Aim: To explore the clinical value of combining pyramidal tract mapping, microscopic-based neuronavigation, and intraoperative magnetic resonance imaging (iMRI) in the surgical treatment of epileptic foci involving sensorimotor cortex.
Material And Methods: We retrospectively analyzed 69 patients with focal epilepsy involving motor and sensory cortex. The surgical operations in Group I (n=38) were performed under the guidance of conventional neuronavigation, and the operations of Group II (n=31) were aided by combining pyramidal tract mapping, microscopic-based neuronavigation and the iMRI technique. Chi square test was used to compare seizure outcome and neurological deficits across groups.
Results: 7 patients (18.4%) in Group I, and 3 patients (9.7%) in Group II didn't recover to the level of preoperative strength within one year post-operation. The 2-year follow-up survey showed that more patients in Group II compared to Group I (71% vs. 55.3%, p=0.181) had a good outcome (Engel class I ~ II).
Conclusion: The techniques of combining pyramidal tract mapping, microscopic-based neuronavigation and iMRI aid in precise mapping and hence resection of epileptic foci in sensorimotor cortex, which lead to improvement of surgical efficacy and significant reduction of postoperative loss of function.
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http://dx.doi.org/10.5137/1019-5149.JTN.9517-13.0 | DOI Listing |
J Biophotonics
January 2025
Univ. Grenoble Alpes, CNRS, LIPhy, Grenoble, France.
A challenge in neuroimaging is acquiring frame sequences at high temporal resolution from the largest possible number of pixels. Measuring 1%-10% fluorescence changes normally requires 12-bit or higher bit depth, constraining the frame size allowing imaging in the kHz range. We resolved Ca or membrane potential signals from cell populations or single neurons in brain slices by acquiring fluorescence at 8-bit depth and by binning pixels offline, achieving unprecedented frame sizes at kHz rates.
View Article and Find Full Text PDFTrials
January 2025
Department of Physical Education, Sports Center, Federal University of Santa Catarina, University Campus Trindade, Florianópolis, Santa Catarina, 88040-900, Brazil.
Background: Physical exercise is crucial in type 2 diabetes management (T2D), and training in the aquatic environment seems to be a promising alternative due to its physical properties and metabolic, functional, cardiovascular, and neuromuscular benefits. Research on combined training in aquatic and dry-land training environments is scarce, especially in long-term interventions. Thus, this study aims to investigate the effects of combined training in both environments on health outcomes related to the management of T2D patients.
View Article and Find Full Text PDFSensors (Basel)
December 2024
College of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China.
In order to achieve infrared aircraft detection under interference conditions, this paper proposes an infrared aircraft detection algorithm based on high-resolution feature-enhanced semantic segmentation network. Firstly, the designed location attention mechanism is utilized to enhance the current-level feature map by obtaining correlation weights between pixels at different positions. Then, it is fused with the high-level feature map rich in semantic features to construct a location attention feature fusion network, thereby enhancing the representation capability of target features.
View Article and Find Full Text PDFBioengineering (Basel)
December 2024
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
In recent years, image-guided brachytherapy for cervical cancer has become an important treatment method for patients with locally advanced cervical cancer, and multi-modality image registration technology is a key step in this system. However, due to the patient's own movement and other factors, the deformation between the different modalities of images is discontinuous, which brings great difficulties to the registration of pelvic computed tomography (CT/) and magnetic resonance (MR) images. In this paper, we propose a multimodality image registration network based on multistage transformation enhancement features (MTEF) to maintain the continuity of the deformation field.
View Article and Find Full Text PDFDiagnostics (Basel)
December 2024
Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia.
Background: Cardiac magnetic resonance imaging (MRI) plays a crucial role in monitoring disease progression and evaluating the effectiveness of treatment interventions. Cardiac MRI allows medical practitioners to assess cardiac function accurately by providing comprehensive and quantitative information about the structure and function, hence making it an indispensable tool for monitoring the disease and treatment response. Deep learning-based segmentation enables the precise delineation of cardiac structures including the myocardium, right ventricle, and left ventricle.
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