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B A Ardekani

Publications and source records attributed to B A Ardekani.

14 recordsLinked to original sources
PubMed2004

Application of a non-linear image registration algorithm to quantitative analysis of T2 relaxation time in transgenic mouse models of AD pathology.

Transgenic mouse models have been essential for understanding the pathogenesis of Alzheimer's disease (AD) including those that model the deposition process of beta-amyloid (Abeta). Several laboratories have focused on research related to the non-invasive detection of early changes in brains of transgenic mouse models of Alzheimer's pathology. Most of this work has been performed using regional image analysis of individual mouse brains and pooling the results for statistical assessment. Here we report the implementation of a non-linear image registration algorithm to register anatomical and transverse relaxation time (T2) maps estimated from MR images of transgenic mice. The algorithm successfully registered mouse brain magnetic resonance imaging (MRI) volumes and T2 maps, allowing reliable estimates of T2 values for different regions of interest from the resultant combined images. This approach significantly reduced the data processing and analysis time, and improved the ability to statistically discriminate between groups. Additionally, 3D visualization of intra-regional distributions of T2 of the resultant registered images provided the ability to detect small changes between groups that otherwise would not be possible to detect.

Algorithms↗
PubMed2001

A quantitative comparison of motion detection algorithms in fMRI.

An important step in the analysis of fMRI time-series data is to detect, and as much as possible, correct for subject motion during the course of the scanning session. Several public domain algorithms are currently available for motion detection in fMRI. This paper compares the performance of four commonly used programs: AIR 3.08, SPM99, AFNI98, and the pyramid method of Thévenaz, Ruttimann, and Unser (TRU). The comparison is based on the performance of the algorithms in correcting a range of simulated known motions in the presence of various degrees of noise. SPM99 provided the most accurate motion detection amongst the algorithms studied. AFNI98 provided only slightly less accurate results than SPM99, however, it was several times faster than the other programs. This algorithm represents a good compromise between speed and accuracy. AFNI98 was also the most robust program in presence of noise. It yielded reasonable results for very low signal to noise levels. For small initial misalignments, TRU's performance was similar to SPM99 and AFNI98. However, its accuracy diminished rapidly for larger misalignments. AIR was found to be the least accurate program studied.

Algorithms↗
PubMed2000

Application of PET-MRI registration techniques to cat brain imaging.

UNLABELLED: In positron emission tomography (PET) studies of diseased animals, it is very useful to have accurate anatomical information as a reference. In human studies, anatomical information is usually obtained from magnetic resonance imaging (MRI) of the subject with retrospective registration of the subject's PET image to the MRI. A number of PET-MRI registration techniques are used for this purpose. However, the utility of these methods has not been tested for animals image registration. This paper studies the feasibility of applying two currently used human brain PET-MRI registration techniques to cat brain images. METHODS: Three cats were anesthetized with isoflurane gas, and PET images were acquired with H(2)(15)O, benzodiazepine receptor ligand 11C-flumazemil (FMZ), dopamine receptor ligand 11C-nemonapride (NEM) and fluorodeoxy glucose (18F-FDG). The four PET scans were acquired consecutively within the same day while the cat remained fixed in the scanner. We also obtained T1-weighted and T2-weighted MRI of the cats in a 4.7 T unit. The PET images were registered to MRI using two human brain registration techniques: a semi-automatic method (SAM), which is a two-step method based on the extraction of the midsagittal plane, and an automatic method (AMIR) method that minimizes PET pixel variance within spatially connected segments determined by MRI. RESULTS: T2-weighted MRI provided better structural information than T1 MRI. FMZ did, while FDG or H(2)O PET images did not, provide a structural outline of the brain. The FMZ PET image was registered to MRI satisfactorily using SAM. The striatum visualized in nemonapride PET image re-sliced with the same parameters matched the striatum identified in T2-weighted MRI. Registration by AMIR was successful by inspection for FMZ, FDG or H(2)O PET images in only one of the three cats. The registration error of SAM was estimated to be less than 2 mm or 2 degrees. CONCLUSION: A satisfactory registration of FMZ-PET to T2-weighted MRI of the cat brain was obtained by a two-step manual registration technique. This will enhance the usefulness of PET in the field of cerebral pathophysiology.

