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At least 235 records · Page 13Linked to original sources
PubMed2001

Tracking tumor growth rates in patients with malignant gliomas: a test of two algorithms.

BACKGROUND AND PURPOSE: Two 3D image analysis algorithms, nearest-neighbor tissue segmentation and surface modeling, were applied separately to serial MR images in patients with glioblastoma multiforme (GBM). Rates of volumetric change were tracked for contrast-enhancing tumor tissue. Our purpose was to compare the two image analysis algorithms in their ability to track tumor volume relative to a manually defined standard of reference. METHODS: Three-dimensional T2-weighted and contrast-enhanced T1-weighted spoiled gradient-echo MR volumes were acquired in 10 patients with GBM. One of two protocols was observed: 1) a nearest-neighbor algorithm, which used manually determined or propagated tags and automatically segmented tissues into specific classes to determine tissue volume; or 2) a surface modeling algorithm, which used operator-defined contrast-enhancing boundaries to convert traced points into a parametric mesh model. Volumes were automatically calculated from the mesh models. Volumes determined by each algorithm were compared with the standard of reference, generated by manual segmentation of contrast-enhancing tissue in each cross section of a scan. RESULTS: Nearest-neighbor algorithm enhancement volumes were highly correlated with manually segmented volumes, as were growth rates, which were measured in terms of halving and doubling times. Enhancement volumes generated by the surface modeling algorithm were also highly correlated with the standard of reference, although growth rates were not. CONCLUSION: The nearest-neighbor tissue segmentation algorithm provides significant power in quantifying tumor volume and in tracking growth rates of contrast-enhancing tissue in patients with GBM. The surface modeling algorithm is able to quantify tumor volume reliably as well.

Adolescent↗
PubMed1995

Closed-line integral optimization edge detection algorithm and its application in equilibrium radionuclide angiocardiography.

UNLABELLED: Automatic evaluation of left ventricular (LV) function using equilibrium radionuclide angiocardiography requires an edge detection algorithm to correct and reproducibly delineate the left ventricle. Available algorithms, usually based on differentiation of a radial profile, generally suffer from low precision due to low signal-to-noise ratios and overlapping structures, for example, the left atrium. METHODS: An edge detection algorithm was developed based on the assumption that the LV border can be defined as the maximum, normalized, closed-line integral of a closed curve in a vector field derived by image differentiation. It is further assumed that the closed curve can be described by a Fourier expansion with a limited number of harmonics. Regions of interest (ROIs) generated by this algorithm were compared with ROIs generated by an algorithm based on a combination of thresholding and second-order derivatives. RESULTS: This algorithm delineates the left ventricle and gives results more closely related to ROIs generated manually than the algorithm combining thresholding and the second-order derivative. Our algorithm can also handle the problem of overlapping structures, as demonstrated in phantom simulations. CONCLUSION: The concept of a maximum, normalized closed-line integral will improve the delineation of the LV in an equilibrium radionuclide angiocardiography study. The problem of overlapping structures is overcome by this algorithm because it takes into consideration global edge information.

Algorithms↗
PubMed1994

Detection of nodular lesions in the lung helical computed tomography: comparison between 360 degrees and 180 degrees reconstruction algorithms.

The present study has evaluated helical computed tomography (HS-CT) performed at a fast couch-top sliding speed (20 mm/sec, sufficient to cover the entire lung field during the holding of a single breath) with regard to its ability to detect pulmonary nodules in patients with metastatic lung disease. The detectability of lesions by HS-CT was compared between 360 degrees and 180 degrees reconstruction algorithms. The subjects were eight consecutive patients with metastatic lung tumors, in whom 214 lesions had been detected by conventional CT. The average diameter of the nodules detected was 7.7 mm (2-22 mm). To scan the entire lung, we first used conventional CT (10-mm slice thickness, 10-mm stepping) and then HS-CT with a couch-top speed of 20 mm/sec and a tube current of 50 mA. Both 360 degrees and 180 degrees algorithms were used to reconstruct images at intervals of 20 and 10 mm from HS-CT data. Detection rates of 79% for 20-mm images and 94% for 10-mm images were achieved with the 360 degrees algorithm. Detection rates of 81% for 20-mm images and 99% for 10-mm images were achieved with the 180 degrees algorithm. The detection of nodules was greater for 10-mm images than 20-mm images (P < 0.01). There was no significant difference between the 360 degrees and 180 degrees algorithms. Images acquired using the 180 degrees algorithm, however, depicted lesions with greater sharpness than those acquired using the 360 degrees algorithm. It is presumed that they indirectly show the superiority of the 180 degrees algorithm over the 360 degrees algorithm. HS-CT should prove a useful screening technique for lesions in the lung field.

