Tensor grid based image registration with application to ventilation estimation on 4D CT lung data

Abstract

For many image registration tasks, the information contained in the original resolution of the image data is crucial for a subsequent medical analysis, e.g. accurate assessment of local pulmonary ventilation. However, the complexity of a non-parametric registration scheme is directly connected to the resolution of the images. Therefore, the registration is often performed on a downsampled version in order to meet runtime demands and thereby producing suboptimal results. To enable the application of the highest resolution at least in regions of high clinical importance, an approach is presented replacing the usually taken equidistant grids by tensor grids for image representation. We employ a non-parametric approach for the registration of a respiratory-gated 4D CT thorax scan. Tensor grids are introduced for the registration setting and compared to equidistant grids. For ventilation assessment, the Jacobian metric is explored. The application of the tensor grid approach makes the local usage of the original resolution feasible; thereby a smaller registration error is achieved in regions of higher resolution using the tensor grids, while the two types of grids perform similar in regions of equal resolution. Concerning the ventilation assessment, the Jacobian metric yields reasonable results, showing more detail using the tensor grids due to the higher resolution. The proposed approach using tensor grids preserves registration accuracy, while reducing computational demands. The application of the Jacobian metric for ventilation assessment in conjunction with tensor grids is promising; however, due to a missing ground-truth the medical relevance could not be established for the ventilation estimation so far.