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At least 1,297 records · Page 72Linked to original sources
PubMed1997

Empirical analyses of BOLD fMRI statistics. II. Spatially smoothed data collected under null-hypothesis and experimental conditions.

In the companion to this paper (E. Zarahn, G. K. Aguirre, and M. D'Esposito, 1997, NeuroImage, 179-197), we describe an implementation of a general linear model for autocorrelated observations in which the voxel-wise false-positive rates in fMRI "noise" datasets were stabilized and brought close to theoretical values. Here, implementations of the model are tested for use with statistical parametric mapping analysis of spatially smoothed fMRI data. Analyses using varying models of intrinsic temporal autocorrelation and either including or excluding a global signal covariate were conducted upon human subject data collected under null hypothesis as well as under experimental conditions. We found that smoothing with an empirically derived impulse response function (IRF), combined with a model of the intrinsic temporal autocorrelation in spatially smoothed fMRI data, resulted in a map-wise false-positive rate which did not exceed a 5% level when a nominal alpha = 0.05 tabular threshold was applied. Use of other models of intrinsic temporal autocorrelation resulted in map-wise false-positive rates that significantly exceeded this level. fMRI data collected while subjects performed a behavioral task were used to examine (a) task-dependent global signal changes and (b) the dependence of sensitivity on the temporal smoothing kernel and inclusion/exclusion of a global signal covariate. The global signal changes within an fMRI dataset were shown to be influenced by the performance of a behavioral task. However, the inclusion of this measure as a covariate did not have an adverse affect upon our measure of sensitivity. Finally, use of an empirically derived estimate of the IRF of the system was shown to result in greater map-wise sensitivity for signal changes than the use of a broader (in time) Poisson (parameter = 8 s) kernel.

Adult↗
PubMed2000

A new statistical approach to detecting significant activation in functional MRI.

There are many ways to detect activation patterns in a time series of observations at a single voxel in a functional magnetic resonance imaging study. The critical problem is to estimate the statistical significance, which depends on the estimation of both the magnitude of the response to the stimulus and the serial dependence of the time series and especially on the assumptions made in that estimation. We show that for experimental designs with periodic stimuli, only a few aspects of the serial dependence are important and these can be estimated reliably via nonparametric estimation of the spectral density of the time series, whereas existing techniques are biased by their assumptions. The linear model with (stationary) serially dependent errors can be analyzed entirely in frequency domain, and doing so provides many insights. In particular, we introduce a technique to detect periodic activations and show that it has a distribution theory that enables us to assign significance levels down to 1 in 100,000, levels which are needed when a whole brain image is under consideration. Nonparametric spectral density estimation is shown to be self-calibrating and accurate when compared to several other time-domain approaches. The technique is especially resistant to high frequency artefacts that we have found in some datasets and we demonstrate that time-domain approaches may be sufficiently susceptible to these effects to give misleading results. The method is easily generalized to handle event-related designs. We found it necessary to consider the trends in the time series carefully and use nonlinear filters to remove the trends and robust techniques to remove "spikes." Using this in connection with our techniques allows us to detect activations in clumps of a few (even one) voxel in periodic designs, yet produce essentially no false positive detections at any voxels in null datasets.

Acoustic Stimulation↗
PubMed2000

Automatic morphology-based brain segmentation (MBRASE) from MRI-T1 data.

A method called morphology-based brain segmentation (MBRASE) has been developed for fully automatic segmentation of the brain from T1-weighted MR image data. The starting point is a supervised segmentation technique, which has proven highly effective and accurate for quantitation and visualization purposes. The proposed method automates the required user interaction, i.e., defining a seed point and a threshold range, and is based on the simple operations thresholding, erosion, and geodesic dilation. The thresholds are detected in a region growing process and are defined by connections of the brain to other tissues. The method is first evaluated on three computer simulated datasets by comparing the automated segmentations with the original distributions. The second evaluation is done on a total of 30 patient datasets, by comparing the automated segmentations with supervised segmentations carried out by a neuroanatomy expert. The comparison between two binary segmentations is performed both quantitatively and qualitatively. The automated segmentations are found to be accurate and robust. Consequently, the proposed method can be used as a default segmentation for quantitation and visualization of the human brain from T1-weighted MR images in routine clinical procedures.