Aged↗
PubMed2003

An optimal algorithm for configuring delivery options of a one-dimensional intensity-modulated beam.

The problem of generating delivery options for one-dimensional intensity-modulated beams (1D IMBs) arises in intensity-modulated radiation therapy. In this paper, we present an algorithm with the optimal running time, based on the 'rightmost-preference' method, for generating all distinct delivery options for an arbitrary 1D IMB. The previously best known method for generating delivery options for a 1D IMB with N left leaf positions and N right leaf positions is a 'brute-force' solution, which first generates all N! possible combinations of the left and right leaf positions and then removes combinations that are not physically allowed delivery options. Compared with the brute-force method, our algorithm has several advantages: (1) our algorithm runs in an optimal time that is linearly proportional to the total number of distinct delivery options that it actually produces. Note that for a 1D IMB with multiple peaks, the total number of distinct delivery options in general tends to be considerably smaller than the worst case N!. (2) Our algorithm can be adapted to generating delivery options subject to additional constraints such as the 'minimum leaf separation' constraint. (3) Our algorithm can also be used to generate random subsets of delivery options; this feature is especially useful when the 1D IMBs in question have too many delivery options for a computer to store and process. The key idea of our method is that we impose an order on how left leaf positions should be paired with right leaf positions. Experiments indicated that our rightmost-preference algorithm runs dramatically faster than the brute-force algorithm. This implies that our algorithm can handle 1D IMBs whose sizes are substantially larger than those handled by the brute-force method. Applications of our algorithm in therapeutic techniques such as intensity-modulated arc therapy and 2D modulations are also discussed.

Computer Simulation↗
PubMed2002

Phase-shifting algorithms for electronic speckle pattern interferometry.

A set of innovative phase-shifting algorithms developed to facilitate metrology based on electronic speckle pattern interferometry (ESPI) are presented. The theory of a phase-shifting algorithm, called a (5,1) algorithm, that takes five phase-shifted intensity maps before a specimen is deformed and one intensity map after a specimen is deformed is presented first. Because a high-speed camera can be used to record the dynamic image of the specimen, this newly developed algorithm has the potential to retain the phase-shifting capability for ESPI in dynamic measurements. Also shown is an algorithm called a (1,5) algorithm that takes five phase-shifted intensity maps after the specimen is deformed. In addition, a direct-correlation algorithm was integrated with these newly developed (5,1) or (1,5) algorithms to form DC-(5,1) and DC-(1,5) algorithms, which are shown to improve significantly the quality of the phase maps. The theoretical and experimental aspects of these two newly developed techniques, which can extend ESPI to areas such as high-speed dynamic measurements, are examined in detail.

Journal Article↗
PubMed2002

Phase retrieval, error reduction algorithm, and Fienup variants: a view from convex optimization.

The phase retrieval problem is of paramount importance in various areas of applied physics and engineering. The state of the art for solving this problem in two dimensions relies heavily on the pioneering work of Gerchberg, Saxton, and Fienup. Despite the widespread use of the algorithms proposed by these three researchers, current mathematical theory cannot explain their remarkable success. Nevertheless, great insight can be gained into the behavior, the shortcomings, and the performance of these algorithms from their possible counterparts in convex optimization theory. An important step in this direction was made two decades ago when the error reduction algorithm was identified as a nonconvex alternating projection algorithm. Our purpose is to formulate the phase retrieval problem with mathematical care and to establish new connections between well-established numerical phase retrieval schemes and classical convex optimization methods. Specifically, it is shown that Fienup's basic input-output algorithm corresponds to Dykstra's algorithm and that Fienup's hybrid input-output algorithm can be viewed as an instance of the Douglas-Rachford algorithm. We provide a theoretical framework to better understand and, potentially, to improve existing phase recovery algorithms.

Journal Article↗
PubMed1999

Evaluation of the WHO/UNICEF algorithm for integrated management of childhood illness between the age of two months to five years.

