Simultaneous Fine and Coarse Diffeomorphic Registration: Application to Atrophy Measurement in Alzheimer’s Disease

Institute for Mathematical Science, Imperial College London, 53 Prince's Gate, SW7 2PG London, UK.
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention 09/2010; 13(Pt 2):610-7. DOI: 10.1007/978-3-642-15745-5_75
Source: PubMed


In this paper, we present a fine and coarse approach for the multiscale registration of 3D medical images using Large Deformation Diffeomorphic Metric Mapping (LDDMM). This approach has particularly interesting properties since it estimates large, smooth and invertible optimal deformations having a rich descriptive power for the quantification of temporal changes in the images. First, we show the importance of the smoothing kernel and its influence on the final solution. We then propose a new strategy for the spatial regularization of the deformations, which uses simultaneously fine and coarse smoothing kernels. We have evaluated the approach on both 2D synthetic images as well as on 3D MR longitudinal images out of the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. Results highlight the regularizing properties of our approach for the registration of complex shapes. More importantly, the results also demonstrate its ability to measure shape variations at several scales simultaneously while keeping the desirable properties of LDDMM. This opens new perspectives for clinical applications.

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Available from: Darryl Holm, Sep 05, 2014
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    • "In particular, our methods have demonstrated localized shape differences in multiple sub-cortical structures in neuroimaging studies of Alzheimer's Disease (Qiu et al., 2008a, 2009c), ADHD (Qiu et al., 2009b), Autism (Qiu et al., 2010), schizophrenia (Wang et al., 2008), and Tourette Syndrome (Wang et al., 2007). The theoretical framework we have adopted is based on the large deformation diffeomorphic metric mapping (LDDMM) algorithm (Beg et al., 2005) and advances have been developed by others as well (Risser et al., 2010, 2011; Auzias et al., 2011). It also has been demonstrated that the best registration methods have high dimensional and diffeomorphic properties that have in common many of the properties incorporated into LDDMM (Klein et al., 2009, 2010). "
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    ABSTRACT: One goal of computational anatomy (CA) is to develop tools to accurately segment brain structures in healthy and diseased subjects. In this paper, we examine the performance and complexity of such segmentation in the framework of the large deformation diffeomorphic metric mapping (LDDMM) registration method with reference to atlases and parameters. First we report the application of a multi-atlas segmentation approach to define basal ganglia structures in healthy and diseased kids' brains. The segmentation accuracy of the multi-atlas approach is compared with the single atlas LDDMM implementation and two state-of-the-art segmentation algorithms-Freesurfer and FSL-by computing the overlap errors between automatic and manual segmentations of the six basal ganglia nuclei in healthy subjects as well as subjects with diseases including ADHD and Autism. The high accuracy of multi-atlas segmentation is obtained at the cost of increasing the computational complexity because of the calculations necessary between the atlases and a subject. Second, we examine the effect of parameters on total LDDMM computation time and segmentation accuracy for basal ganglia structures. Single atlas LDDMM method is used to automatically segment the structures in a population of 16 subjects using different sets of parameters. The results show that a cascade approach and using fewer time steps can reduce computational complexity as much as five times while maintaining reliable segmentations.
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    • "On the other hand, including multiple scales and kernel shapes in the LDDMM framework has been the subject of several works [2] [11] [15] [14] [12]. The common goal has been to model deformation occurring at multiple scales resulting in improved registration results and scale-aware statistics. "
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    ABSTRACT: In order to detect small-scale deformations during disease propagation while allowing large-scale deformation needed for inter-subject registration, we wish to model deformation at multiple scales and represent the deformation at the relevant scales only. With the LDDMM registration framework, enforcing sparsity results in compact representations but with limited ability to represent deformation across scales. In contrast, the LDDKBM extension of LDDMM allows representations of deformation at multiple scales but it does not favour compactness and hence may represent deformation at more scales than necessary. In this paper, we combine a sparsity prior with the multi-scale framework resulting in an algorithm allowing compact representation of deformation across scales. We present a mathematical formulation of the algorithm and evaluate it on a dataset of annotated lung CT images.
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