Patient motion is a common problem during dynamic PET scans for quantification of myocardial blood flow (MBF). The purpose of this study was to quantify the prevalence of body motion in a clinical setting, and evaluate with realistic phantoms the effects of motion on blood flow quantification, including CT attenuation correction artifacts that result from PET-CT misalignment. In addition we evaluated a consistency-based motion correction technique using noise added simulations. A cohort of 236 sequential patients was analyzed for patient motion under resting and peak stress conditions by two independent observers. The presence of motion, affected time frames, and direction of motion was recorded. Based on these results, patient body motion effects on MBF quantification were characterized using the digital NURBS-based Cardiac-Torso (NCAT) phantom, with characteristic time activity curves (TAC) assigned to the heart wall (myocardium) and blood regions. Noise was added to sinograms using the analytical simulator ASIM. In the patient cohort mild motion 0.5 ± 0.1 cm occurred in 24%, and severe motion 1.0 ± 0.3 cm occurred in 38% of patients. Motion in the superior/inferior direction accounted for 45% of all detected motion. Anterior/posterior direction motion accounted for 29%, and left/right motion occurred in 24% of cases. Computer simulation studies indicated that errors in MBF can approach 500% for scans with severe patient motion (up to 2 cm). The largest errors occurred when the heart wall was shifted left towards the adjacent lung region. Body motion effects were more detrimental for higher resolution PET imaging (2 vs 10 mm FWHM), for the physically smaller phantom, and for motion occurring during the mid-to-late time frames. Motion correction of the reconstructed dynamic image series resulted in significant reduction in MBF errors. MBF bias was reduced further using global partial-volume correction, and using dynamic alignment of the PET projection data to the CT scan for accurate attenuation correction during image reconstruction. To reduce MBF errors, new motion correction algorithms must be effective in identifying motion in the left/right direction, and in the mid-to-late time frames, since these conditions produce the largest errors in MBF.