Nishant Ravikumar

Magnetic resonance elastography of the brain: An in silico study to determine the influence of cranial anatomy.

Magnetic resonance elastography (MRE) of the brain has demonstrated potential as a biomarker of neurodegenerative disease such as dementia but requires further evaluation. Cranial anatomical features such as the falx cerebri and tentorium cerebelli membranes may influence MRE measurements through wave reflection and interference and tissue heterogeneity at their boundaries. We sought to determine the influence of these effects via simulation.

A constitutive model for ballistic gelatin at surgical strain rates.

This paper describes a constitutive model for ballistic gelatin at the low strain rates experienced, for example, by soft tissues during surgery. While this material is most commonly associated with high speed projectile penetration and impact investigations, it has also been used extensively as a soft tissue simulant in validation studies for surgical technologies (e.g. surgical simulation and guidance systems), for which loading speeds and the corresponding mechanical response of the material are quite different. We conducted mechanical compression experiments on gelatin specimens at strain rates spanning two orders of magnitude (~0.001-0.1s(-1)) and observed a nonlinear load-displacement history and strong strain rate-dependence. A compact and efficient visco-hyperelastic constitutive model was then formulated and found to fit the experimental data well. An Ogden type strain energy density function was employed for the elastic component. A single Prony exponential term was found to be adequate to capture the observed rate-dependence of the response over multiple strain rates. The model lends itself to immediate use within many commercial finite element packages.

Evaluation of wave delivery methodology for brain MRE: Insights from computational simulations

MR elastography (MRE) of the brain is being explored as a biomarker of neurodegenerative disease such as dementia. However, MRE measures for healthy brain have varied widely. Differing wave delivery methodologies may have influenced this, hence finite element‐based simulations were performed to explore this possibility.

Robust group-wise rigid registration of point sets using t-mixture model

A probabilistic framework for robust, group-wise rigid alignment of point-sets using a mixture of Students t-distribution especially when the point sets are of varying lengths, are corrupted by an unknown degree of outliers or in the presence of missing data. Medical images (in particular magnetic resonance (MR) images), their segmentations and consequently point-sets generated from these are highly susceptible to corruption by outliers. This poses a problem for robust correspondence estimation and accurate alignment of shapes, necessary for training statistical shape models (SSMs). To address these issues, this study proposes to use a t-mixture model (TMM), to approximate the underlying joint probability density of a group of similar shapes and align them to a common reference frame. The heavy-tailed nature of t-distributions provides a more robust registration framework in comparison to state of the art algorithms. Significant reduction in alignment errors is achieved in the presence of outliers, using the proposed TMM-based group-wise rigid registration method, in comparison to its Gaussian mixture model (GMM) counterparts. The proposed TMM-framework is compared with a group-wise variant of the well-known Coherent Point Drift (CPD) algorithm and two other group-wise methods using GMMs, using both synthetic and real data sets. Rigid alignment errors for groups of shapes are quantified using the Hausdorff distance (HD) and quadratic surface distance (QSD) metrics.

Reconstruction of Coronary Artery Centrelines from X-Ray Angiography Using a Mixture of Student’s t-Distributions

Three-dimensional reconstructions of coronary arteries can overcome some of the limitations of 2D X-ray angiography, namely artery overlap/foreshortening and lack of depth information. Model-based arterial reconstruction algorithms usually rely on 2D coronary artery segmentations and require good robustness to outliers. In this paper, we propose a novel probabilistic method to reconstruct coronary artery centrelines from retrospectively gated X-ray images based on a probabilistic mixture model. Specifically, 3D coronary artery centrelines are described by a mixture of Student’s t-distributions, and the reconstruction is formulated as maximum-likelihood estimation of the mixture model parameters, given the 2D segmentations of arteries from 2D X-ray images. Our method provides robustness against the erroneously segmented parts in the 2D segmentations by taking advantage of the inherent robustness of t-distributions. We validate our reconstruction results using synthetic phantom and clinical X-ray angiography data. The results show that the proposed method can cope with imperfect and noisy segmentation data.

A Multi-resolution T-Mixture Model Approach to Robust Group-Wise Alignment of Shapes

A novel probabilistic, group-wise rigid registration framework is proposed in this study, to robustly align and establish correspondence across anatomical shapes represented as unstructured point sets. Student’s t-mixture model (TMM) is employed to exploit their inherent robustness to outliers. The primary application for such a framework is the automatic construction of statistical shape models (SSMs) of anatomical structures, from medical images. Tools used for automatic segmentation and landmarking of medical images often result in segmentations with varying proportions of outliers. The proposed approach is able to robustly align shapes and establish valid correspondences in the presence of considerable outliers and large variations in shape. A multi-resolution registration (mrTMM) framework is also formulated, to further improve the performance of the proposed TMM-based registration method. Comparisons with a state-of-the art approach using clinical data show that the mrTMM method in particular, achieves higher alignment accuracy and yields SSMs that generalise better to unseen shapes.

