Tripolar nerve cuff electrodes have been widely used for measuring peripheral nerve activity. However, despite the high signal-to-noise ratio levels that can be achieved with this recording configuration, the clinical use of cuff electrodes in closed-loop controlled neuroprostheses remains limited. This is largely attributed to artifact noise signals that contaminate the recorded neural activity. In this study, we investigated the use of a conductive shield layer (CSL) as a means of reducing the artifact noise recorded by nerve cuff electrodes. Using both computational simulations and in vivo experiments, we found that the CSL can result in up to an 85% decrease in the recorded artifact signal. Both the electrical conductivity and the surface area of the CSL were identified as important design criteria. Although this study shows that the CSL can significantly reduce artifact noise in tripolar nerve cuff electrodes, long-term implant studies are needed to validate our findings.
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http://dx.doi.org/10.1016/j.medengphy.2016.11.010 | DOI Listing |
Comput Methods Programs Biomed
January 2025
Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing, China; School of Computer Science and Engineering, Southeast University, Nanjing, China.
Purpose: Dual-energy computed tomography (DECT) enables the differentiation of different materials. Additionally, DECT images consist of multiple scans of the same sample, revealing information similarity within the energy domain. To leverage this information similarity and address safety concerns related to excessive radiation exposure in DECT imaging, sparse view DECT imaging is proposed as a solution.
View Article and Find Full Text PDFAJNR Am J Neuroradiol
January 2025
From the Orthopedic Data Innovation Lab (ODIL), Hospital for Special Surgery (A.M.L.S., M.A.F.), Department of Radiology and Imaging, Hospital for Special Surgery Centre (E.E.X, Z.I, E.T.T, D.B.S, J.L.C)and Department of Population Health Sciences, Weill Cornell Medicine (M.A.F), New York, New York, USA.
Background And Purpose: To train and evaluate an open-source generative adversarial networks (GANs) to create synthetic lumbar spine MRI STIR volumes from T1 and T2 sequences, providing a proof-of-concept that could allow for faster MRI examinations.
Materials And Methods: 1817 MRI examinations with sagittal T1, T2, and STIR sequences were accumulated and randomly divided into training, validation, and test sets. GANs were trained to create synthetic STIR volumes using the T1 and T2 volumes as inputs, optimized using the validation set, then applied to the test set.
Neuroimage
January 2025
Department of Neuroscience, The Jikei University School of Medicine, Tokyo, Japan; Human Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan; Faculty of Engineering, University of Tsukuba, Tsukuba, Japan. Electronic address:
Functional MRI (fMRI) is an important tool for investigating functional networks. However, the widely used fMRI with T2*-weighted imaging in rodents has the problem of signal lack in the lateral ventral area of forebrain including the amygdala, which is essential for not only emotion but also noxious pain. Here, we scouted the zero-echo time (ZTE) sequence, which is robust to magnetic susceptibility and motion-derived artifacts, to image activation in the whole brain including the amygdala following the noxious stimulation to the hind paw.
View Article and Find Full Text PDFInvest Radiol
January 2025
From the Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany (N.M., A.I., A.L., L.B., T.D., D. Kravchenko, D. Kuetting, C.C.P., J.A.L.); Quantitative Imaging Lab Bonn (QILaB), Bonn, Germany (N.M., A.I., L.B., D. Kravchenko, D. Kuetting, J.A.L.); Philips Healthcare, Hamburg, Germany (C.K.); Philips Medical Systems, Eindhoven, the Netherlands (A.H.-M.); and Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany (C.Y.).
Objectives: Impaired image quality and long scan times frequently occur in respiratory-triggered sequences in liver magnetic resonance imaging (MRI). We evaluated the impact of an in-bore active breathing guidance (BG) application on image quality and scan time of respiratory-triggered T2-weighted (T2) and diffusion-weighted imaging (DWI) by comparing sequences with standard triggering (T2S and DWIS) and with BG (T2BG and DWIBG).
Materials And Methods: In this prospective study, random patients with clinical indications for liver MRI underwent 3 T MRI with standard and BG acquisitions.
Sensors (Basel)
January 2025
Guangdong Institute of Intelligence Science and Technology, Hengqin, Zhuhai 519031, China.
Electroencephalogram (EEG) signals are important bioelectrical signals widely used in brain activity studies, cognitive mechanism research, and the diagnosis and treatment of neurological disorders. However, EEG signals are often influenced by various physiological artifacts, which can significantly affect data analysis and diagnosis. Recently, deep learning-based EEG denoising methods have exhibited unique advantages over traditional methods.
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