Andreas Schuh

Differential diagnosis of neurodegenerative diseases using structural MRI data.

Different neurodegenerative diseases can cause memory disorders and other cognitive impairments. The early detection and the stratification of patients according to the underlying disease are essential for an efficient approach to this healthcare challenge. This emphasizes the importance of differential diagnostics. Most studies compare patients and controls, or Alzheimers disease with one other type of dementia. Such a bilateral comparison does not resemble clinical practice, where a clinician is faced with a number of different possible types of dementia. Here we studied which features in structural magnetic resonance imaging (MRI) scans could best distinguish four types of dementia, Alzheimers disease, frontotemporal dementia, vascular dementia, and dementia with Lewy bodies, and control subjects. We extracted an extensive set of features quantifying volumetric and morphometric characteristics from T1 images, and vascular characteristics from FLAIR images. Classification was performed using a multi-class classifier based on Disease State Index methodology. The classifier provided continuous probability indices for each disease to support clinical decision making. A dataset of 504 individuals was used for evaluation. The cross-validated classification accuracy was 70.6% and balanced accuracy was 69.1% for the five disease groups using only automatically determined MRI features. Vascular dementia patients could be detected with high sensitivity (96%) using features from FLAIR images. Controls (sensitivity 82%) and Alzheimers disease patients (sensitivity 74%) could be accurately classified using T1-based features, whereas the most difficult group was the dementia with Lewy bodies (sensitivity 32%). These results were notable better than the classification accuracies obtained with visual MRI ratings (accuracy 44.6%, balanced accuracy 51.6%). Different quantification methods provided complementary information, and consequently, the best results were obtained by utilizing several quantification methods. The results prove that automatic quantification methods and computerized decision support methods are feasible for clinical practice and provide comprehensive information that may help clinicians in the diagnosis making.

The developing human connectome project: A minimal processing pipeline for neonatal cortical surface reconstruction

The Developing Human Connectome Project (dHCP) seeks to create the first 4-dimensional connectome of early life. Understanding this connectome in detail may provide insights into normal as well as abnormal patterns of brain development. Following established best practices adopted by the WU-MINN Human Connectome Project (HCP), and pioneered by FreeSurfer, the project utilises cortical surface-based processing pipelines. In this paper, we propose a fully automated processing pipeline for the structural Magnetic Resonance Imaging (MRI) of the developing neonatal brain. This proposed pipeline consists of a refined framework for cortical and sub-cortical volume segmentation, cortical surface extraction, and cortical surface inflation, which has been specifically designed to address considerable differences between adult and neonatal brains, as imaged using MRI. Using the proposed pipeline our results demonstrate that images collected from 465 subjects ranging from 28 to 45 weeks post-menstrual age (PMA) can be processed fully automatically; generating cortical surface models that are topologically correct, and correspond well with manual evaluations of tissue boundaries in 85% of cases. Results improve on state-of-the-art neonatal tissue segmentation models and significant errors were found in only 2% of cases, where these corresponded to subjects with high motion. Downstream, these surfaces will enhance comparisons of functional and diffusion MRI datasets, supporting the modelling of emerging patterns of brain connectivity.

Multimodal surface matching with higher-order smoothness constraints.

&NA; In brain imaging, accurate alignment of cortical surfaces is fundamental to the statistical sensitivity and spatial localisation of group studies, and cortical surface‐based alignment has generally been accepted to be superior to volume‐based approaches at aligning cortical areas. However, human subjects have considerable variation in cortical folding, and in the location of functional areas relative to these folds. This makes alignment of cortical areas a challenging problem. The Multimodal Surface Matching (MSM) tool is a flexible, spherical registration approach that enables accurate registration of surfaces based on a variety of different features. Using MSM, we have previously shown that driving cross‐subject surface alignment, using areal features, such as resting state‐networks and myelin maps, improves group task fMRI statistics and map sharpness. However, the initial implementation of MSMs regularisation function did not penalize all forms of surface distortion evenly. In some cases, this allowed peak distortions to exceed neurobiologically plausible limits, unless regularisation strength was increased to a level which prevented the algorithm from fully maximizing surface alignment. Here we propose and implement a new regularisation penalty, derived from physically relevant equations of strain (deformation) energy, and demonstrate that its use leads to improved and more robust alignment of multimodal imaging data. In addition, since spherical warps incorporate projection distortions that are unavoidable when mapping from a convoluted cortical surface to the sphere, we also propose constraints that enforce smooth deformation of cortical anatomies. We test the impact of this approach for longitudinal modelling of cortical development for neonates (born between 31 and 43 weeks of post‐menstrual age) and demonstrate that the proposed method increases the biological interpretability of the distortion fields and improves the statistical significance of population‐based analysis relative to other spherical methods. HighlightsAdvances the Multimodal Surface Matching (MSM) method, for cortical surface registration of cortical surfaces, by improving control over the smoothness of the deformation.Enhances alignment of multimodal features, including the feature set used for the Human Connectome Projects parcellation of the human cerebral cortex.Also allows statistical modelling of longitudinal patterns of cortical growth.