OBJECTIVE: To evaluate the utility of the "WHO/UNICEF algorithm for integrated management of childhood illness (IMCI) between the age of 2 months to 5 years. DESIGN: Prospective observational. SETTING: The Outpatient Department and Emergency Room of a medical college hospital. METHODS: 203 children presenting to Outpatient Department (n=101) or Emergency Room (n=102) were assessed and classified as per 'IMCI' algorithm and treatment required was identified. A detailed evaluation with all relevant investigations was also done for these subjects. The final diagnoses made and therapies instituted on this basis served as 'gold standard'. The diagnostic and therapeutic agreements between the 'gold standard' and the IMCI and vertical (on the basis of primary presenting complaint) algorithms were computed. RESULTS: More than one illness was present in 135 (66.5%) of subjects as per 'gold standard'. The mean (SD) numbers of morbidities as per the gold standard and IMCI- low and high malaria risks were 2.1 (1.1), 1.8 (1.0) and 2.2 (1.1), respectively. Subjects having any referral criteria as per IMCI module had a greater co-existence of illnesses (mean 2.6 vs. 1.6 illnesses per child, respectively). The referral criteria proved useful in predicting hospitalization and a combination of hospitalization and observation; their sensitivity and specificity were 81% and 69% and 74% and 85%, respectively. IMCI algorithms covered majority (92%) of the recorded illnesses. A total agreement with IMCI (malaria low risk) was found in 129 (64%) cases while in 43 (22%) cases, there was partial agreement. Corresponding figures for vertical (split IMCI) program were 93 (46%; p<0.001) and 41 (25%). The difference was primarily due to underdiagnoses (30%). Diagnostic discordance of IMCI algorithm and gold standard was evident for the cough category due to underdiagnosis of bronchial asthma and bronchiolitis and an overdiagnosis of pneumonia whereas the discordance for fever was due to an overdiagnosis of malaria. Identical results were found for broad treatment categories. The IMCI algorithm had a provision for preventive services of immunization (16.3% possibility of availing missed opportunities) and feeding advice. CONCLUSIONS: There is a sound scientific basis for adopting the IMCI approach since: (i) co-existence of morbidities is frequent; (ii) severe illness is assessed with good sensitivity and specificity; and (iii) the IMCI algorithm is diagnostically and therapeutically superior to the vertical disease specific algorithms. The generic IMCI algorithm needs adaptation to reflect the regional morbidity profile.

Child Health Services↗
PubMed1983

Uses of clinical algorithms.

The clinical algorithm (flow chart) is a text format that is specially suited for representing a sequence of clinical decisions, for teaching clinical decision making, and for guiding patient care. A representative clinical algorithm is described in detail; five steps for writing an algorithm and seven steps for writing a set of algorithms are outlined. Five clinical education and patient care uses of algorithms are then discussed, including a map for teaching clinical decision making and protocol charts for guiding step-by-step care of specific problems. Clinical algorithms are compared as to their clinical usefulness with decision analysis. Three objections to clinical algorithms are answered, including the one that they restrict thinking. It is concluded that methods should be sought for writing clinical algorithms that represent expert consensus. A clinical algorithm could then be written for any area of medical decision making that can be standardized. Medical practice could then be taught more effectively, monitored accurately, and understood better.

Clinical Competence↗
PubMed2025

Quantifying and improving rheumatoid arthritis algorithm performance in biobank settings.

OBJECTIVE: To quantify and improve the performance of standard rheumatoid arthritis (RA) algorithms in a biobank setting. METHODS: This retrospective cohort study within the Mayo Clinic (MC) Biobank and MC Tapestry Study identified RA cases by presence of at least two RA codes OR positive anti-cyclic citrullinated peptide antibodies (CCP) plus disease-modifying anti-rheumatic drug (DMARD) prescription as of 7/18/2022. Rheumatology physicians manually verified all RA cases using RA criteria and/or rheumatology physician diagnosis plus DMARD use. All other biobank participants served as non-RA controls. We defined seropositivity as rheumatoid factor and/or anti-CCP positivity. We assessed rules-based and Electronic Medical Records and Genomics (eMERGE) RA algorithms using positive predictive value (PPV). Finally, we developed a novel RA algorithm using a LASSO-based machine learning approach with five-fold cross validation. RESULTS: We identified 1,316 confirmed RA cases (968 MC Biobank, 348 Tapestry, 70 % seropositive) and 82,123 non-RA controls (mean age 65, 61 % female). The PPV of 3 RA codes was 43 %, codes plus DMARD was 54 %, and codes plus DMARD plus seropositivity was 85 %. The PPV of eMERGE was 77 %. Available in the MC Biobank, self-reported RA (PPV 10 %) only minimally improved algorithm performance (PPV from 83 % to 85 %), whereas family history of RA (PPV 3 %) worsened performance. At 90 % PPV, the novel RA algorithm incorporating key variables such as anti-CCP and DMARD use increased sensitivity by 4-11 % compared to eMERGE. CONCLUSION: Rules-based and eMERGE RA algorithms had worse performance in biobank than administrative settings. Our novel RA algorithm outperformed these standard algorithms.

