Application of adaptive kinetic modeling for bias propagation reduction in direct 4D image reconstruction

Direct 4D image reconstruction algorithms can improve kinetic parameter precision and accuracy in dynamic PET/CT body imaging but in contrast to post-reconstruction kinetic analysis, errors in badly modeled regions will spatially propagate to regions which are well modeled. To reduce error propagation from erroneous model fits, we propose a new approach to direct 4D image reconstruction by incorporating a newly proposed kinetic modeling strategy. This uses a secondary model to allow a less constrained model fit in regions where an erroneous kinetic model is used and adaptively include a portion of the residuals back into the image, whilst preserving the primary model characteristics in other well modeled regions. Using a digital 4-D phantom to simulate [¹⁵O]-H₂O kinetics, we demonstrate substantial bias reduction due to propagation in all kinetic parameters using the proposed 4-D method. Under noisy conditions improvements in bias due to propagation are obtained at the expense of a small increase in bias due to noise and selective inclusion of residuals coming from erroneous kinetic modeling, as opposed to noise, becomes more challenging. However, the overall bias is reduced with improvements depending on the proximity of regions of interest to badly modeled regions and the choice of the secondary model space.