Structured Decision Forests for Multi-modal Ultrasound Image Registration

Interventional procedures in cardiovascular diseases often require ultrasound (US) image guidance. These US images must be combined with pre-operatively acquired tomographic images to provide a roadmap for the intervention. Spatial alignment of pre-operative images with intra-operative US images can provide valuable clinical information. Existing multi-modal US registration techniques often do not achieve reliable registration due to low US image quality. To address this problem, a novel medical image representation based on a trained decision forest named probabilistic edge map (PEM) is proposed in this paper. PEMs are generic and modality-independent. They generate similar anatomical representations from different imaging modalities and can thus guide a multi-modal image registration algorithm more robustly and accurately. The presented image registration framework is evaluated on a clinical dataset consisting of 10 pairs of 3D US-CT and 7 pairs of 3D US-MR cardiac images. The experiments show that a registration based on PEMs is able to estimate more reliable and accurate inter-modality correspondences compared to other state-of-the-art US registration methods.

Assessing the relationship between movement and airflow in the upper airway using computational fluid dynamics with motion determined from magnetic resonance imaging

BACKGROUND Computational fluid dynamics simulations of respiratory airflow in the upper airway reveal clinically relevant information, including sites of local resistance, inhaled particle deposition, and the effect of pathological constrictions. Unlike previous simulations, which have been performed on rigid anatomical models from static medical imaging, this work utilises ciné imaging during respiration to create dynamic models and more closely represent airway physiology. METHODS Airway movement maps were obtained from non-rigid image registration of fast-cine MRI and applied to high-spatial-resolution airway surface models. Breathing flowrates were recorded simultaneously with imaging. These data formed the boundary conditions for large eddy simulation computations of the airflow from exterior mask to bronchi. Simulations with rigid geometries were performed to demonstrate the resulting airflow differences between airflow simulations in rigid and dynamic airways. FINDINGS In the analysed rapid breathing manoeuvre, incorporating airway movement significantly changed the findings of the CFD simulations. Peak resistance increased by 19.8% and occurred earlier in the breath. Overall pressure loss decreased by 19.2%, and the proportion of flow in the mouth increased by 13.0%. Airway wall motion was out-of-phase with the air pressure force, demonstrating the presence of neuromuscular motion. In total, the anatomy did 25.2% more work on the air than vice versa. INTERPRETATIONS Realistic movement of the airway is incorporated into CFD simulations of airflow in the upper airway for the first time. This motion is vital to producing clinically relevant computational models of respiratory airflow and will allow novel analysis of dynamic conditions, such as sleep apnoea.

Construction of a 4D Brain Atlas and Growth Model Using Diffeomorphic Registration

Atlases of the human brain have numerous applications in neurological imaging such as the analysis of brain growth. Publicly available atlases of the developing brain have previously been constructed using the arithmetic mean of free-form deformations which were obtained by asymmetric pairwise registration of brain images. Most of these atlases represent cross-sections of the growth process only. In this work, we use the Log-Euclidean mean of inverse consistent transformations which belong to the one-parameter subgroup of diffeomorphisms, as it more naturally represents average morphology. During the registration, similarity is evaluated symmetrically for the images to be aligned. As both images are equally affected by the deformation and interpolation, asymmetric bias is reduced. We further propose to represent longitudinal change by exploiting the numerous transformations computed during the atlas construction in order to derive a deformation model of mean growth. Based on brain images of 118 neonates, we constructed an atlas which describes the dynamics of early development through mean images at weekly intervals and a continuous spatio-temporal deformation. The evolution of brain volumes calculated on preterm neonates is in agreement with recently published findings based on measures of cortical folding of fetuses at the equivalent age range.

