2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2007
Correction for tissue attenuation is the most important factor affecting the Poisson characterist... more Correction for tissue attenuation is the most important factor affecting the Poisson characteristics of positron emission tomography (PET) data. Although a weighting scheme incorporating the attenuation correction factors in the update rule of expectation-maximization (EM) image reconstruction algorithm has previously been presented, no exact distribution of pre-corrected measurements has been proposed to date. This paper introduces a fixed multiplicative Poisson
Dynamic emission tomography is a technique used for quantifying the biochemical and physiological... more Dynamic emission tomography is a technique used for quantifying the biochemical and physiological processes within the body. For certain neuroimaging applications, like kinetic modelling in positron emission tomography (PET), segmenting the measured data into a fewer number of regions-of-interest (ROI) is an important procedure needed for calculation of regional time-activity curves (TACs). Conventional estimation of regional activities in image domain
IEEE Nuclear Science Symposuim & Medical Imaging Conference, 2010
In quantitative positron emission tomography (PET) brain studies, the temporal dynamics of the ra... more In quantitative positron emission tomography (PET) brain studies, the temporal dynamics of the radiopharmaceutical are usually analyzed separately for different brain structures. In a clinical environment, the delineation of brain structures is still often performed manually by human experts. In this study, we concentrate on automatic segmentation of the striatal brain structures (caudate, posterior and anterior putamen and ventral striatum)
In this study, we evaluate quantitatively the performance of the DM-DSM (deformable model with du... more In this study, we evaluate quantitatively the performance of the DM-DSM (deformable model with dual surface minimization) method for brain surface extraction from PET images with Monte Carlo simulated data. The DM-DSM method is based on a deformable model and has been found reliable in previous tests with images of healthy volunteers acquired with C-11-Raclopride and F-18-FDG. As the evaluation of the method with real data is challenging, it could not provide precise figures describing the accuracy of the method. In addition to evaluation, we adjust parameter values for the DM-DSM method to improve its accuracy in this study. We compare the DM-DSM method to PET brain delineation based on MRI-PET registration. For this we assume either the knowledge of the precise anatomical brain volume or we extract the brain volume from the anatomical MR image. With FDG, the DM-DSM method yielded brain surfaces of high accuracy, almost as accurate as those obtained by using image registration and the knowledge of the exact anatomy. If the precise anatomical brain volume was not known, the DM- DSM method was more accurate than the image registration based method. With Raclopride, the accuracy of the DM-DSM method was slightly lower than with FDG but it was better than the one obtained using image registration and assuming the knowledge of the anatomical brain volume. When we extracted brain volume automatically from the MR image, the sagittal sinus was excluded from the brain improving the registration accuracy and leading to better quantitative results than those obtained with the DM-DSM method.
We propose and evaluate an automatic segmentation method for extracting striatal brain structures... more We propose and evaluate an automatic segmentation method for extracting striatal brain structures (caudate, putamen, and ventral striatum) from parametric (11)C-raclopride positron emission tomography (PET) brain images. We focus on the images acquired using a novel brain dedicated high-resolution (HRRT) PET scanner. The segmentation method first extracts the striatum using a deformable surface model and then divides the striatum into its substructures based on a graph partitioning algorithm. The weighted kernel k-means algorithm is used to partition the graph describing the voxel affinities within the striatum into the desired number of clusters. The method was experimentally validated with synthetic and real image data. The experiments showed that our method was able to automatically extract caudate, ventral striatum, and putamen from the images. Moreover, the putamen could be subdivided into anterior and posterior parts. An automatic method for the extraction of striatal structur...
We evaluated applicability of ICA (Independent Component Analysis) for the separation of function... more We evaluated applicability of ICA (Independent Component Analysis) for the separation of functional components from H 215_{\rm 2}^{\rm 15} O PET (Positron Emission Tomography) cardiac images. The effects of varying myocardial perfusion to the separation results were investigated using a dynamic 2D numerical phantom. The effects of motion in cardiac region were studied using a dynamic 3D phantom. In this
New adaptive edge detection algorithms based on volumetric neighborhood size estimation for autom... more New adaptive edge detection algorithms based on volumetric neighborhood size estimation for automatic three or higher dimensional biomedical image analysis are presented in this work. The proposed methods are based on nonparametric three-dimensional kernel functions obtained using the "three-term" orthogonal-type polynomial equations for different types of orthogonal polynomial families. The obtained multidimensional kernels can be of any volumetric neighborhood size and order of approximation. The optimal sizes of volume estimates, produced by the multidimensional convolution of the kernels with the multidimensional biomedical images, are controlled by a switch type variance dependent volume size selector. The proposed methods show excellent results in approximating the true position and shape of the edges of different organs of the human body represented in multidimensional biomedical images, which can have nonuniform voxel size and anisotropic image intensity and noise distribution.
