Comments on "Probabilistic analysis of functional magnetic resonance imaging data".
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Bayes Theorem↗
Biomedical subjects
B A Ardekani
Publications and source records attributed to B A Ardekani.
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↗
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↗
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↗
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↗
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↗
[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↗
[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↗
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↗
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↗