PT  - JOURNAL ARTICLE
AU  - Martin A. Lodge
AU  - Arman Rahmim
AU  - Richard L. Wahl
TI  - A Practical, Automated Quality Assurance Method for Measuring Spatial Resolution in PET
AID  - 10.2967/jnumed.108.060079
DP  - 2009 Aug 01
TA  - Journal of Nuclear Medicine
PG  - 1307--1314
VI  - 50
IP  - 8
4099  - http://jnm.snmjournals.org/content/50/8/1307.short
4100  - http://jnm.snmjournals.org/content/50/8/1307.full
SO  - J Nucl Med2009 Aug 01; 50
AB  - The use of different scanners, acquisition protocols, and reconstruction algorithms has been identified as a problem that limits the use of PET in multicenter trials. The aim of this project was to aid standardization of data collection by developing a quality assurance method for measuring the spatial resolution achieved with clinical imaging protocols. Methods: A commercially available 68Ge cylinder phantom (diameter, 20 cm) with a uniform activity concentration was positioned in the center of the PET field of view, and an image was acquired using typical clinical parameters. Spatial resolution was measured by artificially generating an object function (O) with uniform activity within a 20-cm-diameter cylinder, assuming no noise and perfect spatial resolution, centered on the original image (I); dividing F[I] by F[O], where F indicates a 2-dimensional Fourier transform, to produce a modulation transfer function; and taking the inverse Fourier transform of the modulation transfer function to produce a point-spread function in image space. The method was validated using data acquired on 4 different commercial PET systems. Results: Spatial resolution on the Discovery LS was measured at 5.75 ± 0.58 mm, compared with 5.54 ± 0.19 mm from separate point source measurements. Variability of the resolution measurements differed between scanners and protocols, but the typical SD was approximately 0.15 mm when iterative reconstruction was used. The potential for predicting resolution recovery coefficients for small objects was also demonstrated. Conclusion: The proposed method does not require elaborate phantom preparation and is practical to perform, and data analysis is fully automated. This approach is useful for evaluating clinical reconstruction protocols across varying scanners and reconstruction algorithms and should greatly aid standardization of data collection between centers.