Biomedical subjects

B A Ardekani

Publications and source records attributed to B A Ardekani.

4 recordsLinked to original sources

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↗