Prion diseases are a group of progressive neurodegenerative conditions which cause cognitive impairment and neurological deficits. To date, there is no accurate measure that can be used to diagnose this illness, or to quantify the evolution of symptoms over time. Prion disease, due to its rarity, is in fact commonly mistaken for other types of dementia. A robust tool to diagnose and quantify the progression of the disease is key as it would lead to more appropriately timed clinical trials, and thereby improve patients’ quality of life. The approaches used to study other types of neurodegenerative diseases are not satisfactory to capture the progression of human form of Prion disease. This is due to the large heterogeneity of phenotypes of Prion disease and to the lack of consistent geometrical pattern of disease progression. In this paper, we aim to identify and select imaging biomarkers that are relevant for the diagnostic on Prion disease. We extract features from magnetic resonance imaging data and use genetic and demographic information from a cohort affected by genetic forms of the disease. The proposed framework consists of a multi-modal subjectspecific feature extraction step, followed by a Gaussian Process classifier used to calculate the probability of a subject to be diagnosed with Prion disease. We show that the proposed method improves the characterisation of Prion disease.
Regional analysis is normally done by fitting models per voxel and then averaging over a region, accounting for partial volume (PV) only to some degree. In thin, folded regions such as the cerebral cortex, such methods do not work well, as the partial volume confounds parameter estimation. Instead, we propose to fit the models per region directly with explicit PV modeling. In this work we robustly estimate region-wise parameters whilst explicitly accounting for partial volume effects. We use a high-resolution segmentation from a T1 scan to assign each voxel in the diffusion image a probabilistic membership to each of k tissue classes. We rotate the DW signal at each voxel so that it aligns with the z-axis, then model the signal at each voxel as a linear superposition of a representative signal from each of the k tissue types. Fitting involves optimising these representative signals to best match the data, given the known probabilities of belonging to each tissue type that we obtained from the segmentation. We demonstrate this method improves parameter estimation in digital phantoms for the diffusion tensor (DT) and ‘Neurite Orientation Dispersion and Density Imaging’ (NODDI) models. The method provides accurate parameter estimates even in regions where the normal approach fails completely, for example where partial volume is present in every voxel. Finally, we apply this model to brain data from preterm infants, where the thin, convoluted, maturing cortex necessitates such an approach.
Metal-on-metal (MoM) hip arthroplasties have been utilised over the last 15 years to restore hip function for 1.5 million patients worldwide. Althoug widely used, this hip arthroplasty releases metal wear debris which lead to muscle atrophy. The degree of muscle wastage differs across patients ranging from mild to severe. The longterm outcomes for patients with MoM hip arthroplasty are reduced for increasing degrees of muscle atrophy, highlighting the need to automatically segment pathological muscles. The automated segmentation of pathological soft tissues is challenging as these lack distinct boundaries and morphologically differ across subjects. As a result, there is no method reported in the literature which has been successfully applied to automatically segment pathological muscles. We propose the first automated framework to delineate severely atrophied muscles by applying a novel automated segmentation propagation framework to patients with MoM hip arthroplasty. The proposed algorithm was used to automatically quantify muscle wastage in these patients.
Dealing with pathological tissues is a very challenging task in medical brain segmentation. The presence of pathology can indeed bias the ultimate results when the model chosen is not appropriate and lead to missegmentations and errors in the model parameters. Model fit and segmentation accuracy are impaired by the lack of flexibility of the model used to represent the data. In this work, based on a finite Gaussian mixture model, we dynamically introduce extra degrees of freedom so that each anatomical tissue considered is modelled as a mixture of Gaussian components. The choice of the appropriate number of components per tissue class relies on a model selection criterion. Its purpose is to balance the complexity of the model with the quality of the model fit in order to avoid overfitting while allowing flexibility. The parameters optimisation, constrained with the additional knowledge brought by probabilistic anatomical atlases, follows the expectation maximisation (EM) framework. Split-and-merge operations bring the new flexibility to the model along with a data-driven adaptation. The proposed methodology appears to improve the segmentation when pathological tissue are present as well as the model fit when compared to an atlas-based expectation maximisation algorithm with a unique component per tissue class. These improvements in the modelling might bring new insight in the characterisation of pathological tissues as well as in the modelling of partial volume effect.
The Free-Form Deformation (FFD) algorithm is a widely used method for non-rigid registration. Modifications
have previously been proposed to ensure topology preservation and invertibility within this framework. However,
in practice, none of these yield the inverse transformation itself, and one loses the parsimonious B-spline
parametrisation. We present a novel log-Euclidean FFD approach in which a spline model of a stationary velocity
field is exponentiated to yield a diffeomorphism, using an efficient scaling-and-squaring algorithm. The
log-Euclidean framework allows easy computation of a consistent inverse transformation, and offers advantages
in group-wise atlas building and statistical analysis. We optimise the Normalised Mutual Information plus a
regularisation term based on the Jacobian determinant of the transformation, and we present a novel analytical
gradient of the latter. The proposed method has been assessed against a fast FFD implementation (F3D) using
simulated T1- and T2-weighted magnetic resonance brain images. The overlap measures between propagated
grey matter tissue probability maps used in the simulations show similar results for both approaches; however,
our new method obtains more reasonable Jacobian values, and yields inverse transformations.
Automatic segmentation of the cerebral cortex from magnetic resonance brain images is a valuable tool for neuroscience
research. Due to the presence of noise, intensity non-uniformity, partial volume effects, the limited
resolution of MRI and the highly convoluted shape of the cerebral cortex, segmenting the brain in a robust,
accurate and topologically correct way still poses a challenge. In this paper we describe a topologically correct
Expectation Maximisation based Maximum a Posteriori segmentation algorithm formulated within the Khalimsky
cubic complex framework, where both the solution of the EM algorithm and the information derived from
a geodesic distance function are used to locally modify the weighting of a Markov Random Field and drive
the topology correction operations. Experiments performed on 20 Brainweb datasets show that the proposed
method obtains a topologically correct segmentation without significant loss in accuracy when compared to two
well established techniques.