Multimodal Non-Rigid Registration Methods Based on Demons Models and Local Uncertainty Quantification Used in 3D Brain Images

摘要

In this work, we propose a novel fully automated method to solve the 3D multimodal non-rigid image registration problem. The proposed strategy overcomes the monomodal intensity restriction of fluid-like registration (FLR) models, such as Demons-based registration algorithms, by applying a mapping that relies on an intensity uncertainty quantification in a local neighbourhood, bringing the target and source images into a common domain where they are comparable, no matter their image modalities or mismatched intensities between them. The proposed methodology was tested with T1, T2 and PD weighted brain magnetic resonance (MR) images with synthetic deformations, and CT-MR brain images from a radiotherapy clinical case. The performance of the proposed approach was evaluated quantitatively by standard indices that assess the correct alignment of anatomical structures of interest. The results obtained in this work show that the addition of the local uncertainty mapping properly resolve the monomodal restriction of FLR algorithms when same anatomic counterparts exists in the images to register, and suggest that the proposed strategy can be an option to achieve multimodal 3D registrations.