Use of a fast EM algorithm for 3D image reconstruction with the YAP-PET tomograph.

OBJECTIVE We would like to improve the image reconstructions for both signal-to-noise ratio (SNR) and spatial resolution characteristics for the small animal positron emission tomograph YAP-PET, built at the Department of Physics of Ferrara University. The three-dimensional (3D) filtered backprojection (FBP) algorithm, usually used for image reconstruction, has a limited angle restriction due to the tomograph geometry, which causes a serious loss in sensitivity. METHODS We implemented a 3D iterative reconstruction program using the symmetry and sparse properties of the 'probability matrix', which correlates the emission from each voxel to the detector within a coincidence tube. A fraction only of matrix elements are calculated before the reconstruction and stored on disk: this allows us to avoid on-line computation. A depth dependent function differentiates the voxels in a coincidence tube. Three experimental phantoms with no background were reconstructed by using the program, in comparison with traditionally used FBP. RESULTS The adopted method allowed us to reduce the computation time significantly. Furthermore, the simple depth dependent function improved the spatial resolution. With 64 x 64 x 20 voxels of 0.625 x 0.625 x 2.0 mm(3) in the field of view, the computation time was less than 4 min per iteration on a Sparc Ultra 450 Workstation, and less than 6 min per iteration on a Mac-PPC G3 300 MHz: the spatial resolution measured with a 0.8 mm diameter 18F-FDG filled capillary reconstructed in this way was 2.0 mm FWHM. By decreasing the voxel size to 0.3125 x 0.3125 x 2.0 mm(3) per voxel the transaxial FWHM was 1.7 mm with a computation time of 15 min per iteration on a Sparc Ultra 450. By using all the acquired data, the SNR improves from 1.3 to 6.0 in the worst measured case, a pair of 0.8mm diameter 18F-FDG filled capillaries, which are 2.5 mm apart each other. CONCLUSION The adoption of iterative reconstruction allowed us to overcome the loss in sensitivity of previously used FBP: this improved the SNR. The studies of symmetry and sparse properties avoided a severe increase of the reconstruction time and of storing space on disk. This fast EM Algorithm is now routinely used for the image reconstruction with the YAP-PET tomograph.

[1]  L. M. Patnaik,et al.  High-speed computation of the EM algorithm for PET image reconstruction , 1994 .

[2]  Michel Defrise,et al.  Fast iterative image reconstruction of 3D PET data , 1996, 1996 IEEE Nuclear Science Symposium. Conference Record.

[3]  Ariela Sofer,et al.  Evaluation of 3D reconstruction algorithms for a small animal PET camera , 1996 .

[4]  M L Egger,et al.  Incremental beamwise backprojection using geometrical symmetries for 3D PET reconstruction in a cylindrical scanner geometry. , 1998, Physics in medicine and biology.

[5]  A. Del Guerra,et al.  First in vivo studies on rats with the YAPPET scanner , 1999, 1999 IEEE Nuclear Science Symposium. Conference Record. 1999 Nuclear Science Symposium and Medical Imaging Conference (Cat. No.99CH37019).

[6]  Yuchen Yan,et al.  A system for the 3D reconstruction of retracted-septa PET data using the EM algorithm , 1995 .

[7]  J. Ollinger,et al.  Maximum likelihood reconstruction in fully 3D PET via the SAGE algorithm , 1996, 1996 IEEE Nuclear Science Symposium. Conference Record.

[8]  Zang-Hee Cho,et al.  Parallelization of the EM algorithm for 3-D PET image reconstruction , 1991 .

[9]  R. Leahy,et al.  High-resolution 3D Bayesian image reconstruction using the microPET small-animal scanner. , 1998, Physics in medicine and biology.

[10]  L. Shepp,et al.  Maximum Likelihood Reconstruction for Emission Tomography , 1983, IEEE Transactions on Medical Imaging.

[11]  A. Del Guerra,et al.  YAP-PET: first results of a small animal positron emission tomograph based on YAP:Ce finger crystals , 1998 .

[12]  K. Erlandsson,et al.  Fast accurate iterative reconstruction for low-statistics positron volume imaging. , 1998, Physics in medicine and biology.

[13]  S.-Y. Lee,et al.  Parallelization Of The EM Algorithm For 3D PEt Image Reconstruction , 1990, 1990 IEEE Nuclear Science Symposium Conference Record.

[14]  A. Del Guerra,et al.  High spatial resolution small animal YAP-PET , 1998 .

[15]  L. M. Patnaik,et al.  Linear array implementation of the EM algorithm for PET image reconstruction , 1995 .

[16]  H. Herzog,et al.  High resolution and better quantification by tube of response modelling in 3D PET reconstruction , 1996, 1996 IEEE Nuclear Science Symposium. Conference Record.

[17]  H. Barrett,et al.  List-mode likelihood: EM algorithm and image quality estimation demonstrated on 2-D PET , 1998, IEEE Transactions on Medical Imaging.