Development and Evaluation of a New Tablet Computer Application for the Therapy of Brain-Injured Patients with Aphasia

Introduction Language is the most important mean of communication and plays a central role in our everyday life. Brain damage (e.g. stroke) can lead to acquired disorders of lan- guage affecting the four linguistic modalities (i.e. reading, writing, speech production and comprehension) in different combinations and levels of severity. Every year, more than 5000 people (Aphasie Suisse) are affected by aphasia in Switzerland alone. Since aphasia is highly individual, the level of difficulty and the content of tasks have to be adapted continuously by the speech therapists. Computer-based assignments allow patients to train independently at home and thus increasing the frequency of ther- apy. Recent developments in tablet computers have opened new opportunities to use these devices for rehabilitation purposes. Especially older people, who have no prior experience with computers, can benefit from the new technologies. Methods The aim of this project was to develop an application that enables patients to train language related tasks autonomously and, on the other hand, allows speech therapists to assign exercises to the patients and to track their results online. Seven categories with various types of assignments were implemented. The application has two parts which are separated by a user management system into a patient interface and a therapist interface. Both interfaces were evaluated using the SUS (Subject Usability Scale). The patient interface was tested by 15 healthy controls and 5 patients. For the patients, we also collected tracking data for further analysis. The therapist interface was evaluated by 5 speech therapists. Results The SUS score are xpatients = 98 and xhealthy = 92.7 (median = 95, SD = 7, 95% CI [88.8, 96.6]) in case of the patient interface and xtherapists = 68 in case of the therapist interface. Conclusion Both, the patients and the healthy subjects, attested high SUS scores to the patient interface. These scores are considered as "best imaginable". The therapist interface got a lower SUS score compared to the patient interface, but is still considered as "good" and "usable". The user tracking system and the interviews revealed that there is room for improvements and inspired new ideas for future versions.

Magnetic resonance imaging and computer tomography of brain lesions in water buffaloes and cattle stunned with handguns or captive bolts

Owing to the demand for genuine mozzarella, some 330 water buffaloes are being slaughtered every year in Switzerland albeit a stunning procedure meeting animal welfare and occupational safety requirements remains to be established. To provide a basis for improvements, we sized anatomical specifics in water buffaloes and cattle and we assessed brain lesions after stunning with captive bolts or handguns by diagnostic imaging. In water buffaloes and cattle, the median distance from the frontal skin surface to the inner bone table was 74.0 mm (56.0–100.0 mm) vs 36.6 mm (29.3–44.3 mm) and from skin to the thalamus 144.8 mm (117.1–172.0 mm) vs 102.0 (101.0–121.0 mm), respectively. Consequently, customary captive bolt stunners may be inadequate. Free bullets are potentially suitable for stunning buffaloes but involve occupational safety hazards. The results of the present study shall be used to develop a device allowing effective and safe stunning of water buffaloes.

Fully Automatic Segmentation of Brain Tumor Images using Support Vector Machine Classiffication in Combination with Hierarchical Conditional Random Field Regularization

Delineating brain tumor boundaries from magnetic resonance images is an essential task for the analysis of brain cancer. We propose a fully automatic method for brain tissue segmentation, which combines Support Vector Machine classification using multispectral intensities and textures with subsequent hierarchical regularization based on Conditional Random Fields. The CRF regularization introduces spatial constraints to the powerful SVM classification, which assumes voxels to be independent from their neighbors. The approach first separates healthy and tumor tissue before both regions are subclassified into cerebrospinal fluid, white matter, gray matter and necrotic, active, edema region respectively in a novel hierarchical way. The hierarchical approach adds robustness and speed by allowing to apply different levels of regularization at different stages. The method is fast and tailored to standard clinical acquisition protocols. It was assessed on 10 multispectral patient datasets with results outperforming previous methods in terms of segmentation detail and computation times.

Correlation properties of spontaneous motor activity in healthy infants: a new computer-assisted method to evaluate neurological maturation

Qualitative assessment of spontaneous motor activity in early infancy is widely used in clinical practice. It enables the description of maturational changes of motor behavior in both healthy infants and infants who are at risk for later neurological impairment. These assessments are, however, time-consuming and are dependent upon professional experience. Therefore, a simple physiological method that describes the complex behavior of spontaneous movements (SMs) in infants would be helpful. In this methodological study, we aimed to determine whether time series of motor acceleration measurements at 40-44 weeks and 50-55 weeks gestational age in healthy infants exhibit fractal-like properties and if this self-affinity of the acceleration signal is sensitive to maturation. Healthy motor state was ensured by General Movement assessment. We assessed statistical persistence in the acceleration time series by calculating the scaling exponent α via detrended fluctuation analysis of the time series. In hand trajectories of SMs in infants we found a mean α value of 1.198 (95 % CI 1.167-1.230) at 40-44 weeks. Alpha changed significantly (p = 0.001) at 50-55 weeks to a mean of 1.102 (1.055-1.149). Complementary multilevel regression analysis confirmed a decreasing trend of α with increasing age. Statistical persistence of fluctuation in hand trajectories of SMs is sensitive to neurological maturation and can be characterized by a simple parameter α in an automated and observer-independent fashion. Future studies including children at risk for neurological impairment should evaluate whether this method could be used as an early clinical screening tool for later neurological compromise.

