Automatic Determination of Anatomical Correspondences for Multimodal Field of View Correction

In spite of a huge body of work in medical image registration, there seems to be very little effort in Field of View (FOV) correction or anatomical overlap estimation especially for multi-modal studies. This is a key step for most registration algorithms to work on image volumes of different coverages. In this work, we consider the FOV correction problem between Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) image volumes for the same patient. A novel algorithm composed of a cascade of a) symmetry based gross rotation/translation correction b) multi-modal feature descriptor and c) matching scheme using dynamic programming is presented. The above combination deals with the challenges of multi-modal studies namely intensity differences, inhomogeneity, and gross patient movement. Validation and comparisons of the proposed algorithm is quantitatively shown on 73 CT-MRI pairs and has yielded promising results.