Principal component analysis of dynamic positron emission tomography images

1994 ◽  
Vol 21 (12) ◽  
pp. 1285-1292 ◽  
Author(s):  
F. Pedersen ◽  
M. Bergströme ◽  
E. Bengtsson ◽  
B. Långström
Keyword(s):  
Positron Emission Tomography ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Emission Tomography ◽  
Dynamic Positron Emission Tomography ◽  
Positron Emission

scholarly journals Masked volume wise principal component analysis of small adrenocortical tumours in dynamic [11C]-metomidate positron emission tomography

2009 ◽  
Vol 9 (1) ◽  
Author(s):  
Pasha Razifar ◽  
Joakim Hennings ◽  
Azita Monazzam ◽  
Per Hellman ◽  
Bengt Långström ◽  
...  
Keyword(s):  
Positron Emission Tomography ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Emission Tomography ◽  
Positron Emission ◽  
Adrenocortical Tumours

TEMPORAL AND SPATIAL COMPRESSION OF DYNAMIC POSITRON EMISSION TOMOGRAPHY IN SINOGRAM DOMAIN

2005 ◽  
Vol 05 (04) ◽  
pp. 839-858 ◽  
Author(s):  
ZHE CHEN ◽  
DAVID DAGAN FENG ◽  
WEIDONG CAI
Keyword(s):  
Positron Emission Tomography ◽  
Principal Component ◽  
Emission Tomography ◽  
Compression Technique ◽  
Dynamic Positron Emission Tomography ◽  
Functional Estimation ◽  
Compression Stage ◽  
Positron Emission ◽  
Temporal And Spatial ◽  
Brain Study

A new algorithm for the compression of dynamic positron emission tomography (PET) data in sinogram domain is presented. It consists of a temporal compression stage based on the application of principal component analysis directly to the PET sinograms to reduce the dimensionality of the data. This is followed by a spatial compression stage using JPEG2000 to each principal component (PC) channel weighted by the signal in each channel. By combing these temporal and spatial compression techniques we can achieve a compression ratio as high as 129:1 while simultaneously reducing noise and improving functional estimation compared with the uncompressed data, and preserving the sinogram data for later analysis. We validate our approach with a simulated phantom 18F-fluoro-deoxyglucose (FDG) brain study and clinical dynamic PET datasets. The results of performance evaluation suggest the new compression technique not only is able to reduce the original sinogram datasets by more than 95%, but also improve the reconstructed image quality for the quantitative analysis.

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