High-resolution computer simulation of the dynamics of isoelectric focusing using carrier ampholytes: focusing with concurrent electrophoretic mobilization is an isotachophoretic process

Focusing of four hemoglobins with concurrent electrophoretic mobilization was studied by computer simulation. A dynamic electrophoresis simulator was first used to provide a detailed description of focusing in a 100-carrier component, pH 6-8 gradient using phosphoric acid as anolyte and NaOH as catholyte. These results are compared to an identical simulation except that the catholyte contained both NaOH and NaCl. A stationary, steady-state distribution of carrier components and hemoglobins is produced in the first configuration. In the second, the chloride ion migrates into and through the separation space. It is shown that even under these conditions of chloride ion flux a pH gradient forms. All amphoteric species acquire a slight positive charge upon focusing and the whole pattern is mobilized towards the cathode. The cathodic gradient end is stable whereas the anodic end is gradually degrading due to the continuous accumulation of chloride. The data illustrate that the mobilization is a cationic isotachophoretic process with the sodium ion being the leading cation. The peak height of the hemoglobin zones decreases somewhat upon mobilization, but the zones retain a relatively sharp profile, thus facilitating detection. The electropherograms that would be produced by whole column imaging and by a single detector placed at different locations along the focusing column are presented and show that focusing can be commenced with NaCl present in the catholyte at the beginning of the experiment. However, this may require detector placement on the cathodic side of the catholyte/sample mixture interface.

Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity (EEG)

The early detection of subjects with probable Alzheimer's disease (AD) is crucial for effective appliance of treatment strategies. Here we explored the ability of a multitude of linear and non-linear classification algorithms to discriminate between the electroencephalograms (EEGs) of patients with varying degree of AD and their age-matched control subjects. Absolute and relative spectral power, distribution of spectral power, and measures of spatial synchronization were calculated from recordings of resting eyes-closed continuous EEGs of 45 healthy controls, 116 patients with mild AD and 81 patients with moderate AD, recruited in two different centers (Stockholm, New York). The applied classification algorithms were: principal component linear discriminant analysis (PC LDA), partial least squares LDA (PLS LDA), principal component logistic regression (PC LR), partial least squares logistic regression (PLS LR), bagging, random forest, support vector machines (SVM) and feed-forward neural network. Based on 10-fold cross-validation runs it could be demonstrated that even tough modern computer-intensive classification algorithms such as random forests, SVM and neural networks show a slight superiority, more classical classification algorithms performed nearly equally well. Using random forests classification a considerable sensitivity of up to 85% and a specificity of 78%, respectively for the test of even only mild AD patients has been reached, whereas for the comparison of moderate AD vs. controls, using SVM and neural networks, values of 89% and 88% for sensitivity and specificity were achieved. Such a remarkable performance proves the value of these classification algorithms for clinical diagnostics.

Computer algebra for x-ray spectral reconstruction between 6 and 25 MV

A previously presented algorithm for the reconstruction of bremsstrahlung spectra from transmission data has been implemented into MATHEMATICA. Spectra vectorial algebra has been used to solve the matrix system A * F = T. The new implementation has been tested by reconstructing photon spectra from transmission data acquired in narrow beam conditions, for nominal energies of 6, 15, and 25 MV. The results were in excellent agreement with the original calculations. Our implementation has the advantage to be based on a well-tested mathematical kernel. Furthermore it offers a comfortable user interface.

Development of a computer-assisted high-pressure Injection device for vertebroplasty

A novel computer-assisted injection device for the delivery of highly viscous bone cements in vertebroplasty is presented. It addresses the shortcomings of manual injection systems ranging from low-pressure and poor level of control to device failure. The presented instrument is capable of generating a maximum pressure of 5000 kPa in traditional 6-ml syringes and provides an advanced control interface for precise cement delivery from outside radiation fields emitted by intraoperative imaging systems. The integrated real-time monitoring of injection parameters, such as flow-rate, volume, pressure, and viscosity, simplifies consistent documentation of interventions and establishes a basis for the identification of safe injection protocols on the longer term. Control algorithms prevent device failure due to overloading and provide means to immediately stop cement flow to avoid leakage into adjacent tissues.