Animals↗
PubMed1999

Activation detection in functional MRI using subspace modeling and maximum likelihood estimation.

A statistical method for detecting activated pixels in functional MRI (fMIRI) data is presented. In this method, the fMRI time series measured at each pixel is modeled as the sum of a response signal which arises due to the experimentally controlled activation-baseline pattern, a nuisance component representing effects of no interest, and Gaussian white noise. For periodic activation-baseline patterns, the response signal is modeled by a truncated Fourier series with a known fundamental frequency but unknown Fourier coefficients. The nuisance subspace is assumed to be unknown. A maximum likelihood estimate is derived for the component of the nuisance subspace which is orthogonal to the response signal subspace. An estimate for the order of the nuisance subspace is obtained from an information theoretic criterion. A statistical test is derived and shown to be the uniformly most powerful (UMP) test invariant to a group of transformations which are natural to the hypothesis testing problem. The maximal invariant statistic used in this test has an F distribution. The theoretical F distribution under the null hypothesis strongly concurred with the experimental frequency distribution obtained by performing null experiments in which the subjects did not perform any activation task. Application of the theory to motor activation and visual stimulation fMRI studies is presented.

Adult↗
PubMed1999

Application of Bayesian inference to fMRI data analysis.

The methods of Bayesian statistics are applied to the analysis of fMRI data. Three specific models are examined. The first is the familiar linear model with white Gaussian noise. In this section, the Jeffreys' Rule for noninformative prior distributions is stated and it is shown how the posterior distribution may be used to infer activation in individual pixels. Next, linear time-invariant (LTI) systems are introduced as an example of statistical models with nonlinear parameters. It is shown that the Bayesian approach can lead to quite complex bimodal distributions of the parameters when the specific case of a delta function response with a spatially varying delay is analyzed. Finally, a linear model with auto-regressive noise is discussed as an alternative to that with uncorrelated white Gaussian noise. The analysis isolates those pixels that have significant temporal correlation under the model. It is shown that the number of pixels that have a significantly large auto-regression parameter is dependent on the terms used to account for confounding effects.

Artifacts↗
PubMed1999

Cerebral blood volume in acute brain infarction: A combined study with dynamic susceptibility contrast MRI and 99mTc-HMPAO-SPECT.

BACKGROUND AND PURPOSE: The aim of this study was to correlate the abnormality in cerebral blood volume (CBV) measured by dynamic susceptibility contrast-enhanced MRI with that in cerebral blood flow (CBF) estimated by single-photon emission CT with [99mTc]hexamethylpropylenamine-oxime in patients with acute ischemic stroke. METHODS: Nine patients with unilateral occlusion of either the middle cerebral artery or the internal carotid artery (4 men and 5 women; mean+/-SD age, 74.4+/-11.6 years) were studied within 6 hours after stroke onset. The relative CBV (relCBV) and CBF (relCBF) in the lesions were defined relative to the contralateral mirror regions. RESULTS: In the brain regions with mild (relCBF >/=0.60), moderate (0.40</=relCBF<0.60), and severe (relCBF <0.40) hypoperfusion, the mean relCBV values were 1.29+/-0.31, 0.94+/-0.49, and 0.30+/-0.22, respectively. The relCBV was significantly elevated in the brain areas with mild hypoperfusion (P<0.001) and significantly reduced in the brain areas with severe hypoperfusion (P<0.001). The relCBF was significantly better than the relCBV in predicting the evolution of infarction (P<0.02). The probability of evolving infarction for the hypervolemic (relCBV >1.0) regions was significantly lower than that for hypovolemic (relCBV <1.0) regions in the relCBF range between 0.40 and 0.50 (P<0.02). CONCLUSIONS: In acute ischemic stroke within 6 hours of onset the CBV can be either increased, normal, or decreased, depending on the severity of hypoperfusion. The increased CBV has a protective effect on evolving infarction. Although the CBF is a better predictor of tissue outcome, the CBV measurement may help detect potentially salvageable brain tissue in the penumbra with compromised blood flow.

