Multiscale Unbiased Diffeomorphic Atlas Construction on Multi-GPUs

Ha, Linh K.; Krüger, Jens; Joshi, Sarang; Silva, Cláudio T.

doi:10.1016/b978-0-12-384988-5.00048-6

Cited by 25 publications

(36 citation statements)

References 9 publications

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“…Our implementation of geodesic regression and atlas building is developed based on MPI and the GPU image processing framework by [10]. We evaluate our proposed shooting method using synthetic and real 3D-structural MRI data both for the geodesic regression and the atlas construction problem.…”

Section: Resultsmentioning

confidence: 99%

A vector momenta formulation of diffeomorphisms for improved geodesic regression and atlas construction

Singh

Hinkle

Joshi

et al. 2013

2013 IEEE 10th International Symposium on Biomedical Imaging

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This paper presents a novel approach for diffeomorphic image regression and atlas estimation that results in improved convergence and numerical stability. We use a vector momenta representation of a diffeomorphism's initial conditions instead of the standard scalar momentum that is typically used. The corresponding variational problem results in a closed-form update for template estimation in both the geodesic regression and atlas estimation problems. While we show that the theoretical optimal solution is equivalent to the scalar momenta case, the simplification of the optimization problem leads to more stable and efficient estimation in practice. We demonstrate the effectiveness of our method for atlas estimation and geodesic regression using synthetically generated shapes and 3D MRI brain scans.

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Section: Resultsmentioning

confidence: 99%

A vector momenta formulation of diffeomorphisms for improved geodesic regression and atlas construction

Singh

Hinkle

Joshi

et al. 2013

2013 IEEE 10th International Symposium on Biomedical Imaging

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“…Thus, we begin by discussing the computational cost and times for matching tasks between two 3D brain images SPM, elastic registration, and diffeomorphic registration. Diffeomorphic registration between two brain MR images with 200 The experiments in this paper used a GPU implementation [9] of LDDMM (roughly 10 minutes required). In real systems to be deployed one would implement SPM on a parallel architecture such as a GPU (a 100× speedup over Matlab is expected).…”

Section: Resultsmentioning

confidence: 99%

Fast Shape-Based Nearest-Neighbor Search for Brain MRIs Using Hierarchical Feature Matching

Zhu

Awate

Gerber

et al. 2011

Lecture Notes in Computer Science

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Abstract. This paper presents a fast method for quantifying shape differences/similarities between pairs of magnetic resonance (MR) brain images. Most shape comparisons in the literature require some kind of deformable registration or identification of exact correspondences. The proposed approach relies on an optimal matching of a large collection of features, using a very fast, hierarchical method from the literature, called spatial pyramid matching (SPM). This paper shows that edge-based image features in combination with SPM results in a fast similarity measure that captures relevant anatomical information in brain MRI. We present extensive comparisons against known methods for shape-based, k-nearest-neighbor lookup to evaluate the performance of the proposed method. Finally, we show that the method compares favorably with more computation-intensive methods in the construction of local atlases for use in brain MR image segmentation.

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“…Further, the parallel nature of many of the image processing algorithms used in the deformation update process lend themselves to an efficient and massively parallel GPU-based implementation. An implementation of LDDMM atlas building for use on a GPU computing cluster was therefore developed, based on MPI and the GPU image processing framework by Ha et al (2009). Individual deformation calculations are distributed across computing nodes, and nodes further distribute deformation calculations among GPUs.…”

Section: Methodsmentioning

confidence: 99%

Quantifying anatomical shape variations in neurological disorders

Singh

Fletcher

Preston

et al. 2014

Medical Image Analysis

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We develop a multivariate analysis of brain anatomy to identify the relevant shape deformation patterns and quantify the shape changes that explain corresponding variations in clinical neuropsychological measures. We use kernel Partial Least Squares (PLS) and formulate a regression model in the tangent space of the manifold of diffeomorphisms characterized by deformation momenta. The scalar deformation momenta completely encode the diffeomorphic changes in anatomical shape. In this model, the clinical measures are the response variables, while the anatomical variability is treated as the independent variable. To better understand the “shape—clinical response” relationship, we also control for demographic confounders, such as age, gender, and years of education in our regression model. We evaluate the proposed methodology on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database using baseline structural MR imaging data and neuropsychological evaluation test scores. We demonstrate the ability of our model to quantify the anatomical deformations in units of clinical response. Our results also demonstrate that the proposed method is generic and generates reliable shape deformations both in terms of the extracted patterns and the amount of shape changes. We found that while the hippocampus and amygdala emerge as mainly responsible for changes in test scores for global measures of dementia and memory function, they are not a determinant factor for executive function. Another critical finding was the appearance of thalamus and putamen as most important regions that relate to executive function. These resulting anatomical regions were consistent with very high confidence irrespective of the size of the population used in the study. This data-driven global analysis of brain anatomy was able to reach similar conclusions as other studies in Alzheimer’s Disease based on predefined ROIs, together with the identification of other new patterns of deformation. The proposed methodology thus holds promise for discovering new patterns of shape changes in the human brain that could add to our understanding of disease progression in neurological disorders.

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Multiscale Unbiased Diffeomorphic Atlas Construction on Multi-GPUs

Cited by 25 publications

References 9 publications

A vector momenta formulation of diffeomorphisms for improved geodesic regression and atlas construction

A vector momenta formulation of diffeomorphisms for improved geodesic regression and atlas construction

Fast Shape-Based Nearest-Neighbor Search for Brain MRIs Using Hierarchical Feature Matching

Quantifying anatomical shape variations in neurological disorders

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