Humans↗
PubMed1997

Applying watershed algorithms to the segmentation of clustered nuclei.

Cluster division is a critical issue in fluorescence microscopy-based analytical cytology when preparation protocols do not provide appropriate separation of objects. Overlooking clustered nuclei and analyzing only isolated nuclei may dramatically increase analysis time or affect the statistical validation of the results. Automatic segmentation of clustered nuclei requires the implementation of specific image segmentation tools. Most algorithms are inspired by one of the two following strategies: 1) cluster division by the detection of internuclei gradients; or 2) division by definition of domains of influence (geometrical approach). Both strategies lead to completely different implementations, and usually algorithms based on a single view strategy fail to correctly segment most clustered nuclei, or perform well just for a specific type of sample. An algorithm based on morphological watersheds has been implemented and tested on the segmentation of microscopic nuclei clusters. This algorithm provides a tool that can be used for the implementation of both gradient- and domain-based algorithms, and, more importantly, for the implementation of mixed (gradient- and shape-based) algorithms. Using this algorithm, almost 90% of the test clusters were correctly segmented in peripheral blood and bone marrow preparations. The algorithm was valid for both types of samples, using the appropriate markers and transformations.

Algorithms↗
PubMed2001

Comparison of five clustering algorithms to classify phytoplankton from flow cytometry data.

BACKGROUND: Artificial neural networks (ANNs) have been shown to be valuable in the analysis of analytical flow cytometric (AFC) data in aquatic ecology. Automated extraction of clusters is an important first stage in deriving ANN training data from field samples, but AFC data pose a number of challenges for many types of clustering algorithm. The fuzzy k-means algorithm recently has been extended to address nonspherical clusters with the use of scatter matrices. Four variants were proposed, each optimizing a different measure of clustering "goodness." METHODS: With AFC data obtained from marine phytoplankton species in culture, the four fuzzy k-means algorithm variants were compared with each other and with another multivariate clustering algorithm based on critical distances currently used in flow cytometry. RESULTS: One of the algorithm variants (adaptive distances, also known as the Gustafson--Kessel algorithm) was found to be robust and reliable, whereas the others showed various problems. CONCLUSIONS: The adaptive distances algorithm was superior in use to the clustering algorithms against which it was tested, but the problem of automatic determination of the number of clusters remains to be addressed.

Algorithms↗
PubMed2002

Validation of a biopsy-based pathologic algorithm for predicting lymph node metastases in patients with clinically localized prostate carcinoma.

BACKGROUND: The authors validated an algorithm for the preoperative prediction of lymph node (LN) metastases in patients with clinically localized prostate carcinoma. The algorithm was applied to sextant biopsy material and radical retropubic prostatectomy (RRP) stage obtained from a cohort of men who were treated at the authors' institution. METHODS: Four hundred forty-three patients underwent systematic sextant biopsy and RRP with staging lymphadenectomy. The original algorithm was based on systematic sextant biopsy data and classified patients into three risk groups for LN metastases based on the biopsy result. If > or = 4 of 6 biopsies contained any Gleason Pattern 4 disease, then the patient was at high risk for LN metastases (45%). Patients with > or = 1 of 6 biopsies with dominant Gleason Pattern 4 disease (excluding high-risk patients) had an intermediate predicted risk (19%) of LN metastases. All other patients had a low predicted risk of LN metastases (2.2%). The authors assed the percentage of patients who were positive and negative for LN metastases and calculated the specificity and negative predictive value in the series when patients were classified according to the original algorithm. RESULTS: Twenty of 443 patients had intraoperative LN metastases. When applied to the current data, the Hamburg algorithm classified 404 patients in the low-risk group, 30 patients in the intermediate-risk group, and 9 patients in the high risk group. The incidence of LN metastases was 2.47% in the low-risk group, 20% in the intermediate-risk group, and 44.4% in the high-risk group. The negative predictive value for the low-risk group was 97.52%, and the specificity was 94.14%. CONCLUSIONS: The Hamburg algorithm proved a valid tool for the prediction of lymphatic spread in this validation study on data from the authors' institution. The algorithm may serve as a tool to select patients who do not need to undergo pelvic lymphadenectomy at the time they undergo RRP, hence reducing morbidity and expense. More importantly, with the increasing numbers of men undergoing treatment options in whom LN dissection is not performed, this validated algorithm provides an important selection basis regarding the appropriateness of a therapy that does not routinely include LN staging.