Acute Disease↗
PubMed1998

Statistical methods for detecting activated regions in functional MRI of the brain.

Two statistical tests for detecting activated pixels in functional MRI (fMRI) data are presented. The first test (t-test) is the optimal solution to the problem of detecting a known activation signal in Gaussian white noise. The results of this test are shown to be equivalent to the cross-correlation method that is widely used for activation detection in fMRI. The second test (F test) is the optimal solution when the measured data are modeled to consist of an unknown activation signal that lies in a known lower dimensional subspace of the measurement space with added Gaussian white noise. A model for the signal subspace based on a truncated trigonometric Fourier series is proposed for periodic activation-baseline imaging paradigms. The advantage of the second method is that it does not assume any information about the shape or delay of the activation signal, except that it is periodic with the same period as the activation-baseline pattern. The two models are applied to experimental echo-planar fMRI data sets and the results are compared.

Algorithms↗
PubMed1997

Automatic detection of the mid-sagittal plane in 3-D brain images.

This article presents a detailed description of an algorithm for the automatic detection of the mid-sagittal plane in three-dimensional (3-D) brain images. The algorithm seeks the plane with respect to which the image exhibits maximum symmetry. For a given plane, symmetry is measured by the cross-correlation between the image sections lying on either side. The search for the plane of maximum symmetry is performed by using a multiresolution approach which substantially decreases computational time. The choice of the starting plane was found to be an important issue in optimization. A method for selecting the initial plane is presented. The algorithm has been tested on brain images from various imaging modalities in both humans and animals. Results were evaluated by visual inspection by neuroradiologists and were judged to be consistently correct.

Algorithms↗
PubMed1996

Minimum cross-entropy reconstruction of PET images using prior anatomical information.

An algorithm is presented for the reconstruction of PET images using prior anatomical information derived from MR images of the same subject. The cross-entropy or Kullback-Leiber distance is a measure of dissimilarity between two images. We propose to reconstruct PET images by minimizing a weighted sum of two cross-entropy terms. The first is the cross-entropy between the measured emission data and the forward projection of the current estimate of the PET image. Minimizing this term alone is equivalent to the ML-EM reconstruction. The second term is the cross-entropy between the current estimate of the PET image and a prior image model which incorporates anatomical information derived from registered MR images. A weighting parameter determines the relative emphasis given to the emission data and the prior model in the reconstruction. Details of this algorithm are presented as well as test reconstructions for real and simulated data. The performance of the algorithm was evaluated with respect to errors in prior anatomical information. The algorithm provided significant improvement in the quality of reconstructed images as compared with the ML-EM reconstruction technique. The reconstructed images had higher resolution as compared with the images obtained from MAP-like reconstructions which do not utilize anatomical information. The algorithm displayed robustness with respect to errors in prior anatomical information.

Algorithms↗
PubMed1995

[Evaluation of partial volume effect in quantitative measurement of regional cerebral blood flow in single photon emission computed tomography--effects of limited spatial resolution and first-pass extraction fraction].

Two numerical brain phantoms were generated in order to investigate errors which might be included in the quantitative measurement of regional CBF with use of single photon emission computed tomography (SPECT). The first phantom simulated the normal brain, and effects of the limited spatial resolution of the SPECT scanner were evaluated for 4 tracer kinetic models of the conventional microsphere model, the intra-carotid bolus injection technique of 133Xe, 133Xe Kanno-Lassen method, and the IMP-autoradiography (IMP-ARG) method. The second phantom simulated the diseased brain with middle-carotid artery (MCA) occlusion, and effects of the limited first-pass extraction fraction were investigated for the microsphere model with various permeability-surface area products. The limited spatial resolution caused systematic underestimation of the radioactivity concentration in the gray matter regions, and systematic overestimation in the low CBF regions. These errors in the original radioactivity distribution were found to cause further systematic errors in the calculated regional CBF images. It was also found that these errors were highly dependent on the tracer kinetic model employed, e.g., regional CBF values were overestimated in the clearance and the Kanno-Lassen methods compared with the conventional microsphere method, whereas values were underestimated in the IMP-ARG method. It was also shown in this study that the limited first-pass extraction fraction caused significant underestimation in the calculated rCBF values. In addition, regional contrast can be reduced when using a tracer with small PS product.(ABSTRACT TRUNCATED AT 250 WORDS)

Brain↗
PubMed1995

[Evaluation of partial volume effect in quantitative measurement of regional cerebral blood flow using positron emission tomography].

