Effect of normalization method on image uniformity and binding potential estimates on microPET

This study investigates different detector normalization procedures for a small animal scanner, specifically the Concorde microPET/sup /spl reg// R4. The procedures were compared in terms of: (i) image uniformity, (ii) performance as a function of count rate, and (iii) impact on the estimate of the binding potential (BP) in brain rat studies. Image uniformity studies were performed on two cylindrical phantoms of different size filled with an aqueous concentration of /sup 11/C (38 kBq/mL and 615 kBq/mL). BP was estimated with the Logan graphical approach on 12 /sup 11/C-Methylphenidate and 9 /sup 11/C-Dihydrotetrabenazine rat studies processed with all the normalization procedures. We found that: 1) the combination, the geometry normalized combination, the component-point, and the geometry normalized component procedures significantly improve radial image uniformity compared to the direct-point and direct-cylinder procedures, 2) the geometry normalized combination procedure seems to provide the best radial and axial uniformity, 3) a mismatch between the count rates at which the normalization and the emission scans are acquired degrades the axial uniformity by 47% to 98% whereas this effect was not observed for radial uniformity, and 4) the difference in BP values obtained from data corrected with different normalization procedures is as high as 15% for normal striatum and 75% for lesion striatum.

[1]  R. D. Badawi,et al.  Self-normalization of emission data in 31) PET , 1999 .

[2]  N. Volkow,et al.  Distribution Volume Ratios without Blood Sampling from Graphical Analysis of PET Data , 1996, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[3]  A Geissbuhler,et al.  A normalization technique for 3D PET data. , 1991, Physics in medicine and biology.

[4]  Magnus Dahlbom,et al.  PET system calibrations and corrections for quantitative and spatially accurate images , 1989 .

[5]  R. Badawi,et al.  Self-normalisation of emission data in 3D-PET , 1998, 1998 IEEE Nuclear Science Symposium Conference Record. 1998 IEEE Nuclear Science Symposium and Medical Imaging Conference (Cat. No.98CH36255).

[6]  J. M. Ollinger Detector efficiency and Compton scatter in fully 3D PET , 1995 .

[7]  R. N. Goble,et al.  Performance evaluation of the microPET R4 PET scanner for rodents , 2003, European Journal of Nuclear Medicine and Molecular Imaging.

[8]  David W. Townsend,et al.  An investigation of factors affecting detector and geometric correction in normalization of 3-D PET data , 1995 .

[9]  M. Defrise,et al.  Three dimensional reconstruction of PET data from a multi-ring camera , 1989 .

[10]  Dale L. Bailey,et al.  Quantitative Procedures in 3D PET , 1998 .

[11]  Edward J. Hoffman,et al.  A study of data loss and mispositioning due to pileup in 2-D detectors in PET , 1990 .

[12]  J Machac,et al.  Cerebral versus myocardial stress perfusion imaging: role of pharmacological intervention in the diagnostic assessment of flow reserve. , 1994, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[13]  Donald Sashin,et al.  Efficiency normalization techniques for 3D PET data , 1995, 1995 IEEE Nuclear Science Symposium and Medical Imaging Conference Record.

[14]  P K Marsden,et al.  Developments in component-based normalization for 3D PET. , 1999, Physics in medicine and biology.

[15]  James F. Young,et al.  MicroPET: a high resolution PET scanner for imaging small animals , 1996, IEEE Nuclear Science Symposium Conference Record.