scholarly journals The Spectral Signature Method for the Analysis of PET Brain Images

1991 ◽  
Vol 11 (1_suppl) ◽  
pp. A103-A113 ◽  
Author(s):  
Brain Images ◽  
A. V. Levy ◽  
E. Laska ◽  
J. D. Brodie ◽  
N. D. Volkow ◽  
...  
Keyword(s):  
Metabolic Activity ◽  
Three Dimensional ◽  
Feature Space ◽  
Normal Subjects ◽  
Spectral Signature ◽  
Centroid Method ◽  
Brain Images ◽  
Metabolic Characteristics ◽  
Positron Emission ◽  
Head Positioning

We introduce the concept of the metabolic centroid spectrum as the feature space to characterize the distribution of metabolic activity in three-dimensional brains. The method computes the metabolic centroid of a brain subvolume for each increment of metabolic activity occurring in the whole brain. The result is the metabolic spectral signature, a continuous three-dimensional curve whose shape reflects the distribution of metabolic rates in the brain. The method's sensitivity to metabolic distribution asymmetries is greatly increased over that of the metabolic centroid method, while retaining its advantages; it is almost invariant to head size, head positioning, photon scatter, and the positron emission tomography (PET) camera's full width at half-maximum. It does not require magnetic resonance, computed tomography, or x-ray images. To test the method we analyzed the metabolic PET images of 40 normal subjects and 20 schizophrenics. The results show a unification of several metabolic characteristics of schizophrenic brains, such as laterality, hypofrontality, cortical/subcortical abnormalities, and overall brain hypometabolism, which were identified by different laboratories in separate studies using differing methodologies. Here they are presented by a single automatic objective method.

scholarly journals The Metabolic Centroid Method for PET Brain Image Analysis

1989 ◽  
Vol 9 (3) ◽  
pp. 388-397 ◽  
Author(s):  
A. V. Levy ◽  
J. D. Brodie ◽  
JJ. A. G. Russell ◽  
N. D. Volkow ◽  
E. Laska ◽  
...  
Keyword(s):  
Random Noise ◽  
Three Dimensional ◽  
Normal Subjects ◽  
Subcortical Structures ◽  
Centroid Method ◽  
Whole Brain ◽  
Positron Emission ◽  
The Mean ◽  
Brain Image Analysis ◽  
The Brain

The method of centroids is an approach to the analysis of three-dimensional whole-brain positron emission tomography (PET) metabolic images. It utilizes the brain's geometric centroid and metabolic centroid so as to objectively characterize the central tendency of the distribution of metabolic activity in the brain. The method characterizes the three-dimensional PET metabolic image in terms of four parameters: the coordinates of the metabolic centroid and the mean metabolic rate of the whole brain. These parameters are not sensitive to spatially uniform random noise or to the position of the subject's head within a uniform PET camera field of view. The method has been applied to 40 normal subjects, 22 schizophrenics who were treated with neuroleptics, and 20 schizophrenics who were neuroleptic-free. The mean metabolic centroid of the normal subjects was found to be superior to the mean geometric centroid of the brain. The mean metabolic centroid of chronic schizophrenics is lower and more posterior to the mean geometric centroid than is that of normals. This difference is greater in medicated than in unmedicated schizophrenics. The posterior and downward displacement of the mean metabolic centroid is consistent with the concepts of hypofrontality, hyperactivity of subcortical structures, and neuroleptic effect in schizophrenics.

Automatic Brain Cancer Segmentation on PET Image

2020 ◽  
pp. 2150008
Author(s):  
Ching-Lin Wang ◽  
Chi-Shiang Chan ◽  
Wei-Jyun Wang ◽  
Yung-Kuan Chan ◽  
Meng-Hsiun Tsai ◽  
...  
Keyword(s):  
Brain Tumor ◽  
3D Model ◽  
Early Stage ◽  
Three Dimensional ◽  
Ground Truth ◽  
Emission Tomography ◽  
Segmentation Method ◽  
Brain Images ◽  
Positron Emission ◽  
Image Set

When treating a brain tumor, a doctor needs to know the site and the size of the tumor. Positron emission tomography (PET) can be effectively applied to diagnose such cancers based on the heightened glucose metabolism of early-stage cancer cells. The purpose of this research is to extract the regions of skull, brain tumor, and brain tissue from a series of PET brain images and then a three-dimensional (3D) model is reconstructed from the extracted skulls, brain tumors, and brain tissues. Knowing the relative site and size of a tumor within the skull is helpful to a doctor. The contours obtained by the segmentation method proposed in this study are quantitatively compared with the contours drawn by doctors on the same image set since the ground truth is unknown. The experimental results are impressive.

scholarly journals Functional Mapping of Human Sensorimotor Cortex with 3D BOLD fMRI Correlates Highly with H215O PET rCBF

