A Statistical Model Relating Image Quality to Image Registration Accuracy in Image-Guided Surgery

Image-guided procedures often rely on the ability to accurately register (i.e., align the coordinate systems of) a preoperative image and an intraoperative image. While the accuracy of this registration step is generally thought to increase with improved image quality (in x-ray CT, for example, achieved at the cost of higher dose), there is little quantitative understanding of how registration accuracy relates to image quality. We present a statistical model that relates factors of spatial resolution, noise, and dose to image registration accuracy (viz. root-mean-squared error in the transform parameters). We further show how this framework may be extended to model how rigid registration of bone structures is affected by deformation of surrounding soft-tissue structures. The model is tested in comparison to experiments performed over a range of dose and deformation magnitude showing accurate agreement in general trends and prediction of optimal registration similarity metric. A statistical foundation for understanding the effect of image quality and soft-tissue deformation is an important step in physics-based modeling of imaging systems and guiding the development of new systems for image-guided procedures.