Nonuniform collimator sensitivity: improved precision for quantitative SPECT.

Abstract

UNLABELLED: Attenuation of photons degrades both the accuracy and the precision of SPECT images; attenuation correction algorithms correct the bias but cannot improve precision. Increased noise due to photon attenuation is most pronounced in regions deep in solid body sections, such as the brain and abdomen. We have quantified the degradation in performance in several estimation tasks that can be attributed to photon attenuation and determined the degree to which performance might be improved by a collimator with a nonuniform sensitivity profile. METHODS: The analysis used ideal-observer models of performance in tasks involving estimation of the activity and size of a focal lesion. The models were based on the Cramer-Rao lower bound on the variance with which lesion activity and size can be estimated by an unbiased procedure. To quantify the effects of attenuation, values of the Cramer-Rao bound were calculated for each estimation task as a function of location of the lesion in circularly-shaped attenuators of 10- and 20-cm radii, with and without attenuation. Values of the bound were also determined for two nonuniform sensitivity profiles, one of which was designed to equalize (or nearly equalize) task performance throughout the image. RESULTS: For 99mTc, photon attenuation increased the variance of the estimates by factors of up to 4.5 for the 10-cm radius attenuator and up to 20.0 for the 20-cm radius attenuator. A collimator with a nonuniform sensitivity function reduced variance by factors of up to 1.8 for the 10-cm radius attenuator and up to 2.8 for the 20-cm radius attenuator. These gains in estimation performance were insensitive to the imaging task and to deviations from the assumed attenuator size and shape. CONCLUSION: Performance in estimation tasks using images from SPECT systems with uniform sensitivity collimators is considerably lower than the theoretical optimum. We have derived a sensitivity function, realizable using existing technology, that improves performance substantially.