Adolescent↗
PubMed2002

An empirical comparison of SPM preprocessing parameters to the analysis of fMRI data.

We present the results from two sets of Monte Carlo simulations aimed at evaluating the robustness of some preprocessing parameters of SPM99 for the analysis of functional magnetic resonance imaging (fMRI). Statistical robustness was estimated by implementing parametric and nonparametric simulation approaches based on the images obtained from an event-related fMRI experiment. Simulated datasets were tested for combinations of the following parameters: basis function, global scaling, low-pass filter, high-pass filter and autoregressive modeling of serial autocorrelation. Based on single-subject SPM analysis, we derived the following conclusions that may serve as a guide for initial analysis of fMRI data using SPM99: (1) The canonical hemodynamic response function is a more reliable basis function to model the fMRI time series than HRF with time derivative. (2) Global scaling should be avoided since it may significantly decrease the power depending on the experimental design. (3) The use of a high-pass filter may be beneficial for event-related designs with fixed interstimulus intervals. (4) When dealing with fMRI time series with short interstimulus intervals (<8 s), the use of first-order autoregressive model is recommended over a low-pass filter (HRF) because it reduces the risk of inferential bias while providing a relatively good power. For datasets with interstimulus intervals longer than 8 seconds, temporal smoothing is not recommended since it decreases power. While the generalizability of our results may be limited, the methods we employed can be easily implemented by other scientists to determine the best parameter combination to analyze their data.

Algorithms↗
PubMed2005

Computer aided detection for low-dose CT colonography.

The paper describes a method for automatic detection of colonic polyps, robust enough to be directly applied to low-dose CT colonographic datasets. Polyps are modeled using gray level intensity profiles and extended Gaussian images. Spherical harmonic decompositions ensure an easy comparison between a polyp candidate and a set of polypoid models, found in a previously built database. The detection sensitivity and specificity values are evaluated at different dose levels. Starting from the original raw-data (acquired at 55mAs), 5 patient datasets (prone and supine scans) are reconstructed at different dose levels (down to 5mAs), using different kernel filters and slice increments. Although the image quality decreases when lowering the acquisition mAs, all polyps above 6mm are successfully detected even at 15 mAs. Accordingly the effective dose can be reduced from 4.93mSv to 1.61 mSv, without affecting detection capabilities, particularly important when thinking of population screening.

Algorithms↗
PubMed2005

Automatic segmentation of the left ventricle in 3D SPECT data by registration with a dynamic anatomic model.

We present a fully automatic 3D segmentation method for the left ventricle (LV) in human myocardial perfusion SPECT data. This model-based approach consists of 3 phases: 1. finding the LV in the dataset, 2. extracting its approximate shape and 3. segmenting its exact contour. Finding of the LV is done by flexible pattern matching, whereas segmentation is achieved by registering an anatomical model to the functional data. This model is a new kind of stable 3D mass spring model using direction-weighted 3D contour sensors. Our approach is much faster than manual segmention, which is standard in this application up to now. By testing it on 41 LV SPECT datasets of mostly pathological data, we could show, that it is very robust and its results are comparable with those made by human experts.

Artificial Intelligence↗
PubMed2005

Automatic initialization algorithm for carotid artery segmentation in CTA images.

Analysis of CT datasets is commonly time consuming because of the required manual interaction. We present a novel and fast automatic initialization algorithm to detect the carotid arteries providing a fully automated approach of the segmentation and centerline detection. First, the volume of interest (VOI) is estimated using a shoulder landmark. The carotid arteries are subsequently detected in axial slices of the VOI by applying a circular Hough transform. To select carotid arteries related signals in the Hough space, a 3-D, direction dependent hierarchical clustering is used. To allow a successful detection for a wide range of vessel diameters, a feedback architecture was introduced. The algorithm was designed and optimized using a training set of 20 patients and subsequently evaluated using 31 test datasets. The detection algorithm, including VOI estimation, correctly detects 88% of the carotid arteries. Even though not all carotid arteries have been correctly detected, the results are very promising.