1996 ◽  
Vol 16 (5) ◽  
pp. 755-764 ◽  
Author(s):  
Nick F. Ramsey ◽  
Brenda S. Kirkby ◽  
Peter Van Gelderen ◽  
Karen F. Berman ◽  
Jeff H. Duyn ◽  
...  
Keyword(s):  
Sensorimotor Cortex ◽  
Three Dimensional ◽  
Normal Subjects ◽  
Blood Oxygen Level Dependent ◽  
Blood Oxygenation ◽  
Positron Emission ◽  
Signal Changes ◽  
Highly Correlated ◽  
Centers Of Mass ◽  
H215o Pet

Positron emission tomography (PET) functional imaging is based on changes in regional cerebral blood flow (rCBF). Functional magnetic resonance imaging (fMRI) is based on a variety of physiological parameters as well as rCBF. This study is aimed at the cross validation of three-dimensional (3D) fMRI, which is sensitive to changes in blood oxygenation, with oxygen-15-labeled water (H215O) PET. Nine normal subjects repeatedly performed a simple finger opposition task during fMRI scans and during PET scans. Within-subject statistical analysis revealed significant (“activated”) signal changes ( p < 0.05, Bonferroni corrected for number of voxels) in contralateral primary sensorimotor cortex (PSM) in all subjects with fMRI and with PET. With both methods, 78% of all activated voxels were located in the PSM. Overlap of activated regions occurred in all subjects (mean 43%, SD 26%). The size of the activated regions in PSM with both methods was highly correlated ( rho = 0.87, p < 0.01). The mean distance between centers of mass of the activated regions in the PSM for fMRI versus PET was 6.7 mm (SD 3.0 mm). Average magnitude of signal change in activated voxels in this region, expressed as z-values adapted to timeseries, zt, was similar (fMRI 5.5, PET 5.3). Results indicate that positive blood oxygen level-dependent (BOLD) signal changes obtained with 3D principles of echo shifting with a train of observations (PRESTO) fMRI are correlated with rCBF, and that sensitivity of fMRI can equal that of H215O PET.

DCS-SVM: a novel semi-automated method for human brain MR image segmentation

2017 ◽  
Vol 62 (6) ◽  
pp. 581-590 ◽  
Author(s):  
Ali Ahmadvand ◽  
Mohammad Reza Daliri ◽  
Mohammadtaghi Hajiali
Keyword(s):  
Image Segmentation ◽  
Computational Cost ◽  
Three Dimensional ◽  
Feature Space ◽  
Support Vector ◽  
Brain Images ◽  
Automated Method ◽  
Classifier Selection ◽  
Novel Method ◽  
Mr Brain

AbstractIn this paper, a novel method is proposed which appropriately segments magnetic resonance (MR) brain images into three main tissues. This paper proposes an extension of our previous work in which we suggested a combination of multiple classifiers (CMC)-based methods named dynamic classifier selection-dynamic local training local Tanimoto index (DCS-DLTLTI) for MR brain image segmentation into three main cerebral tissues. This idea is used here and a novel method is developed that tries to use more complex and accurate classifiers like support vector machine (SVM) in the ensemble. This work is challenging because the CMC-based methods are time consuming, especially on huge datasets like three-dimensional (3D) brain MR images. Moreover, SVM is a powerful method that is used for modeling datasets with complex feature space, but it also has huge computational cost for big datasets, especially those with strong interclass variability problems and with more than two classes such as 3D brain images; therefore, we cannot use SVM in DCS-DLTLTI. Therefore, we propose a novel approach named “DCS-SVM” to use SVM in DCS-DLTLTI to improve the accuracy of segmentation results. The proposed method is applied on well-known datasets of the Internet Brain Segmentation Repository (IBSR) and promising results are obtained.

scholarly journals Texture-Based Analysis of 18F-Labeled Amyloid PET Brain Images

2021 ◽  
Vol 11 (5) ◽  
pp. 1991
Author(s):  
Alexander P. Seiffert ◽  
Adolfo Gómez-Grande ◽  
Eva Milara ◽  
Sara Llamas-Velasco ◽  
Alberto Villarejo-Galende ◽  
...  
Keyword(s):  
Area Under The Curve ◽  
Standardized Uptake Value ◽  
Texture Features ◽  
Roc Curves ◽  
Significant Feature ◽  
Study Cohort ◽  
Amyloid Pet ◽  
Reference Region ◽  
Brain Images ◽  
Positron Emission

