Visualization of uncertainty in fiber tracking based on diffusion tensor imaging

Diffusion tensor imaging (DTI) is an imaging technique based on magnetic resonance that describes, in each point of the tissue, the distribution of diffusing water molecules. The distribution is mathematically modelled using a second-order tensor. In fibrous tissues the diffusion tensor will have an elongated, ellipsoid shape whose main axis is assumed to be aligned with the underlying fiber structure. Fiber tractography traces paths through the tensor field by following each tensor's main direction thereby resulting in a three-dimensional reconstruction of the fibers. This is particularly interesting for the exploration and visualization of neuronal connections in brain white matter and has great potential for applications in neuroscience and neurosurgery. DTI and fiber tractography are unique in that they provide insight into white matter structures in vivo and non-invasively. However, despite these capabilities the application of DTI and fiber tractography in clinical practice remains limited. The image acquisition and post-processing pipeline is complex and consists of many stages. At each stage errors and uncertainties are introduced due to image noise, magnetic distortions, partial volume effects, scanner settings, diffusion model assumptions and user parameters. These uncertainties are propagated through the pipeline and possibly enhanced in subsequent stages thereby leading to potentially unreliable results in the final tractography output. To the user the processing pipeline behaves like a black box whose internal details remain hidden and whose quality of output cannot be reliably assessed. Contrary to standard CT and MR images it is not possible to look at the "raw" diffusion-weighted images. Without further processing the images are practically meaningless. This means the user either has to accept (and trust) the processing output or refrain from using fiber tracking all together. In this thesis we assume that the user has certain reservations about the quality of the tractography output. Unfortunately, there is no gold standard against which the output of tractography can be validated. Consequently, we cannot make definitive statements about the "true" certainty or uncertainty of fiber reconstructions. We can, however, discuss tractography output in terms of stability and reproducibility. The output of tractography algorithms can be subject to large variations. In this thesis we present a number of visualization strategies that make these variations visible to the user and allow a better assessment of the reliability of fiber reconstructions obtained from any given tractography algorithm

"Look at my classifier's result": Disentangling unresponsive from (minimally) conscious patients

Given the fact that clinical bedside examinations can have a high rate of misdiagnosis, machine learning techniques based on neuroimaging and electrophysiological measurements are increasingly being considered for comatose patients and patients with unresponsive wakefulness syndrome, a minimally conscious state or locked-in syndrome. Machine learning techniques have the potential to move from group-level statistical results to personalized predictions in a clinical setting. They have been applied for the purpose of (1) detecting changes in brain activation during functional tasks, equivalent to a behavioral command-following test and (2) estimating signs of consciousness by analyzing measurement data obtained from multiple subjects in resting state. In this review, we provide a comprehensive overview of the literature on both approaches and discuss the translation of present findings to clinical practice. We found that most studies struggle with the difficulty of establishing a reliable behavioral assessment and fluctuations in the patient's levels of arousal. Both these factors affect the training and validation of machine learning methods to a considerable degree. In studies involving more than 50 patients, small to moderate evidence was found for the presence of signs of consciousness or good outcome, where one study even showed strong evidence for good outcome

Fuzzy fibers:uncertainty in dMRI tractography

\u3cp\u3eFiber tracking based on diffusion weighted Magnetic Resonance Imaging (dMRI) allows for noninvasive reconstruction of fiber bundles in the human brain. In this chapter, we discuss sources of error and uncertainty in this technique, and review strategies that afford a more reliable interpretation of the results. This includes methods for computing and rendering probabilistic tractograms, which estimate precision in the face of measurement noise and artifacts. However, we also address aspects that have received less attention so far, such as model selection, partial voluming, and the impact of parameters, both in preprocessing and in fiber tracking itself. We conclude by giving impulses for future research.\u3c/p\u3

Clustering multidimensional sequences in spatial and temporal databases

Many environmental, scientific, technical or medical database applications require effective and efficient mining of time series, sequences or trajectories of measurements taken at different time points and positions forming large temporal or spatial databases. Particularly the analysis of concurrent andmultidimensional sequences poses newchallenges in finding clusters of arbitrary length and varying number of attributes. We present a novel algorithm capable of finding parallel clusters in different subspaces and demonstrate our results for temporal and spatial applications. Our analysis of structural quality parameters in rivers is successfully used by hydrologists to develop measures for river quality improvements

