A shortened radiofrequency denervation method for cervical zygapophysial joint pain based on ultrasound localization of the nerves

Radiofrequency neurotomy is a recognized treatment for cervical zygapophysial joint pain. In several studies, the method has provided complete pain relief in 60-70% of the patients for approximately 9 months. The validated technique has the disadvantage of procedural times of 2-4 hours because several lesions are performed to take into account the variable nerve course. We tested the hypothesis that ultrasound localization of the nerves would enable us to reduce the number of lesions performed, while reaching the benchmark of at least 80% pain relief in 80% of patients with a median duration of 35 weeks, as achieved by a previous investigation using the standard method.

Typical 3-D localization of tumor remnants of WHO grade II hemispheric gliomas--lessons learned from the use of intraoperative high-field MRI control

Complete resection of grade II gliomas might prolong survival but is not always possible. The goal of the study was to evaluate the location of unexpected grade II gliomas remnants after assumed complete removal with intraoperative (iop) MRI and to assess the reason for their non-detection.

Intraoperative localization of tantalum markers for proton beam radiation of choroidal melanoma by an opto-electronic navigation system: a novel technique

External beam proton radiation therapy has been used since 1975 to treat choroidal melanoma. For tumor location determination during proton radiation treatment, surgical tantalum clips are registered with image data. This report introduces the intraoperative application of an opto-electronic navigation system to determine with high precision the position of the tantalum markers and their spatial relationship to the tumor and anatomical landmarks. The application of the technique in the first 4 patients is described.

Investigating human audio-visual object perception with a combination of hypothesis-generating and hypothesis-testing fMRI analysis tools

Primate multisensory object perception involves distributed brain regions. To investigate the network character of these regions of the human brain, we applied data-driven group spatial independent component analysis (ICA) to a functional magnetic resonance imaging (fMRI) data set acquired during a passive audio-visual (AV) experiment with common object stimuli. We labeled three group-level independent component (IC) maps as auditory (A), visual (V), and AV, based on their spatial layouts and activation time courses. The overlap between these IC maps served as definition of a distributed network of multisensory candidate regions including superior temporal, ventral occipito-temporal, posterior parietal and prefrontal regions. During an independent second fMRI experiment, we explicitly tested their involvement in AV integration. Activations in nine out of these twelve regions met the max-criterion (A < AV > V) for multisensory integration. Comparison of this approach with a general linear model-based region-of-interest definition revealed its complementary value for multisensory neuroimaging. In conclusion, we estimated functional networks of uni- and multisensory functional connectivity from one dataset and validated their functional roles in an independent dataset. These findings demonstrate the particular value of ICA for multisensory neuroimaging research and using independent datasets to test hypotheses generated from a data-driven analysis.

Evaluating the Impact of the Number of Access Points in Mobile Robots Localization Using Artificial Neural Networks

Localization is information of fundamental importance to carry out various tasks in the mobile robotic area. The exact degree of precision required in the localization depends on the nature of the task. The GPS provides global position estimation but is restricted to outdoor environments and has an inherent imprecision of a few meters. In indoor spaces, other sensors like lasers and cameras are commonly used for position estimation, but these require landmarks (or maps) in the environment and a fair amount of computation to process complex algorithms. These sensors also have a limited field of vision. Currently, Wireless Networks (WN) are widely available in indoor environments and can allow efficient global localization that requires relatively low computing resources. However, the inherent instability in the wireless signal prevents it from being used for very accurate position estimation. The growth in the number of Access Points (AP) increases the overlap signals areas and this could be a useful means of improving the precision of the localization. In this paper we evaluate the impact of the number of Access Points in mobile nodes localization using Artificial Neural Networks (ANN). We use three to eight APs as a source signal and show how the ANNs learn and generalize the data. Added to this, we evaluate the robustness of the ANNs and evaluate a heuristic to try to decrease the error in the localization. In order to validate our approach several ANNs topologies have been evaluated in experimental tests that were conducted with a mobile node in an indoor space.

Evaluation of respiratory parameters at presentation as clinical indicators of the respiratory localization in dogs and cats with respiratory distress

OBJECTIVE: To describe clinical respiratory parameters in cats and dogs with respiratory distress and identify associations between respiratory signs at presentation and localization of the disease with particular evaluation between the synchrony of abdominal and chest wall movements as a clinical indicators for pleural space disease. Design - Prospective observational clinical study. SETTING: Emergency service in a university veterinary teaching hospital. ANIMALS: Cats and dogs with respiratory distress presented to the emergency service between April 2008 and July 2009. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The following parameters were systematically determined at time of admission: respiratory rate, heart rate, temperature, type of breathing, movement of the thoracic and abdominal wall during inspiration, presence of stridor, presence and type of dyspnea, and results of thoracic auscultation. Abdominal and chest wall movement was categorized as synchronous, asynchronous, or inverse. Diagnostic test results, diagnosis, and outcome were subsequently recorded. Based on the final diagnoses, animals were assigned to 1 or more of the following groups regarding the anatomical localization of the respiratory distress: upper airways, lower airways, lung parenchyma, pleural space, thoracic wall, nonrespiratory causes, and normal animals. One hundred and seventy-six animals (103 cats and 73 dogs) were evaluated. Inspiratory dyspnea was associated with upper airway disease in dogs and expiratory dyspnea with lower airway disease in cats. Respiratory noises were significantly associated and highly sensitive and specific for upper airway disease. An asynchronous or inverse breathing pattern and decreased lung auscultation results were significantly associated with pleural space disease in both dogs and cats (P<0.001). The combination is highly sensitive (99%) but not very specific (45%). Fast and shallow breathing was not associated with pleural space disease. Increased or moist pulmonary auscultation findings were associated with parenchymal lung disease. CONCLUSIONS: Cats and dogs with pleural space disease can be identified by an asynchronous or inverse breathing pattern in combination with decreased lung sounds on auscultation.