Visualization and three-dimensional image processing of positron emission tomography (PET) brain images

2002 ◽  
Author(s):  
N.D. Gershon
Keyword(s):  
Image Processing ◽  
Positron Emission Tomography ◽  
Three Dimensional ◽  
Emission Tomography ◽  
Brain Images ◽  
Dimensional Image ◽  
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.

Objective Brains, Prejudicial Images

1999 ◽  
Vol 12 (1) ◽  
pp. 173-201 ◽  
Author(s):  
Joseph Dumit
Keyword(s):  
Positron Emission Tomography ◽  
Popular Culture ◽  
Computerized Tomography ◽  
Emission Tomography ◽  
Brain Images ◽  
Positron Emission ◽  
Undue Risk

The ArgumentIn this article I argue that brain images constructed with computerized tomography (CT) and positron emission tomography (PET) are part of a category of “expert images” and are both visually persuasive and also particularly difficult to interpret and understand by non-experts. Following the innovative judicial analogy of “demonstrative evidence” traced by Jennifer Mnookin (1998), I show how brain images are more than mere illustrations when they enter popular culture and courtrooms. Attending to the role of experts in producing data in the form of images, in selecting extreme images for publication, and in testifying as to their relevance, I argue that there is an undue risk in courtrooms that brain images will not be seen as prejudiced, stylized representations of correlation, but rather as straightforward, objective photographs of, for example, madness.

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.

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