Parameter sensitivity visualization for DTI fiber tracking

Fiber tracking of Diffusion Tensor Imaging (DTI) data offers a unique insight into the three-dimensional organisation of white matter structures in the living brain. However, fiber tracking algorithms require a number of user-defined input parameters that strongly affect the output results. Usually the fiber tracking parameters are set once and are then re-used for several patient datasets. However, the stability of the chosen parameters is not evaluated and a small change in the parameter values can give very differentresults. The user remains completely unaware of such effects. Furthermore, it is difficult to reproduce output results between different users. We propose a visualization tool that allows the user to visually explore how small variations in parameter values affect the output of fiber tracking. With this knowledge the user cannot only assess the stability of commonly used parameter values but also evaluate in a more reliable way the output results between different patients. Existing tools do not provide such information. A small user evaluation of our tool has been done to show the potential of the technique

Illustrative uncertainty visualization for DTI fiber pathways

Diffusion Tensor Imaging (DTI) and fiber tracking provide unique insight into the 3D structure of fibrous tissues in the brain. However, the output of fiber tracking contains a sig- nificant amount of uncertainty accumulated in the various steps of the processing pipeline. Existing DTI visualization methods do not present these uncertainties to the end user. This creates an impression of certainty that can be mislead- ing and even dangerous in applications such as neurosurgery which rely heavily on risk assessment and decision-making. However, adding uncertainty to an already complex visual- ization can easily lead to cognitive overload. In this work we propose illustrative confidence intervals to reduce the com- plexity of the visualization and present only those aspects of uncertainty that are of interest to the user. We look specifi- cally at the uncertainty in fiber shape due to noise and mod- eling errors. Any method that produces a set of streamlines with associated confidence values can be visualized with our framework

Fuzzy fibers: uncertainty in dMRI tractography

Fiber tracking based on diffusion weighted Magnetic Resonance Imaging (dMRI) allows for noninvasive reconstruction of fiber bundles in the human brain. In this chapter, we discuss sources of error and uncertainty in this technique, and review strategies that afford a more reliable interpretation of the results. This includes methods for computing and rendering probabilistic tractograms, which estimate precision in the face of measurement noise and artifacts. However, we also address aspects that have received less attention so far, such as model selection, partial voluming, and the impact of parameters, both in preprocessing and in fiber tracking itself. We conclude by giving impulses for future research

CT-Based Prediction of Liver Function and Post-PVE Hypertrophy Using an Artificial Neural Network

Background: This study aimed to evaluate whether hypertrophy after portal vein embolization (PVE) and maximum liver function capacity (LiMAx) are predictable by an artificial neural network (ANN) model based on computed tomography (CT) texture features.Methods: We report a retrospective analysis on 118 patients undergoing preoperative assessment by CT before and after PVE for subsequent extended liver resection due to a malignant tumor at RWTH Aachen University Hospital. The LiMAx test was carried out in a subgroup of 55 patients prior to PVE. Associations between CT texture features and hypertrophy as well as liver function were assessed by a multilayer perceptron ANN model.Results: Liver volumetry showed a median hypertrophy degree of 33.9% (16.5-60.4%) after PVE. Non-response, defined as a hypertrophy grade lower than 25%, was found in 36.5% (43/118) of the cases. The ANN prediction of the hypertrophy response showed a sensitivity of 95.8%, specificity of 44.4% and overall prediction accuracy of 74.6% (p < 0.001). The observed median LiMAx was 327 (248-433) μg/kg/h and was strongly correlated with the predicted LiMAx (R2 = 0.89).Conclusion: Our study shows that an ANN model based on CT texture features is able to predict the maximum liver function capacity and may be useful to assess potential hypertrophy after performing PVE.Keywords: artificial neural network; computed tomography; liver function; liver volume; portal vein embolization