Quantitative evaluation of simultaneous reconstruction with model-based crosstalk compensation for 99mTc/123I dual-isotope simultaneous acquisition brain SPECT.

A model-based method has been previously developed to estimate and compensate for the crosstalk and downscatter contamination in simultaneous 123I/99mTc dual-isotope SPECT imaging. In this method, photon scatter in the object is modeled using the effective source scatter estimate technique. Photon interactions with the collimator-detector are estimated using precalculated Monte Carlo simulated point response functions. Two different approaches, simultaneous and alternating model-based compensations, have been proposed for iterative reconstruction-based crosstalk and downscatter contamination compensation. In this work, both model-based approaches were evaluated in the context of quantitative accuracy when imaging the dopaminergic system using both Monte Carlo simulated and experimentally acquired data. Results indicate that mddel-based estimates of the crosstalk and downscatter contamination in both energy windows were in good agreement with the truth for the simulated data. The effects of the contamination reduced image contrast and overestimated absolute activity in all structures by up to 66%. Compensation using both model-based approaches improved image contrast. Errors in absolute activity quantitation were also reduced to less than +/-5% for most brain structures. The accuracy of striatal specific binding potentials, calculated as the ratio of activity in various striatal structures to the background, was also greatly improved after model-based compensation. In conclusion, model-based compensation of simultaneously acquired images of 99mTc and 123I labeled brain imaging agents provided image quality and quantitative accuracy that were comparable to the image without crosstalk. Both proposed compensation approaches can potentially be applied clinically, but when reconstruction time is a limiting factor, the alternating model-based compensation may be preferable.