Peter Gemmar

Implantation of electrodes for deep brain stimulation of the subthalamic nucleus in advanced Parkinson's disease with the aid of intraoperative microrecording under general anesthesia.

OBJECTIVEDeep brain stimulation (DBS) is widely accepted in the treatment of advanced Parkinsons disease (PD) and other movement disorders. The standard implantation procedure is performed under local anesthesia (LA). Certain groups of patients may not be eligible for surgery under LA because of clinical reasons, such as massive fear, reduced cooperativity, or coughing attacks. Microrecording (MER) has been shown to be helpful in DBS surgery. The purpose of this study was to evaluate the feasibility of MER for DBS surgery under general anesthesia (GA) and to compare the data of intraoperative MER as well as the clinical data with that of the current literature of patients undergoing operation under LA. CLINICAL PRESENTATIONThe data of nine patients with advanced PD (mean Hoehn and Yahr status, 4.2) who were operated with subthalamic nucleus (STN) DBS under GA, owing to certain clinical circumstances ruling out DBS under LA, were retrospectively analyzed. All operations were performed under analgosedation with propofol or remifentanil and intraoperative MER. For MER, remifentanil was ceased completely and propofol was lowered as far as possible. INTERVENTIONThe STN could be identified intraoperatively in all patients with MER. The typical bursting pattern was identified, whereas a widening of the baseline noise could not be as adequately detected as in patients under LA. The daily off phases of the patients were reduced from 50 to 17%, whereas the Unified Parkinsons Disease Rating Scale III score was reduced from 43 (preoperative, medication off) to 19 (stimulation on, medication off) and 12 (stimulation on, medication on). Two patients showed a transient neuropsychological deterioration after surgery, but both also had preexisting episodes of disorientation. One implantable pulse generator infection was noticed. No further significant clinical complications were observed. CONCLUSIONSTN surgery for advanced PD with MER guidance is possible with good clinical results under GA. Intraoperative MER of the STN region can be performed under GA with a special anesthesiological protocol. In this setting, the typical STN bursting pattern can be identified, whereas the typical widening of the background noise baseline while entering the STN region is obviously absent. This technique may enlarge the group of patients eligible for STN surgery. Although the clinical improvements and parameter settings in this study were within the range of the current literature, further randomized controlled studies are necessary to compare the results of STN DBS under GA and LA, respectively.

Qualitative and quantitative analysis of voice onset by means of a multidimensional voice analysis system (MVAS) using high-speed imaging

High-speed filming is one of the most informative methods for assessing voice physiology data. Tracing high-speed images of the glottis provides quantitative parameters such as the glottal area and the glottal width function. By way of example, a number of studies are discussed which extract quantitative data from high-speed images showing voice onsets. Furthermore, a new computer system (MVAS; multi-dimensional voice analysis system) is presented that synchronously displays a laryngoscopic high-speed film, the electroglottographical signal, and several acoustic analyses of the recorded voice sample. The automatic measurement of glottal width and glottal area from the laryngoscopic images is also provided. Looking at former studies and our analyses of voice onsets reveals a tremendous intersubject and even intrasubject variability (different prephonatory closure, different time span until full amplitude is reached, different open quotient).

Advanced Methods for Target Navigation using Microelectrode Recordings in Stereotactic Neurosurgery for Deep Brain Stimulation

This study describes novel methods for navigating and placing of electrodes into specific structures in the basal ganglia for deep brain stimulation (DBS), as it is common in the treatment of Parkinsons disease. Critical to these procedures in neurosurgery is the localization and identification of different target structures such as subthalamic nucleus (STN) along the electrodes trajectory and finding the best position for the stimulating electrode. Typically, microelectrode recordings (MER) of local neural activity along up to five parallel trajectories are used by neurosurgeons for detecting the target region and creating the anatomic positions of the electrodes by imagination. We developed a method for automatic classification of the MER signals, which provides an electrode model with patient specific borders of the STN. In addition, a method is provided for finding the best matching of the electrode model with a 3D model of the STN. As a result, a 2.5D visualization of the target region is produced with the most probable positions of the electrodes and their intersections.

Fuzzy logic-based rainfall-runoff modelling using soil moisture measurements to represent system state

Abstract Initial catchment state, such as soil moisture, strongly controls rainfall—runoff transformation processes. However, due to the high spatial and temporal variability of soil moisture, point measurements may not always be suitable to represent the actual system state of a whole catchment as required in distributed catchment modelling. In this study a fuzzy rule-based system (FRBS) using the Takagi-Sugeno-Kang approach has been developed using soil moisture and rainfall as input variables to predict the actual discharge at the catchment outlet. Four soil moisture probes from the hydrological test site Dürreych (Black Forest, southwest Germany) were selected, each of them representing a particular runoff generation process (saturation excess flow, infiltration excess flow, slow and fast interflow, return flow). After manual calibration, the simulated peak discharges were very similar to the measured values. Furthermore, the pattern of rule activation in the FRBS reflected the complex, highly nonlinear behaviour of the catchment. Thus, in the FRBS framework, the measurements of soil moisture at representative locations could be used as representation for the actual system state, allowing for an entirely data-driven prediction of the runoff response using rainfall.

A solution for multi-alignment by transformation synchronisation

The alignment of a set of objects by means of transformations plays an important role in computer vision. Whilst the case for only two objects can be solved globally, when multiple objects are considered usually iterative methods are used. In practice the iterative methods perform well if the relative transformations between any pair of objects are free of noise. However, if only noisy relative transformations are available (e.g. due to missing data or wrong correspondences) the iterative methods may fail. Based on the observation that the underlying noise-free transformations can be retrieved from the null space of a matrix that can directly be obtained from pairwise alignments, this paper presents a novel method for the synchronisation of pairwise transformations such that they are transitively consistent. Simulations demonstrate that for noisy transformations, a large proportion of missing data and even for wrong correspondence assignments the method delivers encouraging results.

