Whole body dynamic PET imaging has the potential to enhance detectability and quantification when assessing disease stage or progress. The same body region is repeatedly scanned within relatively short acquisition frames and with delays between time samples. Repeated scanning is sensitive to patient motion, which may cause mismatches between attenuation and activity maps and thus erroneous correction factors. Moreover, since count rates change with time, standard software correction factors can be time dependent. The generation of parametric images requires proper physiological modeling and has been shown to benefit from so-called direct 4D reconstruction methods. In this work we extend the MLACF/MLAA algorithms for application to dynamic direct reconstruction. Handling time-dependent normalization requires a redesign of the existing algorithm as well. The reconstruction methodology was verified on Siemens mCT scanner patient data using the standard Patlak model. Different number of frames and scan initiation time points were investigated. Initial results showed that direct 4D reconstruction outperformed the indirect approach. Available CT attenuation information can be corrected based on emission data.