Simultaneous Multi-scale Registration Using Large Deformation Diffeomorphic Metric Mapping

• Published in: IEEE transactions on medical imaging Vol. 30; no. 10; pp. 1746 - 1759
• Main Authors: Risser, L., Vialard, F., Wolz, R., Murgasova, M., Holm, D. D., Rueckert, D.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2011
• Subjects: Algorithms; Alzheimer Disease - pathology; Biomedical imaging; Brain - anatomy & histology; Brain - pathology; Diffeomorphic registration; Humans; image comparison; Image Processing, Computer-Assisted - methods; Infant, Newborn; Infant, Premature; Kernel; large deformation diffeomorphic metric mapping (LDDMM); Magnetic Resonance Imaging - methods; Measurement; multi-scale; Phantoms, Imaging; Pixel; Shape; smoothing kernel; Smoothing methods; Statistics, Nonparametric; Three dimensional displays
• ISSN: 0278-0062; 1558-254X; 1558-254X
• DOI: 10.1109/TMI.2011.2146787

In the framework of large deformation diffeomorphic metric mapping (LDDMM), we present a practical methodology to integrate prior knowledge about the registered shapes in the regularizing metric. Our goal is to perform rich anatomical shape comparisons from volumetric images with the mathematical properties offered by the LDDMM framework. We first present the notion of characteristic scale at which image features are deformed. We then propose a methodology to compare anatomical shape variations in a multi-scale fashion, i.e., at several characteristic scales simultaneously. In this context, we propose a strategy to quantitatively measure the feature differences observed at each characteristic scale separately. After describing our methodology, we illustrate the performance of the method on phantom data. We then compare the ability of our method to segregate a group of subjects having Alzheimer's disease and a group of controls with a classical coarse to fine approach, on standard 3D MR longitudinal brain images. We finally apply the approach to quantify the anatomical development of the human brain from 3D MR longitudinal images of pre-term babies. Results show that our method registers accurately volumetric images containing feature differences at several scales simultaneously with smooth deformations.