Eyelid contour detection and tracking for startle research related eye-blink measurements from high-speed video records

Using the positions of the eyelids is an effective and contact-free way for the measurement of startle induced eye-blinks, which plays an important role in human psychophysiological research. To the best of our knowledge, no methods for an efficient detection and tracking of the exact eyelid contours in image sequences captured at high-speed exist that are conveniently usable by psychophysiological researchers. In this publication a semi-automatic model-based eyelid contour detection and tracking algorithm for the analysis of high-speed video recordings from an eye tracker is presented. As a large number of images have been acquired prior to method development it was important that our technique is able to deal with images that are recorded without any special parametrisation of the eye tracker. The method entails pupil detection, specular reflection removal and makes use of dynamic model adaption. In a proof-of-concept study we could achieve a correct detection rate of 90.6%. With this approach, we provide a feasible method to accurately assess eye-blinks from high-speed video recordings.

Fast correspondences for statistical shape models of brain structures

Statistical shape models based on point distribution models are powerful tools for image segmentation or shape analysis. The most challenging part in the generation of point distribution models is the identification of corresponding landmarks among all training shapes. Since in general the true correspondences are unknown, correspondences are frequently established under the hypothesis that correct correspondences lead to a compact model, which is mostly tackled by continuous optimisation methods. In favour of the prospect of an efficient optimisation, we present a simplified view of the correspondence problem for statistical shape models that is based on point-set registration, the linear assignment problem and mesh fairing. At first, regularised deformable point-set registration is performed and combined with solving the linear assignment problem to obtain correspondences between shapes on a global scale. With that, rough correspondences are established that may not yet be accurate on a local scale. Then, by using a mesh fairing procedure, consensus of the correspondences on a global and local scale among the entire set of shapes is achieved. We demonstrate that for the generation of statistical shape models of deep brain structures, the proposed approach is preferable over existing population-based methods both in terms of a significantly shorter runtime and in terms of an improved quality of the resulting shape model.

PaCER - A fully automated method for electrode trajectory and contact reconstruction in deep brain stimulation

Deep brain stimulation (DBS) is a neurosurgical intervention where electrodes are permanently implanted into the brain in order to modulate pathologic neural activity. The post-operative reconstruction of the DBS electrodes is important for an efficient stimulation parameter tuning. A major limitation of existing approaches for electrode reconstruction from post-operative imaging that prevents the clinical routine use is that they are manual or semi-automatic, and thus both time-consuming and subjective. Moreover, the existing methods rely on a simplified model of a straight line electrode trajectory, rather than the more realistic curved trajectory. The main contribution of this paper is that for the first time we present a highly accurate and fully automated method for electrode reconstruction that considers curved trajectories. The robustness of our proposed method is demonstrated using a multi-center clinical dataset consisting of N = 44 electrodes. In all cases the electrode trajectories were successfully identified and reconstructed. In addition, the accuracy is demonstrated quantitatively using a high-accuracy phantom with known ground truth. In the phantom experiment, the method could detect individual electrode contacts with high accuracy and the trajectory reconstruction reached an error level below 100 μm (0.046 ± 0.025 mm). An implementation of the method is made publicly available such that it can directly be used by researchers or clinicians. This constitutes an important step towards future integration of lead reconstruction into standard clinical care.

Shape-aware surface reconstruction from sparse 3D point-clouds

HIGHLIGHTSA surface reconstruction method from sparse data is proposed.A statistical shape model is used as prior to compensate missing data.The problem is formulated probabilistically using Gaussian Mixture Models (GMM).Anisotropic covariances “oriented” by surface normals achieve a surface‐based fitting.A fast approximation having the same complexity as isotropic GMMs is proposed. ABSTRACT The reconstruction of an objects shape or surface from a set of 3D points plays an important role in medical image analysis, e.g. in anatomy reconstruction from tomographic measurements or in the process of aligning intra‐operative navigation and preoperative planning data. In such scenarios, one usually has to deal with sparse data, which significantly aggravates the problem of reconstruction. However, medical applications often provide contextual information about the 3D point data that allow to incorporate prior knowledge about the shape that is to be reconstructed. To this end, we propose the use of a statistical shape model (SSM) as a prior for surface reconstruction. The SSM is represented by a point distribution model (PDM), which is associated with a surface mesh. Using the shape distribution that is modelled by the PDM, we formulate the problem of surface reconstruction from a probabilistic perspective based on a Gaussian Mixture Model (GMM). In order to do so, the given points are interpreted as samples of the GMM. By using mixture components with anisotropic covariances that are “oriented” according to the surface normals at the PDM points, a surface‐based fitting is accomplished. Estimating the parameters of the GMM in a maximum a posteriori manner yields the reconstruction of the surface from the given data points. We compare our method to the extensively used Iterative Closest Points method on several different anatomical datasets/SSMs (brain, femur, tibia, hip, liver) and demonstrate superior accuracy and robustness on sparse data.

An Approach for Data Analysis and Forecasting with Neuro Fuzzy Systems - Demonstrated on Flood Events at River Mosel

The development and usage of soft computing systems for forecasting of water level progress in case of flood events at river Mosel are presented. The practical situation and its requirements are explained and two different system approaches are discussed: a) a neural network for supervised learning of the functional behavior of time series data and its approximation, and b) a fuzzy system for modeling of the system behavior with possibilities to exploit expert information and for systematic optimization. Advantages and disadvantages of both concepts are described and emphasis is laid on the structural development of the fuzzy system. Both systems have been tested and satisfying results are shown with practical data.