Five-class differential diagnostics of neurodegenerative diseases using random undersampling boosting

Differentiating between different types of neurodegenerative diseases is not only crucial in clinical practice when treatment decisions have to be made, but also has a significant potential for the enrichment of clinical trials. The purpose of this study is to develop a classification framework for distinguishing the four most common neurodegenerative diseases, including Alzheimers disease, frontotemporal lobe degeneration, Dementia with Lewy bodies and vascular dementia, as well as patients with subjective memory complaints. Different biomarkers including features from images (volume features, region-wise grading features) and non-imaging features (CSF measures) were extracted for each subject. In clinical practice, the prevalence of different dementia types is imbalanced, posing challenges for learning an effective classification model. Therefore, we propose the use of the RUSBoost algorithm in order to train classifiers and to handle the class imbalance training problem. Furthermore, a multi-class feature selection method based on sparsity is integrated into the proposed framework to improve the classification performance. It also provides a way for investigating the importance of different features and regions. Using a dataset of 500 subjects, the proposed framework achieved a high accuracy of 75.2% with a balanced accuracy of 69.3% for the five-class classification using ten-fold cross validation, which is significantly better than the results using support vector machine or random forest, demonstrating the feasibility of the proposed framework to support clinical decision making.

A deformable model for the reconstruction of the neonatal cortex

We present a method based on deformable meshes for the reconstruction of the cortical surfaces of the developing human brain at the neonatal period. It employs a brain segmentation for the reconstruction of an initial inner cortical surface mesh. Errors in the segmentation resulting from poor tissue contrast in neonatal MRI and partial volume effects are subsequently accounted for by a local edge-based refinement. We show that the obtained surface models define the cortical boundaries more accurately than the segmentation. The surface meshes are further guaranteed to not intersect and subdivide the brain volume into disjoint regions. The proposed method generates topologically correct surfaces which facilitate both a flattening and spherical mapping of the cortex.

Unbiased construction of a temporally consistent morphological atlas of neonatal brain development

Premature birth increases the risk of developing neurocognitive and neurobehavioural disorders. The mechanisms of altered brain development causing these disorders are yet unknown. Studying the morphology and function of the brain during maturation provides us not only with a better understanding of normal development, but may help us to identify causes of abnormal development and their consequences. A particular difficulty is to distinguish abnormal patterns of neurodevelopment from normal variation. The Developing Human Connectome Project (dHCP) seeks to create a detailed four-dimensional (4D) connectome of early life. This connectome may provide insights into normal as well as abnormal patterns of brain development. As part of this project, more than a thousand healthy fetal and neonatal brains will be scanned in vivo. This requires computational methods which scale well to larger data sets. We propose a novel groupwise method for the construction of a spatio-temporal model of mean morphology from cross-sectional brain scans at different gestational ages. This model scales linearly with the number of images and thus improves upon methods used to build existing public neonatal atlases, which derive correspondence between all pairs of images. By jointly estimating mean shape and longitudinal change, the atlas created with our method overcomes temporal inconsistencies, which are encountered when mean shape and intensity images are constructed separately for each time point. Using this approach, we have constructed a spatio-temporal atlas from 275 healthy neonates between 35 and 44 weeks post-menstrual age (PMA). The resulting atlas qualitatively preserves cortical details significantly better than publicly available atlases. This is moreover confirmed by a number of quantitative measures of the quality of the spatial normalisation and sharpness of the resulting template brain images.

Construction of a neonatal cortical surface atlas using Multimodal Surface Matching in the Developing Human Connectome Project

&NA; We propose a method for constructing a spatio‐temporal cortical surface atlas of neonatal brains aged between 36 and 44 weeks of post‐menstrual age (PMA) at the time of scan. The data were acquired as part of the Developing Human Connectome Project (dHCP), and the constructed surface atlases are publicly available. The method is based on a spherical registration approach: Multimodal Surface Matching (MSM), using cortical folding for driving the alignment. Templates have been generated for the anatomical cortical surface and for the cortical feature maps: sulcal depth, curvature, thickness, T1w/T2w myelin maps and cortical regions. To achieve this, cortical surfaces from 270 infants were first projected onto the sphere. Templates were then generated in two stages: first, a reference space was initialised via affine alignment to a group average adult template. Following this, templates were iteratively refined through repeated alignment of individuals to the template space until the variability of the average feature sets converged. Finally, bias towards the adult reference was removed by applying the inverse of the average affine transformations on the template and de‐drifting the template. We used temporal adaptive kernel regression to produce age‐dependant atlases for 9 weeks (36–44 weeks PMA). The generated templates capture expected patterns of cortical development including an increase in gyrification as well as an increase in thickness and T1w/T2w myelination with increasing age. HighlightsCreation of spatio‐temporal cortical surface atlas of the developing brain (36‐44 weeks PMA).Atlas captures patterns of cortical development in the neonatal dHCP population.Includes surface features: sulcal depth, curvature, thickness, T1w/T2w myelin, cortical labels.