High-quality intraoperative 3D imaging systems such as cone-beam CT (CBCT) hold considerable promise for imageguided surgical procedures in the head and neck. With a large amount of preoperative imaging and planning information available in addition to the intraoperative images, it becomes desirable to be able to integrate all sources of imaging information within the same anatomical frame of reference using deformable image registration. Fast intensity-based algorithms are available which can perform deformable image registration within a period of time short enough for intraoperative use. However, CBCT images often contain voxel intensity inaccuracy which can hinder registration accuracy - for example, due to x-ray scatter, truncation, and/or erroneous scaling normalization within the 3D reconstruction algorithm. In this work, we present a method of integrating an iterative intensity matching step within the operation of a multi-scale Demons registration algorithm. Registration accuracy was evaluated in a cadaver model and showed that a conventional Demons implementation (with either no intensity match or a single histogram match) introduced anatomical distortion and degradation in target registration error (TRE). The iterative intensity matching procedure, on the other hand, provided robust registration across a broad range of intensity inaccuracies.