Algorithms↗
PubMed2005

Robust pulmonary nodule segmentation in CT: improving performance for juxtapleural cases.

Two novel methods are proposed for robust segmentation of pulmonary nodules in CT images. The proposed solutions locate and segment a nodule in a semi-automatic fashion with a marker indicating the target. The solutions are motivated for handling the difficulty to segment juxtapleural, or wall-attached, nodules by using only local information without a global lung segmentation. They are realized as extensions of the recently proposed robust Gaussian fitting approach. Algorithms based on i) 3D morphological opening with anisotropic structuring element and ii) extended mean shift with a Gaussian repelling prior are presented. They are empirically compared against the robust Gaussian fitting solution by using a large clinical high-resolution CT dataset. The results show 8% increase, resulting in 95% correct segmentation rate for the dataset.

Algorithms↗
PubMed2005

Physiological recordings from electrodes implanted in the basal ganglia for deep brain stimulation in Parkinson's disease. the relevance of fast subthalamic rhythms.

Deep brain stimulation electrodes implanted in the subthalamic nucleus of patients with Parkinson's disease allow electrophysiological recordings from the human basal ganglia. Subthalamic local field potential recordings revealed the presence of multiple rhythms, from the classical EEG frequency range (<50 Hz), to surprisingly high frequencies (70 Hz and 300 Hz). Fast rhythms are particularly attractive because of their likely interaction with the excitatory mechanisms of action of deep brain stimulation. Here we investigated whether the two rhythms at 70 Hz and at 300 Hz represent distinct modes of operation, and therefore different targets, within the subthalamic nucleus. We retrospectively analyzed the dataset we used to describe the 300 Hz rhythm (Foffani, Priori et al., Brain 126: 2153-2163, 2003) searching for significant 70 Hz oscillations after levodopa administration. Whereas (as previously reported) 300 Hz activity was a consistent feature in the dataset, significant 70 Hz activity was observed in only 2 of 11 nuclei. Therefore, 70 Hz oscillations are not a necessary condition for the presence of 300 Hz oscillations. The two rhythms probably arise from different mechanisms, reflecting different functional and/or spatial aspects of subthalamic pathophysiology. Fast subthalamic oscillations could be exploited for intra-operative electrophysiological monitoring of the subthalamic nucleus, post-operative confirmation of electrode placement and patient-specific 'reglage' of the electrical parameters for chronic deep brain stimulation.

Basal Ganglia↗
PubMed1994

The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure.

A new simple empirical function has been developed that estimates the free energy of binding for a given protein-ligand complex of known 3D structure. The function takes into account hydrogen bonds, ionic interactions, the lipophilic protein-ligand contact surface and the number of rotatable bonds in the ligand. The dataset for the calibration of the function consists of 45 protein-ligand complexes. The new energy function reproduces the binding constants (ranging from 2.5.10(-2) to 4.10(-14) M, corresponding to binding energies between -9 and -76 kJ/mol) of the dataset with a standard deviation of 7.9 kJ/mol, corresponding to 1.4 orders of magnitude in binding affinity. The individual contributions to protein-ligand binding obtained from the scoring function are: ideal neutral hydrogen bond: -4.7 kJ/mol; ideal ionic interaction: -8.3 kJ/mol; lipophilic contact: -0.17 kJ/mol A2; one rotatable bond in the ligand: +1.4 kJ/mol. The function also contains a constant contribution (+5.4 kJ/mol) which may be rationalized as loss of translational and rotational entropy. The function can be evaluated very fast and is therefore also suitable for application in a 3D database search or de novo ligand design program such as LUDI.

Binding Sites↗
PubMed1993

Exploratory data analysis of hyperlipidemia on the Macintosh: software tools for analysis of biochemical, clinical, and genetic variables in 1677 consecutive lipid clinic patients.

