An advanced fully 3D OSEM reconstruction for positron emission tomography

YUN Ming-Kai; LIU Shuang-Quan; SHAN Bao-Ci; WEI Long

doi:10.1088/1674-1137/34/2/015

Chinese Physics C> 2010, Vol. 34> Issue(2) : 231-236 DOI: 10.1088/1674-1137/34/2/015

An advanced fully 3D OSEM reconstruction for positron emission tomography

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Abstract：
A fully 3D OSEM reconstruction method for positron emission tomography (PET) based on symmetries and sparse matrix technique is described. Great savings in both storage space and computation time were achieved by exploiting the symmetries of scanner and sparseness of the system matrix. More reduction of storage requirement was obtained by introducing the approximation of system matrix. Iteration-filter was performed to restrict image noise in reconstruction. Performances of simulation data and phantom data got from Micro-PET (Type: Epuls-166) demonstrated that similar image quality was achieved using the approximation of the system matrix.

References

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YUN Ming-Kai, LIU Shuang-Quan, SHAN Bao-Ci and WEI Long. An advanced fully 3D OSEM reconstruction for positron emission tomography[J]. Chinese Physics C, 2010, 34(2): 231-236. doi: 10.1088/1674-1137/34/2/015

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Received: 2009-03-03
Revised: 2009-05-05

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沈阳化工大学材料科学与工程学院沈阳 110142

An advanced fully 3D OSEM reconstruction for positron emission tomography

Corresponding author: SHAN Bao-Ci,

Received Date: 2009-03-03
Accepted Date: 2009-05-05
Available Online: 2010-02-05

Abstract: A fully 3D OSEM reconstruction method for positron emission tomography (PET) based on symmetries and sparse matrix technique is described. Great savings in both storage space and computation time were achieved by exploiting the symmetries of scanner and sparseness of the system matrix. More reduction of storage requirement was obtained by introducing the approximation of system matrix. Iteration-filter was performed to restrict image noise in reconstruction. Performances of simulation data and phantom data got from Micro-PET (Type: Epuls-166) demonstrated that similar image quality was achieved using the approximation of the system matrix.