AN SVD INVESTIGATION OF MODELING SCATTER IN MULTIPLE ENERGY WINDOWS FOR IMPROVED SPECT IMAGES

In this work singular value decomposition (SVD) techniques are used to investigate how the use of low energy photons and multiple energy win dows affects the noise properties of Tc-99m SPECT imaging. We have pre viously shown that, when modeling scatter in the projector and backpro jector of iterative reconstruction algorithms, simultaneous reconstruc tion from multiple energy window data can result in very different noi se characteristics. Further, the properties depend upon the width and number of energy windows used. To investigate this further, we have ge nerated photon transport matrices using models for scatter, an ellipti cal phantom containing cold rods of various sizes, and a number of mul tiple energy window acquisition schemes. Transfer matrices were also g enerated for the cases of perfect scatter rejection and ideal scatter subtraction. The matrices were decomposed using SVD, and signal power and projection space variance spectra were computed using the basis fo rmed by the left singular vectors. Results indicate very different noi se levels for the various energy window combinations. The perfect scat ter rejection case resulted in the lowest variance spectrum, and recon struction-based scatter compensation performed better than the scatter subtraction case. When including lower energy photons in reconstructi on-based scatter compensation, using a series of multiple energy windo ws outperformed a single large energy window. One multiple window comb ination is presented which achieves a lower variance spectrum than the standard 20% energy window indicating the potential for using low ene rgy photons to improve the noise characteristics of SPECT images.