Algorithms↗
PubMed2004

Algorithms for inferring haplotypes.

Haplotype phase information in diploid organisms provides valuable information on human evolutionary history and may lead to the development of more efficient strategies to identify genetic variants that increase susceptibility to human diseases. Molecular haplotyping methods are labor-intensive, low-throughput, and very costly. Therefore, algorithms based on formal statistical theories were shown to be very effective and cost-efficient for haplotype reconstruction. This review covers 1) population-based haplotype inference methods: Clark's algorithm, expectation-maximization (EM) algorithm, coalescence-based algorithms (pseudo-Gibbs sampler and perfect/imperfect phylogeny), and partition-ligation algorithm implemented by a fully Bayesian model (Haplotyper) or by EM (PLEM); 2) family-based haplotype inference methods; 3) the handling of genotype scoring uncertainties (i.e., genotyping errors and raw two-dimensional genotype scatterplots) in inferring haplotypes; and 4) haplotype inference methods for pooled DNA samples. The advantages and limitations of each algorithm are discussed. By using simulations based on empirical data on the G6PD gene and TNFRSF5 gene, I demonstrate that different algorithms have different degrees of sensitivity to various extents of population diversities and genotyping error rates. Future development of statistical algorithms for addressing haplotype reconstruction will resort more and more to ideas based on combinatorial mathematics, graphical models, and machine learning, and they will have profound impacts on population genetics and genetic epidemiology with the advent of the human HapMap.

Algorithms↗
PubMed1996

An algorithm for the grading of activity in chronic hepatitis C. The METAVIR Cooperative Study Group.

Histological activity reflects the global assessment of basic necroinflammatory lesions and is a criterion of major importance in chronic hepatitis C. The aim of this study was to propose and test the accuracy of a simple algorithm that generates a single activity score based on basic pathological features. A panel of 10 pathologists reviewed 363 chronic hepatitis C liver biopsies and graded the activity of hepatitis according to their own experience (reference activity). Then, a consensual algorithm on the grading of activity was established by the 10 experts in a panel discussion. Finally, stepwise discriminant analysis was performed to define which basic features had been intuitively used in the reference activity (statistical activity). To test the accuracy of the algorithm, concordance between the activity defined by the algorithm and the reference activity was assessed. It was compared with concordance between the activity defined by the statistical model and the reference activity. The algorithm proposed by the panel for the grading of activity included piecemeal necrosis and lobular necrosis. Concordance between reference activity and activity defined by the algorithm was substantial (305 cases, 84%, kappa = .75). Discriminant analysis showed that piecemeal necrosis, lobular necrosis, and portal inflammation were independently used to grade the activity. Concordance between reference activity and activity defined by the statistical model was substantial (300 cases, 83%, kappa = .73), virtually identical to the concordance between reference activity and activity defined by algorithm. This study proposes a simple algorithm for the grading of activity in chronic hepatitis. Its accuracy is as high as that obtained using a statistical approach.

Algorithms↗
PubMed2005

An evaluation, comparison, and accurate benchmarking of several publicly available MS/MS search algorithms: sensitivity and specificity analysis.

MS/MS and associated database search algorithms are essential proteomic tools for identifying peptides. Due to their widespread use, it is now time to perform a systematic analysis of the various algorithms currently in use. Using blood specimens used in the HUPO Plasma Proteome Project, we have evaluated five search algorithms with respect to their sensitivity and specificity, and have also accurately benchmarked them based on specified false-positive (FP) rates. Spectrum Mill and SEQUEST performed well in terms of sensitivity, but were inferior to MASCOT, X!Tandem, and Sonar in terms of specificity. Overall, MASCOT, a probabilistic search algorithm, correctly identified most peptides based on a specified FP rate. The rescoring algorithm, PeptideProphet, enhanced the overall performance of the SEQUEST algorithm, as well as provided predictable FP error rates. Ideally, score thresholds should be calculated for each peptide spectrum or minimally, derived from a reversed-sequence search as demonstrated in this study based on a validated data set. The availability of open-source search algorithms, such as X!Tandem, makes it feasible to further improve the validation process (manual or automatic) on the basis of "consensus scoring", i.e., the use of multiple (at least two) search algorithms to reduce the number of FPs. complement.