Microstates in language-related brain potential maps show noun-verb differences

Brain processing of grammatical word class was studied analyzing event-related potential (ERP) brain fields. Normal subjects observed a randomized sequence of single German nouns and verbs on a computer screen, while 20-channel ERP field map series were recorded separately for both word classes. Spatial microstate analysis was applied, based on the observation that series of ERP maps consist of epochs of quasi-stable map landscapes and based on the rationale that different map landscapes must have been generated by different neural generators and thus suggest different brain functions. Space-oriented segmentation of the mean map series identified nine successive, different functional microstates, i.e., steps of brain information processing characterized by quasi-stable map landscapes. In the microstate from 116 to 172 msec, noun-related maps differed significantly from verb-related maps along the left–right axis. The results indicate that different neural populations represent different grammatical word classes in language processing, in agreement with clinical observations. This word class differentiation as revealed by the spatial–temporal organization of neural activity occurred at a time after word input compatible with speed of reading.

Hierarchical segmentation-assisted multimodal registration for MR brain images

Information theory-based metric such as mutual information (MI) is widely used as similarity measurement for multimodal registration. Nevertheless, this metric may lead to matching ambiguity for non-rigid registration. Moreover, maximization of MI alone does not necessarily produce an optimal solution. In this paper, we propose a segmentation-assisted similarity metric based on point-wise mutual information (PMI). This similarity metric, termed SPMI, enhances the registration accuracy by considering tissue classification probabilities as prior information, which is generated from an expectation maximization (EM) algorithm. Diffeomorphic demons is then adopted as the registration model and is optimized in a hierarchical framework (H-SPMI) based on different levels of anatomical structure as prior knowledge. The proposed method is evaluated using Brainweb synthetic data and clinical fMRI images. Both qualitative and quantitative assessment were performed as well as a sensitivity analysis to the segmentation error. Compared to the pure intensity-based approaches which only maximize mutual information, we show that the proposed algorithm provides significantly better accuracy on both synthetic and clinical data.

Integrated Segmentation of Brain Tumor Images for Radiotherapy and Neurosurgery

Image-based modeling of tumor growth combines methods from cancer simulation and medical imaging. In this context, we present a novel approach to adapt a healthy brain atlas to MR images of tumor patients. In order to establish correspondence between a healthy atlas and a pathologic patient image, tumor growth modeling in combination with registration algorithms is employed. In a first step, the tumor is grown in the atlas based on a new multi-scale, multi-physics model including growth simulation from the cellular level up to the biomechanical level, accounting for cell proliferation and tissue deformations. Large-scale deformations are handled with an Eulerian approach for finite element computations, which can operate directly on the image voxel mesh. Subsequently, dense correspondence between the modified atlas and patient image is established using nonrigid registration. The method offers opportunities in atlasbased segmentation of tumor-bearing brain images as well as for improved patient-specific simulation and prognosis of tumor progression.

Patient-Specific Semi-Supervised Learning for Postoperative Brain Tumor Segmentation

In contrast to preoperative brain tumor segmentation, the problem of postoperative brain tumor segmentation has been rarely approached so far. We present a fully-automatic segmentation method using multimodal magnetic resonance image data and patient-specific semi-supervised learning. The idea behind our semi-supervised approach is to effectively fuse information from both pre- and postoperative image data of the same patient to improve segmentation of the postoperative image. We pose image segmentation as a classification problem and solve it by adopting a semi-supervised decision forest. The method is evaluated on a cohort of 10 high-grade glioma patients, with segmentation performance and computation time comparable or superior to a state-of-the-art brain tumor segmentation method. Moreover, our results confirm that the inclusion of preoperative MR images lead to a better performance regarding postoperative brain tumor segmentation.

Interactive Segmentation of MR Images from Brain Tumor Patients

Medical doctors often do not trust the result of fully automatic segmentations because they have no possibility to make corrections if necessary. On the other hand, manual corrections can introduce a user bias. In this work, we propose to integrate the possibility for quick manual corrections into a fully automatic segmentation method for brain tumor images. This allows for necessary corrections while maintaining a high objectiveness. The underlying idea is similar to the well-known Grab-Cut algorithm, but here we combine decision forest classification with conditional random field regularization for interactive segmentation of 3D medical images. The approach has been evaluated by two different users on the BraTS2012 dataset. Accuracy and robustness improved compared to a fully automatic method and our interactive approach was ranked among the top performing methods. Time for computation including manual interaction was less than 10 minutes per patient, which makes it attractive for clinical use.