Exploratory data analysis (EDA) software facilitates unstructured, iterative open exploration of complex datasets with the aid of multiple linked graphical displays. We are investigating relationships between plasma lipoproteins and coronary artery disease by retrospective analysis of 1677 consecutive UCSF Lipid Clinic patients. Our preliminary experience is with Data Deck 3.0 although several additional software programs (JMP 2.0, Systat 5.1, Minitab 8.0, StatView 4.0) are mentioned. Lipid diagnosis (751 women and 925 men) was 22% primary hypercholesterolemia, 19% combined hyperlipidemia, 3% dysbetalipoproteinemia, 15% endogenous lipemia, 4% mixed lipemia, 5% elevated Lp(a) and 32% with no major lipid abnormality. We found the Macintosh platform (68030) to be flexible and powerful for analysis of moderate size (less than 1 Mb) clinical datasets. High resolution color monitors (1024 x 768 pixels), fast hard disks (< 18 msec) and moderate amounts of system memory (8 + Mb) facilitate exploratory analysis.

Artificial Intelligence↗
PubMed1997

Recent results in color compositing of three-parameter magnetic resonance scans as a preoperative aid to the management of upper limb sarcomas.

We report on improved image quality and some recent clinical results with volume-rendered, color composite, multiparameter, magnetic resonance (MR) datasets and their color histograms. In a previous article we demonstrated the major components of an interactive visualization environment for multiparameter T1-, T2-, PD-weighted MR volume datasets. These are compositing of the three parametric volumes, color volume rendering of the resulting composite, interactive segmentation of the tissues based on color cluster statistics derived from display of the three-dimensional (3D) histogram, and head-tracking stereo display. We herein review the system design and describe improvements to the quality of the color volume rendering, in the display of the 3D color histogram data, and in the specification of the volume renderer's opacity function. Finally, we report on some recent clinical results using our system.

Arm↗
PubMedDate not supplied

Parameter estimation in distributed models of blood-tissue exchange: a Monte Carlo strategy to assess precision.

Distributed parameter models of blood-tissue exchange are increasingly used to interpret multiple tracer dilution data in regional kinetic studies. To derive a measure of the precision with which the model parameters are estimated is therefore of paramount importance. The standard approach to deriving precision of estimates does not take into account the fact that some of the model parameters are fixed. Thus, the precision of parameter estimates is not realistic and, in all likelihood, it is overestimated. The aim of this study is to describe a Monte Carlo method devised to obtain a theoretically sound measure of the precision of estimates, which takes into account both measurement error and the uncertainty associated with the fixed parameters. The fixed parameter values are taken from a probability distribution. By letting the fixed parameters vary according to their distribution, a large number of synthetic datasets is generated. Noise is then added. Estimating the parameters in each of these synthetic datasets allows the derivation of a Monte Carlo mean and standard deviation, which provides a realistic measure of precision. The methodology is illustrated for a simulated data case study dealing with the estimation of the capillary permeability-surface area product in a two tracer experiment.

Animals↗
PubMed2004

An efficient algorithm for mining closed itemsets.

This paper presents a new efficient algorithm for mining frequent closed itemsets. It enumerates the closed set of frequent itemsets by using a novel compound frequent itemset tree that facilitates fast growth and efficient pruning of search space. It also employs a hybrid approach that adapts search strategies, representations of projected transaction subsets, and projecting methods to the characteristics of the dataset. Efficient local pruning, global subsumption checking, and fast hashing methods are detailed in this paper. The principle that balances the overhead of search space growth and pruning is also discussed. Extensive experimental evaluations on real world and artificial datasets showed that our algorithm outperforms CHARM by a factor of five and is one to three orders of magnitude more efficient than CLOSET and MAFIA.

Algorithms↗
PubMed2000

Generation of deviation parameters for amino acid singlets, doublets and triplets from three-dimentional structures of proteins and its implications for secondary structure prediction from amino acid sequences.

