Treatment planning algorithms usually assume that the correct or at least the mean organ position is derived from the CT imaging procedure, and that this position is reproduced throughout the treatment. In reality a mobile organ is unlikely to be in its exact mean position at the time of imaging, causing the treatment to be planned with an organ off-set from its assumed mean position. This introduces an extra 'CT uncertainty' into the treatment. A Monte Carlo (MC) model is used to simulate organ translations at imaging and evaluate the effect of this uncertainty (above the treatment delivery uncertainties) on the dose distribution. An underdose by 4 Gy in a 60 Gy treatment is calculated in the penumbral region of a single-field dose distribution as a result of the CT uncertainty. The effect is reduced to less then 0.5 Gy when the organ position at planning is derived as the average from multiple pretreatment CT scans. It is shown that a convolution method can be applied to predict the effect of CT uncertainty on the dose distribution for a patient population. Additionally, a variation kernel for a convolution method is derived that incorporates uncertainty at both imaging and treatment.