Effects of limited spatial resolution of the positron emission tomography (PET) scanner on the quantitative measurement of regional cerebral blood flow (rCBF) was investigated for various tracer kinetic models with use of 15O labeled water and PET. Using a numerical brain phantom consisting of a gray matter, white matter and cerebrospinal fluid components, dynamical tracer distribution images were calculated for the H215O bolus injection and for the C15O2 gas inhalation protocols. The tracer distribution images were convoluted with a 2 dimensional gaussian function with full-width at half maximum (FWHM) of 4, 7, 12 mm to simulate a limited spatial resolution of the PET scanner, and rCBF images were calculated according to some kinetic models. Smoothing the tracer distribution images caused a heterogeneous structure (tissue mixture) in a given volume element. rCBF values calculated by models with use of 15O-water and PET were found to provide rCBF values that were systematically underestimated compared with those obtained by the microsphere model for a mixed tissue region. Moreover, the magnitude of the underestimation was shown to be highly dependent on the tracer kinetic models employed, those errors for mixed tissue of gray and white matter were 20% on steady state, 9% on autoradiography and on weighted integration method, and 2% on non-linear least squares fitting, compared with microsphere model. More errors observed by steady state method and autoradiography method happened for tissue mixture consisting gray matter, white matter and cerebrospinal fluid components. It is important to take into account for difference of the partial volume effect for each models in calculated rCBF.

Brain↗
PubMedDate not supplied

Automatic detection of intradural spaces in MR images.

OBJECTIVE: An algorithm is presented for the automatic detection of intradural spaces in MR images of the human head. The primary motivation behind the present work has been to serve as a preprocessing step in automatic segmentation of brain tissue and CSF. A second objective was to use the algorithm in a fully automatic PET-MR registration algorithm. MATERIALS AND METHODS: The method is primarily designed for, and requires, dual echo (T1- and T2-weighted) MR images with transaxial orientations. The algorithm consists of three main stages. First, the head contour is detected using a series of low-level image-processing techniques. In the second stage, the pixels inside the head contour are clustered into a number of classes using the K-means algorithm. Finally, the extradural connected components are eliminated based on a number of heuristics. RESULTS: Test results are presented for 10 MR image sets consisting of 197 slices. As a quantitative measure of accuracy, manual segmentations were performed by radiologists on a number of slices and compared with the results obtained automatically. CONCLUSION: Visual inspection and quantitative validation of the results indicate that the algorithm accurately detects the intradural spaces in MR images. This is an important step in fully automatic segmentation and registration of MR images.

Algorithms↗
PubMedDate not supplied

A fully automatic multimodality image registration algorithm.

OBJECTIVE: A fully automatic multimodality image registration algorithm is presented. The method is primarily designed for 3D registration of MR and PET images of the brain. However, it has also been successfully applied to CT-PET, MR-CT, and MR-SPECT registrations. MATERIALS AND METHODS: The head contour is detected on the MR image using a gradient threshold method. The head region in the MR image is then segmented into a set of connected components using the K-means clustering algorithm. When the two image sets are registered, the segmentation of the MR image indirectly generates a segmentation of the PET image. The best registration is taken to be the one that optimizes the segmentation induced on the PET image. In this article, the K-means minimum variance criterion is used as a cost function, and the optimization is performed using the method of coordinate descent. RESULTS: The algorithm was tested on 80 H2 15O PET and MR image pairs from 10 subjects. Qualitatively correct results were obtained in all cases. With use of external markers visible in both image modalities, the average registration error was estimated to be < 3 mm. CONCLUSION: The algorithm presented in this article requires no user interaction and can be applied to a wide range of registration problems. Quantitative and qualitative evaluations of the algorithm indicate a high degree of accuracy.

Algorithms↗