the scalp, the skull and the brain tissue, in a three-shell piecewise homogeneous head model. Computer simulation studies were conducted in both a spherical and realistic-geometry head model with a single variable (the brain-to-skull conductivity ratio) and three variables (the conductivity of the brain, the skull, and the scalp). The relative error between the target and estimated head conductivity values was less than 12% for both the single-variable and three-variable simulations. These promising simulation results demonstrate the feasibility of the proposed RSM-MREIT algorithm in estimating electrical conductivity values in a piecewise homogeneous head model of the human head, and suggest that the RSM-MREIT algorithm merits further investigation.

Algorithms↗
PubMed1999

Which algorithm for scheduling add-on elective cases maximizes operating room utilization? Use of bin packing algorithms and fuzzy constraints in operating room management.

BACKGROUND: The algorithm to schedule add-on elective cases that maximizes operating room (OR) suite utilization is unknown. The goal of this study was to use computer simulation to evaluate 10 scheduling algorithms described in the management sciences literature to determine their relative performance at scheduling as many hours of add-on elective cases as possible into open OR time. METHODS: From a surgical services information system for two separate surgical suites, the authors collected these data: (1) hours of open OR time available for add-on cases in each OR each day and (2) duration of each add-on case. These empirical data were used in computer simulations of case scheduling to compare algorithms appropriate for "variable-sized bin packing with bounded space." "Variable size" refers to differing amounts of open time in each "bin," or OR. The end point of the simulations was OR utilization (time an OR was used divided by the time the OR was available). RESULTS: Each day there were 0.24 +/- 0.11 and 0.28 +/- 0.23 simulated cases (mean +/- SD) scheduled to each OR in each of the two surgical suites. The algorithm that maximized OR utilization, Best Fit Descending with fuzzy constraints, achieved OR utilizations 4% larger than the algorithm with poorest performance. CONCLUSIONS: We identified the algorithm for scheduling add-on elective cases that maximizes OR utilization for surgical suites that usually have zero or one add-on elective case in each OR. The ease of implementation of the algorithm, either manually or in an OR information system, needs to be studied.

Algorithms↗
PubMed2005

Soft learning vector quantization and clustering algorithms based on non-Euclidean norms: single-norm algorithms.

This paper presents the development of soft clustering and learning vector quantization (LVQ) algorithms that rely on a weighted norm to measure the distance between the feature vectors and their prototypes. The development of LVQ and clustering algorithms is based on the minimization of a reformulation function under the constraint that the generalized mean of the norm weights be constant. According to the proposed formulation, the norm weights can be computed from the data in an iterative fashion together with the prototypes. An error analysis provides some guidelines for selecting the parameter involved in the definition of the generalized mean in terms of the feature variances. The algorithms produced from this formulation are easy to implement and they are almost as fast as clustering algorithms relying on the Euclidean norm. An experimental evaluation on four data sets indicates that the proposed algorithms outperform consistently clustering algorithms relying on the Euclidean norm and they are strong competitors to non-Euclidean algorithms which are computationally more demanding.

Algorithms↗
PubMed2006

Integration of nucleic acid amplification test results into hepatitis C virus supplemental serologic testing algorithms: implications for donor counseling and revision of existing algorithms.

