A Monte Carlo simulation has been developed to simulate and correct for the effect of Compton scatter in 3-D acquired PET brain scans. The method utilizes the 3-D reconstructed image volume as the source intensity distribution for a photon-tracking Monte Carlo simulation. It is assumed that the number of events in each pixel of the image represents the isotope concentration at that location in the brain. The history of each annihilation photon's interactions in the scattering medium is followed, and the sinograms for the scattered and unscattered photon pairs are generated in a simulated 3-D PET acquisition. The calculated scatter contribution is used to correct the original data set. The method is general and can be applied to any scanner configuration or geometry. In its current form the simulation requires 25 hours on a single SparclO CPU when every pixel in a IS-plane, 128x128 pixel image volume is sampled, and less than 2 hours when 16 pixels (4x4) are grouped as a single pixel. Results of the correction applied to 3-D human and phantom studies are presented.

We are developing an accelerated Monte Carlo simulation of positron emission tomography (PET) that can be used for scatter correction of three-dimensional (3-D) PET data. Our Monte Carlo technique accurately accounts for single, multiple, and dual Compton scatter events, attenuation through the patient bed, and activity from outside the field of view. We have incorporated innovative sampling techniques that are compatible with our simulation approach, increasing computational efficiency by a factor of seven while improving accuracy through more sophisticated stratification and by incorporating the true energy response of the scanner. The required execution time to acquire 10 million scatter coincidence events for a 3-D thorax PET scan is only 4 min on a 300-MHz Sun dual-processor workstation. We demonstrate that for a low-noise thorax phantom study, image data corrected using the Monte Carlo 3-D PET scatter correction demonstrate no statistically significant deviation from the true activity concentration provided corresponding input data are accurate. The speed and accuracy of our simulation makes it an efficient research tool for studying scatter effects in PET and a practical scatter correction for 3-D PET clinical imaging.

We have been developing Monte Carlo Techniques for calculating primary and scatter photon distributions in PET. Our first goal has been to accelerate the Monte Carlo Code for fast PET simulation. Our second goal has been to use the simulation to analyze scatter effects in PET and explore the potential for use in scatter correction of clinical 3D PET studies. We have reduced the execution time to about 30 minutes or-1 million coincidences per minute on a dual 300MHz processor UltraSparcII workstation. The short execution time makes it feasible to use this technique for 3D PET scatter correction in the clinic. Fast simulation also allows us rapid feedback for the close examination of the accuracy of the method. We present techniques used to improve computational efficiency of Monte Carlo PET simulations. We use the simulation to analyze how scatter from within the body, outside the FOV, and from scanner shielding as well as the chosen energy threshold affect 3D PET sinograms.

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