Amyloid positron emission tomography (PET) brain imaging with radiotracers like [18F]florbetapir (FBP) or [18F]flutemetamol (FMM) is frequently used for the diagnosis of Alzheimer’s disease. Quantitative analysis is usually performed with standardized uptake value ratios (SUVR), which are calculated by normalizing to a reference region. However, the reference region could present high variability in longitudinal studies. Texture features based on the grey-level co-occurrence matrix, also called Haralick features (HF), are evaluated in this study to discriminate between amyloid-positive and negative cases. A retrospective study cohort of 66 patients with amyloid PET images (30 [18F]FBP and 36 [18F]FMM) was selected and SUVRs and 6 HFs were extracted from 13 cortical volumes of interest. Mann–Whitney U-tests were performed to analyze differences of the features between amyloid positive and negative cases. Receiver operating characteristic (ROC) curves were computed and their area under the curve (AUC) was calculated to study the discriminatory capability of the features. SUVR proved to be the most significant feature among all tests with AUCs between 0.692 and 0.989. All HFs except correlation also showed good performance. AUCs of up to 0.949 were obtained with the HFs. These results suggest the potential use of texture features for the classification of amyloid PET images.

scholarly journals Fast Nonsupervised 3D Registration of PET and MR Images of the Brain

1994 ◽  
Vol 14 (5) ◽  
pp. 749-762 ◽  
Author(s):  
Jean-François Mangin ◽  
Vincent Frouin ◽  
Isabelle Bloch ◽  
Bernard Bendriem ◽  
Jaime Lopez-Krahe
Keyword(s):  
Three Dimensional ◽  
Magnetic Resonance Images ◽  
Surface Matching ◽  
3D Registration ◽  
Matching Algorithm ◽  
Distance Map ◽  
Brain Surface ◽  
Discrete Surfaces ◽  
Positron Emission ◽  
The Brain

We propose a fully nonsupervised methodology dedicated to the fast registration of positron emission tomography (PET) and magnetic resonance images of the brain. First, discrete representations of the surfaces of interest (head or brain surface) are automatically extracted from both images. Then, a shape-independent surface-matching algorithm gives a rigid body transformation, which allows the transfer of information between both modalities. A three-dimensional (3D) extension of the chamfer-matching principle makes up the core of this surface-matching algorithm. The optimal transformation is inferred from the minimization of a quadratic generalized distance between discrete surfaces, taking into account between-modality differences in the localization of the segmented surfaces. The minimization process is efficiently performed via the precomputation of a 3D distance map. Validation studies using a dedicated brain-shaped phantom have shown that the maximum registration error was of the order of the PET pixel size (2 mm) for the wide variety of tested configurations. The software is routinely used today in a clinical context by the physicians of the Service Hospitalier Frédéric Joliot (>150 registrations performed). The entire registration process requires ∼5 min on a conventional workstation.

Three-Dimensional Imaging of High Performance Liquid Chromatography Processes Using a Mini Positron Emission Tomograph

2017 ◽  
Vol 2 (30) ◽  
pp. 9797-9802
Author(s):  
Eva Sarkadi-Priboczki ◽  
Ivan Valastyan ◽  
Karoly Brezovcsik ◽  
David Nagy ◽  
Gabor Opposits ◽  
...  
Keyword(s):  
High Performance Liquid Chromatography ◽  
Liquid Chromatography ◽  
High Performance ◽  
Three Dimensional ◽  
Positron Emission Tomograph ◽  
Three Dimensional Imaging ◽  
Positron Emission

Positron Emission Tomography (PET) for Flow Measurement

2011 ◽  
Vol 301-303 ◽  
pp. 1316-1321 ◽  
Author(s):  
Arthur E. Ruggles ◽  
Bi Yao Zhang ◽  
Spero M. Peters
Keyword(s):  
Positron Emission Tomography ◽  
Pet Imaging ◽  
Three Dimensional ◽  
Bulk Water ◽  
Emission Tomography ◽  
Optical Methods ◽  
Imaging Method ◽  
Analysis And Design ◽  
Pet Tracer ◽  
Positron Emission

Positron Emission Tomography (PET) produces a three dimensional spatial distribution of positron-electron annihilations within an image volume. Various positron emitters are available for use in aqueous, organic and liquid metal flows. Preliminary experiments at the University of Tennessee at Knoxville (UTK) injected small flows of PET tracer into a bulk water flow in a four rod bundle. The trajectory and diffusion of the tracer in the bulk flow were then mapped using a PET scanner. A spatial resolution of 1.4 mm is achieved with current preclinical Micro-PET imaging equipment resulting in 200 MB 3D activity fields. A time resolved 3-D spatial activity profile was also measured. The PET imaging method is especially well suited to complex geometries where traditional optical methods such as LDV and PIV are difficult to apply. PET methods are uniquely useful for imaging in opaque fluids, opaque pressure boundaries, and multiphase studies. Several commercial and shareware Computational Fluid Dynamics (CFD) codes are currently used for science and engineering analysis and design. These codes produce detailed three dimensional flow predictions. The models produced by these codes are often difficult to validate. The development of this experimental technique offers a modality for the comparison of CFD outcomes with experimental data. Developed data sets from PET can be used in verification and validation exercises of simulation outcomes.

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