Using Deconvolution to Improve PET Spatial Resolution in OSEM Iterative Reconstruction

Summary

Objectives : A novel approach to the PET image reconstruction is presented, based on the inclusion of image deconvolution during conventional OSEM reconstruction. Deconvolution is here used to provide a recovered PET image to be included as “a priori" information to guide OSEM toward an improved solution.

Methods : Deconvolution was implemented using the Lucy-Richardson (LR) algorithm: Two different deconvolution schemes were tested, modifying the conventional OSEM iterative formulation: 1) We built a regularizing penalty function on the recovered PET image obtained by deconvolution and included i in the OSEM iteration. 2) After each conventional global OSEM iteration, we deconvolved the resulting PET image and used this “recovered" version as the initialization image for the next OSEM iteration. Tests were performed on both simulated and acquired data.

Results : Compared to the conventional OSEM, both these strategies, applied to simulated and acquired data, showed an improvement in image spatial resolution with better behavior in the second case. In this way, small lesions, present on data, could be better discriminated in terms of contrast.

Conclusions : Application of this approach to both simulated and acquired data suggests its efficacy in obtaining PET images of enhanced quality.

Keywords

Deconvolution - OSEM reconstruction - PET spatial resolution recovery

• References

• 1. Raylman RR, Kison PV, Wahl RL. Capabilities of two and three-dimensional FDG-PET for detecting small lesions and lymph nodes in the upper torso: A dynamic phantom study. Eur J Nucl Med 1999; 26: 39-45.
• 2. Jacobson M, Levkovitz R, Ben-Tal A, Thielemans K. et al. Enhanced 3D PET OSEM reconstruction using inter-update Metz filtering. Phys Med Biol 2000; 45: 2417-2439.
• 3. Green P. Bayesian reconstructions from emission tomography data using a modified EM algorithm. IEEE Trans Med Imag 1990; 9: 84-93.
• 4. Lucy LB. An iterative technique for the rectification of observed distributions. Astron J 1974; 79: 745-754.
• 5. Adam L, Zaers J, Ostertag H, Trojan H, Belleman ME, Brix G. Performance evaluation of the whole-body PET scanner ECAT EXACT HR+, following the IEC standards. IEEE Trans Nucl Sci 1997; 44: 1171-1179.
• 6. Castiglioni I, Cremonesi O, Gilardi MC, Savi A, Bettinardi V, Rizzo G, Bellotti E, Fazio F. A Monte Carlo model of noise components in 3-D PET. IEEE Trans Nucl Sci 2002; 49: 2297-2303.
• 7. Rizzo G, Castiglioni I, Russo G, Gilardi MC, Panzacchi A, Fazio F. Data rebinning and reconstruction in 3D PET/CT oncological studies: a Monte Carlo evaluation. Proc IEEE Medical Imaging Conf, Rome, Italy, 2004 171. 172.
• 8. Castiglioni I, Rizzo G, Gilardi MC, Bettinardi V, Savi A, Fazio F. Lesion detectability and quantification in PET/CT oncological studies by Monte Carlo simulations. IEEE Trans Nucl Sci 2005; 52: 136-142.
• 9. Qi J, Leahy RM, Cherry SR, Chatziioannou A, Farquhar TH. High-resolution 3D Bayesian image reconstruction using the microPET small-animal scanner. Phys Med Biol 1998; 43: 1001-1013.