Cone Beam Computed Tomography (CBCT) is used to determine patient position before each irradiation, which results in increased radiation exposure for the patient. To reduce the patient exposure, reduction of either X-ray imaging time or X-ray tube current is necessary. However, this leads to increased noise and reduced contrast in images. To meet this requirement, We have developed an image reconstruction method employing an iterative algorithm that is robust to noise and that can achieve high image contrast. In the iterative image reconstruction process, a system matrix corresponding to the measured geometry is required. In gantry-mounted CBCT, the X-ray tube and the flat panel detector (FPD) deviate some from their ideal position, resulting in a different geometry for each measurement angle. However, full calculation of system matrices requires a large data capacity or significant computation time. To overcome these issues, our method calculates the system matrix for an ideal geometry in advance and performs positional deviation correction on the projection data, allowing us to obtain a reconstructed image with positional deviations taken into account. We applied both the Feldkamp method and the proposed method to digital phantom data with positional deviations and Poisson noise, and by comparing the Detectability Index results, we were able to confirm improved noise reduction and image contrast.