Algorithms↗
PubMed2002

Performance evaluation of a new algorithm for the detection of remote homologs with sequence comparison.

A detailed analysis of the performance of hybrid, a new sequence alignment algorithm developed by Yu and coworkers that combines Smith Waterman local dynamic programming with a local version of the maximum-likelihood approach, was made to access the applicability of this algorithm to the detection of distant homologs by sequence comparison. We analyzed the statistics of hybrid with a set of nonhomologous protein sequences from the SCOP database and found that the statistics of the scores from hybrid algorithm follows an Extreme Value Distribution with lambda approximately 1, as previously shown by Yu et al. for the case of artificially generated sequences. Local dynamic programming was compared to the hybrid algorithm by using two different test data sets of distant homologs from the PFAM and COGs protein sequence databases. The studies were made with several score functions in current use including OPTIMA, a new score function originally developed to detect remote homologs with the Smith Waterman algorithm. We found OPTIMA to be the best score function for both both dynamic programming and the hybrid algorithms. The ability of dynamic programming to discriminate between homologs and nonhomologs in the two sets of distantly related sequences is slightly better than that of hybrid algorithm. The advantage of producing accurate score statistics with only a few simulations may overcome the small differences in performance and make this new algorithm suitable for detection of homologs in conjunction with a wide range of score functions and gap penalties.

Algorithms↗
PubMed2002

Improved Lanczos algorithms for blackbox MRS data quantitation.

Magnetic resonance spectroscopy (MRS) has been shown to be a potentially important medical diagnostic tool. The success of MRS depends on the quantitative data analysis, i.e., the interpretation of the signal in terms of relevant physical parameters, such as frequencies, decay constants, and amplitudes. A variety of time-domain algorithms to extract parameters have been developed. On the one hand, there are so-called blackbox methods. Minimal user interaction and limited incorporation of prior knowledge are inherent to this type of method. On the other hand, interactive methods exist that are iterative, require user involvement, and allow inclusion of prior knowledge. We focus on blackbox methods. The computationally most intensive part of these blackbox methods is the computation of the singular value decomposition (SVD) of a Hankel matrix. Our goal is to reduce the needed computational time without affecting the accuracy of the parameters of interest. To this end, algorithms based on the Lanczos method are suitable because the main computation at each step, a matrix-vector product, can be efficiently performed by means of the fast Fourier transform exploiting the structure of the involved matrix. We compare the performance in terms of accuracy and efficiency of four algorithms: the classical SVD algorithm based on the QR decomposition, the Lanczos algorithm, the Lanczos algorithm with partial reorthogonalization, and the implicitly restarted Lanczos algorithm. Extensive simulation studies show that the latter two algorithms perform best.

Algorithms↗
PubMed2001

Qualitative and quantitative evaluation of six algorithms for correcting intensity nonuniformity effects.

The desire to correct intensity nonuniformity in magnetic resonance images has led to the proliferation of nonuniformity-correction (NUC) algorithms with different theoretical underpinnings. In order to provide end users with a rational basis for selecting a given algorithm for a specific neuroscientific application, we evaluated the performance of six NUC algorithms. We used simulated and real MRI data volumes, including six repeat scans of the same subject, in order to rank the accuracy, precision, and stability of the nonuniformity corrections. We also compared algorithms using data volumes from different subjects and different (1.5T and 3.0T) MRI scanners in order to relate differences in algorithmic performance to intersubject variability and/or differences in scanner performance. In phantom studies, the correlation of the extracted with the applied nonuniformity was highest in the transaxial (left-to-right) direction and lowest in the axial (top-to-bottom) direction. Two of the six algorithms demonstrated a high degree of stability, as measured by the iterative application of the algorithm to its corrected output. While none of the algorithms performed ideally under all circumstances, locally adaptive methods generally outperformed nonadaptive methods.

Algorithms↗