Mapping Multi-Modal Routine Imaging Data to a Single Reference via Multiple Templates

Abstract

Population level analysis of medical imaging data relies on finding spatial correspondence across individuals as a basis for local comparison of visual characteristics. Here, we describe and evaluate a framework to normalize routine images covering different parts of the human body, in different modalities to a common reference space. The framework performs two basic steps towards normalization: (1) The identification of the location and coverage of the human body by an image and (2) a non-linear mapping to the common reference space. Based on these mappings, either coordinates, or label-masks can be transferred across a population of images. We evaluate the framework on a set of routine CT and MR scans exhibiting large variability on location and coverage. A set of manually annotated landmarks is used to assess the accuracy and stability of the approach. We report distinct improvement in stability and registration accuracy compared to a classical single-atlas approach.