Dr. Matthew G. Liptrot Profile

Dr. Matthew G. Liptrot

Post-Doc at Univ of Copenhagen

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Area of Expertise:

Medical Imaging , MRI , Diffusion MRI , Tractography

Publications (2)

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Proceedings Article | 21 March 2016 Paper

Rotationally invariant clustering of diffusion MRI data using spherical harmonics

Matthew Liptrot, François Lauze

Proceedings Volume 9784, 97843C (2016) https://doi.org/10.1117/12.2217090

KEYWORDS: Diffusion, Diffusion weighted imaging, Brain, Data modeling, Diffusion magnetic resonance imaging, Magnetic resonance imaging, Tissues, Performance modeling

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We present a simple approach to the voxelwise classification of brain tissue acquired with diffusion weighted MRI (DWI). The approach leverages the power of spherical harmonics to summarise the diffusion information, sampled at many points over a sphere, using only a handful of coefficients. We use simple features that are invariant to the rotation of the highly orientational diffusion data. This provides a way to directly classify voxels whose diffusion characteristics are similar yet whose primary diffusion orientations differ. Subsequent application of machine-learning to the spherical harmonic coefficients therefore may permit classification of DWI voxels according to their inferred underlying fibre properties, whilst ignoring the specifics of orientation. After smoothing apparent diffusion coefficients volumes, we apply a spherical harmonic transform, which models the multi-directional diffusion data as a collection of spherical basis functions. We use the derived coefficients as voxelwise feature vectors for classification. Using a simple Gaussian mixture model, we examined the classification performance for a range of sub-classes (3-20). The results were compared against existing alternatives for tissue classification e.g. fractional anisotropy (FA) or the standard model used by Camino.1 The approach was implemented on both two publicly-available datasets: an ex-vivo pig brain and in-vivo human brain from the Human Connectome Project (HCP). We have demonstrated how a robust classification of DWI data can be performed without the need for a model reconstruction step. This avoids the potential confounds and uncertainty that such models may impose, and has the benefit of being computable directly from the DWI volumes. As such, the method could prove useful in subsequent pre-processing stages, such as model fitting, where it could inform about individual voxel complexities and improve model parameter choice.

Proceedings Article | 21 March 2016 Paper

Supervised hub-detection for brain connectivity

Niklas Kasenburg, Matthew Liptrot, Nina Linde Reislev, Ellen Garde, Mads Nielsen, Aasa Feragen

Proceedings Volume 9784, 978409 (2016) https://doi.org/10.1117/12.2216186

KEYWORDS: Data modeling, Brain, Neuroimaging, Algorithm development, Machine learning, Diffusion weighted imaging, Network security, Statistical analysis, Visualization, Distortion

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A structural brain network consists of physical connections between brain regions. Brain network analysis aims to find features associated with a parameter of interest through supervised prediction models such as regression. Unsupervised preprocessing steps like clustering are often applied, but can smooth discriminative signals in the population, degrading predictive performance. We present a novel hub-detection optimized for supervised learning that both clusters network nodes based on population level variation in connectivity and also takes the learning problem into account. The found hubs are a low-dimensional representation of the network and are chosen based on predictive performance as features for a linear regression. We apply our method to the problem of finding age-related changes in structural connectivity. We compare our supervised hub-detection (SHD) to an unsupervised hub-detection and a linear regression using the original network connections as features. The results show that the SHD is able to retain regression performance, while still finding hubs that represent the underlying variation in the population. Although here we applied the SHD to brain networks, it can be applied to any network regression problem. Further development of the presented algorithm will be the extension to other predictive models such as classification or non-linear regression.

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