We present a new method, secondary structure prediction by deviation parameter (SSPDP) for predicting the secondary structure of proteins from amino acid sequence. Deviation parameters (DP) for amino acid singlets, doublets and triplets were computed with respect to secondary structural elements of proteins based on the dictionary of secondary structure prediction (DSSP)-generated secondary structure for 408 selected non-homologous proteins. To the amino acid triplets which are not found in the selected dataset, a DP value of zero is assigned with respect to the secondary structural elements of proteins. The total number of parameters generated is 15,432, in the possible parameters of 25,260. Deviation parameter is complete with respect to amino acid singlets, doublets, and partially complete with respect to amino acid triplets. These generated parameters were used to predict secondary structural elements from amino acid sequence. The secondary structure predicted by our method (SSPDP) was compared with that of single sequence (NNPREDICT) and multiple sequence (PHD) methods. The average value of the percentage of prediction accuracy for a helix by SSPDP, NNPREDICT and PHD methods was found to be 57%, 44% and 69% respectively for the proteins in the selected dataset. For b-strand the prediction accuracy is found to be 69%, 21% and 53% respectively by SSPDP, NNPREDICT and PHD methods. This clearly indicates that the secondary structure prediction by our method is as good as PHD method but much better than NNPREDICT method.

Amino Acid Sequence↗
PubMed1999

A fast way to visualize the brain surface with volume rendering of MRI data.

The preprocessing of 3-dimensional (3D) MRI data constitutes a bottleneck in the process of visualizing the brain surface with volume rendering. As a fast way to achieve this preprocessing, the authors propose a simple pipeline based on an algorithm of seed-growing type, for approximate segmentation of the intradural space in T1-weighted 3D MRI data. Except for the setting of a seed and four parameters, this pipeline proceeds in an unsupervised manner; no interactive intermediate step is involved. It was tested with 15 datasets from normal adults. The result was reproducible in that as long as the seed was located within the cerebral white matter, identical segmentation was achieved for each dataset. Although the pipeline ran with gross segmentation error along the floor of the cranial cavity, it performed well along the cranial vault so that subsequent volume rendering permitted the observation of the sulco-gyral pattern over cerebral convexities. Use of this pipeline followed by volume rendering is a handy approach to the visualization of the brain surface from 3D MRI data.

Adult↗
PubMed2006

First experiences of radiation treatment planning with PET/CT.

BACKGROUND: Positron emission tomography/computed tomography (PET/CT) is composed of modern CT and PET technology in one machine enabling examinations of patients in one session in the same position. Its value for modern radiation treatment planning is under investigation. METHODS: In 53 patients with head-and-neck (n = 11), non-small cell lung (n = 16), prostate (n = 14) and other cancers (n = 12), a PET/CT investigation was performed. During the diagnostic examination process an integrated scan under radiation treatment-planning conditions was included. Interpretation and delineation of macroscopic tumor were done in an interdisciplinary approach. Treatment changes occurred after critical interpretation of the PET/CT findings by the responsible radiotherapist. Analysis is descriptive with regard to changes in treatment intention, mode, radiation volumes and doses. RESULTS: Examinations were well tolerated. CT datasets in treatment position could be used for planning. Delineation of macroscopic tumor led to changes of the planning target volume after PET/CT 15 times, total dose was modified twelve times. PET/CT examinations led to changes of the general treatment mode in 19 cases. Using the separate CT and PET datasets, fusion in the planning software was easily performed in all patients due to the use of the same positioning and immobilization devices in PET/CT. CONCLUSION: Despite the low number of patients and an expected bias of selection, the first results are encouraging to perform more extended and detailed trials of this technology in radiotherapy planning. Whether PET/CT is superior to PET alone is part of ongoing investigations.

Adult↗
PubMed2005

[Virtual multislice computed tomography cystoscopy for evaluation of urinary bladder lesions].

The introduction of multislice computed tomography (MDCT) with the possibility of acquiring isotropic datasets has been an ideal prerequisite for development of virtual MDCT cystoscopy. Remarkable technical progress regarding post-processing of high-resolution 3D datasets as well as a considerable reduction of the time required for post-processing made it possible to introduce virtual MDCT cystoscopy into the clinical routine. 3D post-processing that often required 7-8 h when virtual endoscopy techniques were first developed can now be performed in less than 5 min after transfer of data to the 3D workstation. With the limitations and contraindications of conventional cystoscopy in mind, virtual MDCT cystoscopy may be seen as a valuable alternative to conventional cystoscopy for evaluation of hematuria.

Cystoscopy↗