Group-wise similarity registration of point sets using Student’s t-mixture model for statistical shape models

HighlightsGroup‐wise similarity point set registration using Students t‐mixture model.Automatic robustness to outliers.Automatic construction of statistical shape models.Improved registration accuracy and SSM quality compared to Gaussian mixture models. Graphical abstract Figure. No Caption available. Abstract A probabilistic group‐wise similarity registration technique based on Student’s t‐mixture model (TMM) and a multi‐resolution extension of the same (mr‐TMM) are proposed in this study, to robustly align shapes and establish valid correspondences, for the purpose of training statistical shape models (SSMs). Shape analysis across large cohorts requires automatic generation of the requisite training sets. Automated segmentation and landmarking of medical images often result in shapes with varying proportions of outliers and consequently require a robust method of alignment and correspondence estimation. Both TMM and mrTMM are validated by comparison with state‐of‐the‐art registration algorithms based on Gaussian mixture models (GMMs), using both synthetic and clinical data. Four clinical data sets are used for validation: (a) 2D femoral heads (Symbol 1000 samples generated from DXA images of healthy subjects); (b) control‐hippocampi (Symbol 50 samples generated from T1‐weighted magnetic resonance (MR) images of healthy subjects); (c) MCI‐hippocampi (Symbol 28 samples generated from MR images of patients diagnosed with mild cognitive impairment); and (d) heart shapes comprising left and right ventricular endocardium and epicardium (Symbol 30 samples generated from short‐axis MR images of: 10 healthy subjects, 10 patients diagnosed with pulmonary hypertension and 10 diagnosed with hypertrophic cardiomyopathy). The proposed methods significantly outperformed the state‐of‐the‐art in terms of registration accuracy in the experiments involving synthetic data, with mrTMM offering significant improvement over TMM. With the clinical data, both methods performed comparably to the state‐of‐the‐art for the hippocampi and heart data sets, which contained few outliers. They outperformed the state‐of‐the‐art for the femur data set, containing large proportions of outliers, in terms of alignment accuracy, and the quality of SSMs trained, quantified in terms of generalization, compactness and specificity. Symbol. No caption available.

Subject-specific multi-poroelastic model for exploring the risk factors associated with the early stages of Alzheimer's disease

There is emerging evidence suggesting that Alzheimers disease is a vascular disorder, caused by impaired cerebral perfusion, which may be promoted by cardiovascular risk factors that are strongly influenced by lifestyle. In order to develop an understanding of the exact nature of such a hypothesis, a biomechanical understanding of the influence of lifestyle factors is pursued. An extended poroelastic model of perfused parenchymal tissue coupled with separate workflows concerning subject-specific meshes, permeability tensor maps and cerebral blood flow variability is used. The subject-specific datasets used in the modelling of this paper were collected as part of prospective data collection. Two cases were simulated involving male, non-smokers (control and mild cognitive impairment (MCI) case) during two states of activity (high and low). Results showed a marginally reduced clearance of cerebrospinal fluid (CSF)/interstitial fluid (ISF), elevated parenchymal tissue displacement and CSF/ISF accumulation and drainage in the MCI case. The peak perfusion remained at 8 mm s−1 between the two cases.

Generalised Coherent Point Drift for Group-Wise Registration of Multi-dimensional Point Sets

In this paper we propose a probabilistic approach to group-wise registration of unstructured high-dimensional point sets. We focus on registration of generalised point sets which encapsulate both the positions of points on surface boundaries and corresponding normal vectors describing local surface geometry. Richer descriptions of shape can be especially valuable in applications involving complex and intricate variations in geometry, where spatial position alone is an unreliable descriptor for shape registration. A hybrid mixture model combining Student’s t and Von-Mises-Fisher distributions is proposed to model position and orientation components of the point sets, respectively. A group-wise rigid and non-rigid registration framework is then formulated on this basis. Two clinical data sets, comprising 27 brain ventricle and 15 heart shapes, were used to assess registration accuracy. Significant improvement in accuracy and anatomical validity of the estimated correspondences was achieved using the proposed approach, relative to state-of-the-art point set registration approaches, which consider spatial positions alone.

3D active shape models of human brain structures: application to patient-specific mesh generation

The use of biomechanics-based numerical simulations has attracted growing interest in recent years for computer-aided diagnosis and treatment planning. With this in mind, a method for automatic mesh generation of brain structures of interest, using statistical models of shape (SSM) and appearance (SAM), for personalised computational modelling is presented. SSMs are constructed as point distribution models (PDMs) while SAMs are trained using intensity profiles sampled from a training set of T1-weighted magnetic resonance images. The brain structures of interest are, the cortical surface (cerebrum, cerebellum & brainstem), lateral ventricles and falx-cerebri membrane. Two methods for establishing correspondences across the training set of shapes are investigated and compared (based on SSM quality): the Coherent Point Drift (CPD) point-set registration method and B-spline mesh-to-mesh registration method. The MNI-305 (Montreal Neurological Institute) average brain atlas is used to generate the template mesh, which is deformed and registered to each training case, to establish correspondence over the training set of shapes. 18 healthy patients’ T1-weightedMRimages form the training set used to generate the SSM and SAM. Both model-training and model-fitting are performed over multiple brain structures simultaneously. Compactness and generalisation errors of the BSpline-SSM and CPD-SSM are evaluated and used to quantitatively compare the SSMs. Leave-one-out cross validation is used to evaluate SSM quality in terms of these measures. The mesh-based SSM is found to generalise better and is more compact, relative to the CPD-based SSM. Quality of the best-fit model instance from the trained SSMs, to test cases are evaluated using the Hausdorff distance (HD) and mean absolute surface distance (MASD) metrics.