2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2007
Correction for tissue attenuation is the most important factor affecting the Poisson characterist... more Correction for tissue attenuation is the most important factor affecting the Poisson characteristics of positron emission tomography (PET) data. Although a weighting scheme incorporating the attenuation correction factors in the update rule of expectation-maximization (EM) image reconstruction algorithm has previously been presented, no exact distribution of pre-corrected measurements has been proposed to date. This paper introduces a fixed multiplicative Poisson
Dynamic emission tomography is a technique used for quantifying the biochemical and physiological... more Dynamic emission tomography is a technique used for quantifying the biochemical and physiological processes within the body. For certain neuroimaging applications, like kinetic modelling in positron emission tomography (PET), segmenting the measured data into a fewer number of regions-of-interest (ROI) is an important procedure needed for calculation of regional time-activity curves (TACs). Conventional estimation of regional activities in image domain
IEEE Nuclear Science Symposuim & Medical Imaging Conference, 2010
In quantitative positron emission tomography (PET) brain studies, the temporal dynamics of the ra... more In quantitative positron emission tomography (PET) brain studies, the temporal dynamics of the radiopharmaceutical are usually analyzed separately for different brain structures. In a clinical environment, the delineation of brain structures is still often performed manually by human experts. In this study, we concentrate on automatic segmentation of the striatal brain structures (caudate, posterior and anterior putamen and ventral striatum)
In this study, we evaluate quantitatively the performance of the DM-DSM (deformable model with du... more In this study, we evaluate quantitatively the performance of the DM-DSM (deformable model with dual surface minimization) method for brain surface extraction from PET images with Monte Carlo simulated data. The DM-DSM method is based on a deformable model and has been found reliable in previous tests with images of healthy volunteers acquired with C-11-Raclopride and F-18-FDG. As the evaluation of the method with real data is challenging, it could not provide precise figures describing the accuracy of the method. In addition to evaluation, we adjust parameter values for the DM-DSM method to improve its accuracy in this study. We compare the DM-DSM method to PET brain delineation based on MRI-PET registration. For this we assume either the knowledge of the precise anatomical brain volume or we extract the brain volume from the anatomical MR image. With FDG, the DM-DSM method yielded brain surfaces of high accuracy, almost as accurate as those obtained by using image registration and the knowledge of the exact anatomy. If the precise anatomical brain volume was not known, the DM- DSM method was more accurate than the image registration based method. With Raclopride, the accuracy of the DM-DSM method was slightly lower than with FDG but it was better than the one obtained using image registration and assuming the knowledge of the anatomical brain volume. When we extracted brain volume automatically from the MR image, the sagittal sinus was excluded from the brain improving the registration accuracy and leading to better quantitative results than those obtained with the DM-DSM method.
We propose and evaluate an automatic segmentation method for extracting striatal brain structures... more We propose and evaluate an automatic segmentation method for extracting striatal brain structures (caudate, putamen, and ventral striatum) from parametric (11)C-raclopride positron emission tomography (PET) brain images. We focus on the images acquired using a novel brain dedicated high-resolution (HRRT) PET scanner. The segmentation method first extracts the striatum using a deformable surface model and then divides the striatum into its substructures based on a graph partitioning algorithm. The weighted kernel k-means algorithm is used to partition the graph describing the voxel affinities within the striatum into the desired number of clusters. The method was experimentally validated with synthetic and real image data. The experiments showed that our method was able to automatically extract caudate, ventral striatum, and putamen from the images. Moreover, the putamen could be subdivided into anterior and posterior parts. An automatic method for the extraction of striatal structur...
We evaluated applicability of ICA (Independent Component Analysis) for the separation of function... more We evaluated applicability of ICA (Independent Component Analysis) for the separation of functional components from H 215_{\rm 2}^{\rm 15} O PET (Positron Emission Tomography) cardiac images. The effects of varying myocardial perfusion to the separation results were investigated using a dynamic 2D numerical phantom. The effects of motion in cardiac region were studied using a dynamic 3D phantom. In this
New adaptive edge detection algorithms based on volumetric neighborhood size estimation for autom... more New adaptive edge detection algorithms based on volumetric neighborhood size estimation for automatic three or higher dimensional biomedical image analysis are presented in this work. The proposed methods are based on nonparametric three-dimensional kernel functions obtained using the "three-term" orthogonal-type polynomial equations for different types of orthogonal polynomial families. The obtained multidimensional kernels can be of any volumetric neighborhood size and order of approximation. The optimal sizes of volume estimates, produced by the multidimensional convolution of the kernels with the multidimensional biomedical images, are controlled by a switch type variance dependent volume size selector. The proposed methods show excellent results in approximating the true position and shape of the edges of different organs of the human body represented in multidimensional biomedical images, which can have nonuniform voxel size and anisotropic image intensity and noise distribution.
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