期刊
IEEE TRANSACTIONS ON MEDICAL IMAGING
卷 30, 期 10, 页码 1746-1759出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2011.2146787
关键词
Diffeomorphic registration; image comparison; large deformation diffeomorphic metric mapping (LDDMM); multi-scale; smoothing kernel
类别
资金
- Imperial College
- Royal Society of London
- European Research Council
- European Union
- Alzheimer's Disease Neuroimaging Initiative (ADNI)
- NIH [U01 AG024904]
- National Institute on Aging
- National Institute of Biomedical Imaging and Bioengineering (NIBIB)
- Engineering and Physical Sciences Research Council [EP/H046410/1, EP/F011830/1] Funding Source: researchfish
- EPSRC [EP/H046410/1, EP/F011830/1] Funding Source: UKRI
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.
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