Evaluation of quantitative, efficient image reconstruction for VersaPET, a compact PET system

2020 ◽  
Vol 47 (7) ◽  
pp. 2852-2868
Author(s):  
Shouyi Wei ◽  
Paul Vaska
Keyword(s):  
Image Reconstruction ◽  
Pet System

Ultrafast image reconstruction of a dual-head PET system by use of CUDA architecture

2011 ◽  
Author(s):  
YuKai Hung ◽  
Yun Dong ◽  
Felix R. Chern ◽  
Weichung Wang ◽  
Chien-Min Kao ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Cuda Architecture ◽  
Pet System

Image reconstruction algorithms for a novel PET system with a half-ring insert

2006 ◽  
Author(s):  
Debashish Pal ◽  
Yuan-Chuan Tai ◽  
Martin Janecek ◽  
Heyu Wu ◽  
Joseph A. O'Sullivan
Keyword(s):  
Image Reconstruction ◽  
Reconstruction Algorithms ◽  
Pet System ◽  
Image Reconstruction Algorithms

scholarly journals High resolution image reconstruction method for a double-plane PET system with changeable spacing

2016 ◽  
Vol 40 (5) ◽  
pp. 058201 ◽  
Author(s):  
Xiao-Yue Gu ◽  
Wei Zhou ◽  
Lin Li ◽  
Long Wei ◽  
Peng-Fei Yin ◽  
...  
Keyword(s):  
High Resolution ◽  
Image Reconstruction ◽  
Reconstruction Method ◽  
Resolution Image ◽  
High Resolution Image ◽  
Image Reconstruction Method ◽  
Double Plane ◽  
Pet System

Image reconstruction for a 3D PET system using a minimum norm constraint

1991 ◽  
Vol 36 (1) ◽  
pp. 35-46 ◽  
Author(s):  
M J Hogan ◽  
P J Harvey ◽  
D C Howse ◽  
B T A McKee
Keyword(s):  
Image Reconstruction ◽  
Minimum Norm ◽  
Norm Constraint ◽  
Pet System ◽  
3D Pet

Time-of-flight image reconstruction with TV minimization constraint for a dual-head small animal PET system

2012 ◽  
Author(s):  
Hung-Yi Chou ◽  
Chung-Yung Tsai ◽  
Chien-Min Kao ◽  
Chin-Tu Chen ◽  
Cheng-Ying Chou
Keyword(s):  
Image Reconstruction ◽  
Time Of Flight ◽  
Small Animal ◽  
Small Animal Pet ◽  
Animal Pet ◽  
Pet System

scholarly journals Image reconstruction by conditional entropy maximisation for PET system

2004 ◽  
Vol 151 (5) ◽  
pp. 345 ◽  
Author(s):  
P.P. Mondal ◽  
K. Rajan
Keyword(s):  
Image Reconstruction ◽  
Conditional Entropy ◽  
Pet System ◽  
Entropy Maximisation

High resolution reconstruction of PET images using the iterative OSEM algorithm

2004 ◽  
Vol 43 (03) ◽  
pp. 72-78 ◽  
Author(s):  
M. Henze ◽  
O. Bublitz ◽  
A. Werling ◽  
L. E. Adam ◽  
U. Haberkorn ◽  
...  
Keyword(s):  
High Resolution ◽  
Image Reconstruction ◽  
Radial Distance ◽  
Whole Body ◽  
Contrast Resolution ◽  
Line Spread Function ◽  
Positron Emission ◽  
Forming Characteristics ◽  
Pet System ◽  
Spatially Variant

Summary Aim: Improvement of the spatial resolution in positron emission tomography (PET) by incorporation of the image-forming characteristics of the scanner into the process of iterative image reconstruction. Methods: All measurements were performed at the whole-body PET system ECAT EXACT HR+ in 3D mode. The acquired 3D sinograms were sorted into 2D sinograms by means of the Fourier rebinning (FORE) algorithm, which allows the usage of 2D algorithms for image reconstruction. The scanner characteristics were described by a spatially variant line-spread function (LSF), which was determined from activated copper-64 line sources. This information was used to model the physical degradation processes in PET measurements during the course of 2D image reconstruction with the iterative OSEM algorithm. To assess the performance of the high-resolution OSEM algorithm, phantom measurements performed at a cylinder phantom, the hotspot Jaszczack phantom, and the 3D Hoffmann brain phantom as well as different patient examinations were analyzed. Results: Scanner characteristics could be described by a Gaussian-shaped LSF with a full-width at half-maximum increasing from 4.8 mm at the center to 5.5 mm at a radial distance of 10.5 cm. Incorporation of the LSF into the iteration formula resulted in a markedly improved resolution of 3.0 and 3.5 mm, respectively. The evaluation of phantom and patient studies showed that the high-resolution OSEM algorithm not only lead to a better contrast resolution in the reconstructed activity distributions but also to an improved accuracy in the quantification of activity concentrations in small structures without leading to an amplification of image noise or even the occurrence of image artifacts. Conclusion: The spatial and contrast resolution of PET scans can markedly be improved by the presented image restauration algorithm, which is of special interest for the examination of both patients with brain disorders and small animals.

scholarly journals Accelerating Image Reconstruction in Dual-Head PET System by GPU and Symmetry Properties

PLoS ONE ◽  
2012 ◽  
Vol 7 (12) ◽  
pp. e50540 ◽  
Author(s):  
Cheng-Ying Chou ◽  
Yun Dong ◽  
Yukai Hung ◽  
Yu-Jiun Kao ◽  
Weichung Wang ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Symmetry Properties ◽  
Pet System
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