BACKGROUND: The routine use of hepatitis C virus (HCV) nucleic acid amplification testing (NAT) donor screening assays has provided an opportunity for revision of the current HCV supplemental testing algorithm, which requires that recombinant immunoblot assay (RIBA) be performed on every HCV enzyme immunoassay (EIA)-repeat-reactive donation. The FDA has approved variance requests to use a new algorithm that eliminates the need to perform RIBA when HCV NAT results are reactive. Data are provided in support of this new algorithm. STUDY DESIGN AND METHODS: HCV EIA (including signal-to-cutoff optical density ratio), RIBA, and NAT data were compiled from 33.2 million donations screened over an approximately 4-year period by the American Red Cross and Blood Systems Laboratories. Further, donations having specific combinations of HCV EIA, RIBA, and minipool (MP) NAT results were evaluated, with more sensitive individual-donation (ID) NAT, to construct improved counseling messages for donors. RESULTS: Of 47,041 EIA-repeat-reactive donations, 49.3 percent were RIBA-positive, 17.1 percent RIBA-indeterminate, and 33.5 percent RIBA-negative. NAT-reactive rates were 79.2, 2.5, and 0.18 percent for RIBA-positive, -indeterminate, and -negative donations, respectively. The new algorithm classified an additional 1 percent of donations as HCV-infected while at the same time detecting all infections classified as HCV-infected under the current algorithm. An additional 2.4 percent of RIBA-positive, MP NAT-nonreactive donations were reactive when a frozen-thawed aliquot was retested by ID NAT. CONCLUSION: Integrating HCV NAT results with RIBA results for purposes of donor notification allows more appropriate counseling messages to be given to EIA-repeat-reactive donors. The new HCV supplemental algorithm is an acceptable alternative to the current algorithm because it provides equivalent or superior accuracy in formulating donor counseling messages and may also result in reduced costs and more timely notification of infected donors.

Algorithms↗
PubMed2004

The Texas Medication Algorithm Project antipsychotic algorithm for schizophrenia: 2003 update.

BACKGROUND: The Texas Medication Algorithm Project (TMAP) has been a public-academic collaboration in which guidelines for medication treatment of schizophrenia, bipolar disorder, and major depressive disorder were used in selected public outpatient clinics in Texas. Subsequently, these algorithms were implemented throughout Texas and are being used in other states. Guidelines require updating when significant new evidence emerges; the antipsychotic algorithm for schizophrenia was last updated in 1999. This article reports the recommendations developed in 2002 and 2003 by a group of experts, clinicians, and administrators. METHOD: A conference in January 2002 began the update process. Before the conference, experts in the pharmacologic treatment of schizophrenia, clinicians, and administrators reviewed literature topics and prepared presentations. Topics included ziprasidone's inclusion in the algorithm, the number of antipsychotics tried before clozapine, and the role of first generation antipsychotics. Data were rated according to Agency for Healthcare Research and Quality criteria. After discussing the presentations, conference attendees arrived at consensus recommendations. Consideration of aripiprazole's inclusion was subsequently handled by electronic communications. RESULTS: The antipsychotic algorithm for schizophrenia was updated to include ziprasidone and aripiprazole among the first-line agents. Relative to the prior algorithm, the number of stages before clozapine was reduced. First generation antipsychotics were included but not as first-line choices. For patients refusing or not responding to clozapine and clozapine augmentation, preference was given to trying monotherapy with another antipsychotic before resorting to antipsychotic combinations. CONCLUSION: Consensus on algorithm revisions was achieved, but only further well-controlled research will answer many key questions about sequence and type of medication treatments of schizophrenia.

Algorithms↗
PubMed2002

Reconstruction of an object from its Fourier modulus: development of the combination algorithm composed of the hybrid input-output algorithm and its converging part.

The hybrid input-output algorithm (HIO) used for phase retrieval is in many cases combined with the error-reduction algorithm (ER) to attempt to stabilize the HIO. However, in our previous paper [J. Opt. Soc. Am. A 16, 2163 (1999)], it was demonstrated that this combination makes it more likely that the resultant algorithm will fall into a periodic state before reaching a solution because the values of the input object outside the support, which is imposed as the object-domain constraint, are set to be zero in the intervals in which the ER is implemented. This paper deals with this problem inherent in the combination algorithm. The converging part of the HIO (CPHIO), which is an algorithm we previously developed [J. Opt. Soc. Am. A 15, 2849 (1998)], can be thought of as an extension of the ER for the case in which the input object can have nonzero values outside the support. Keeping this in mind, the algorithm is then constructed by combining the HIO with the CPHIO instead of with the ER. The computer simulation results that demonstrate the effectiveness of the proposed algorithm are given.

Journal Article↗