Improved regional activity quantitation in nuclear medicine using a new approach to correct for tissue partial volume and spillover effects

We have developed a new method of compensating for effects of partial volume and spillover in dual-modality imaging. The approach requires segmentation of just a few tissue types within a small volume-of-interest (VOI) surrounding a lesion; the algorithm estimates simultaneously, from projection data, the activity concentration within each segmented tissue inside the VOI. Measured emission projections were fitted to the sum of resolution-blurred projections of each such tissue, scaled by its unknown activity concentration, plus a global background contribution obtained by reprojection through the reconstructed image volume outside the VOI. The method was evaluated using multiple-pinhole \mu{\rm SPECT} data simulated for the MOBY mouse phantom containing two spherical lung tumors and one liver tumor, as well as using multiple-bead phantom data acquired on \mu{\rm SPECT} and \mu{\rm CT} scanners. Each VOI in the simulation study was 4.8 mm (12 voxels) cubed and, depending on location, contained up to four tissues (tumor, liver, heart, lung) with different values of relative ^{99{\rm m}}{\rm Tc} concentration. All tumor activity estimates achieved {<}{3\%} bias after {\sim}{15} ordered-subsets expectation maximization (OSEM) iterations (\times 10~{\hbox {subsets}}) , with better than 8% precision ({\leq}{25\%} greater than the Cramer-Rao lower bound). The projection-based fitting approach also outperformed three standardized uptake value (SUV)-like metrics, one of which was corrected for count spillover. In the bead phantom experiment, the mean {\pm} standard deviation of the bias of VOI estimates of bead concentration were 0.9\pm 9.5\%, comparable to those of a perturbation geometric transfer matrix (pGTM) approach ({-}{5.4}\pm 8.6\%); however, VOI estimates were more stable with increasing iteration number than pGTM estimates, even in the presence of substantial axial misalignment between \mu{\rm CT} and \mu{\rm SPECT} image volumes.