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Optogenetics has transformed the study of neural circuit function, but limitations in its application to species with large brains, such as non-human primates (NHPs), remain. A major challenge in NHP optogenetics is delivering light to sufficiently large volumes of deep neural tissue with high spatiotemporal precision, without simultaneously affecting superficial tissue. To overcome these limitations, we recently developed and tested in NHP cortex, the Utah Optrode Array (UOA).
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Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, USA.
Real-time dynamic MRI is important for visualizing time-varying processes in several applications, including cardiac imaging, where it enables free-breathing images of the beating heart without ECG gating. However, current real-time MRI techniques commonly face challenges in achieving the required spatio-temporal resolutions due to limited acceleration rates. In this study, we propose a deep learning (DL) technique for improving the estimation of stationary outer-volume signal from shifted time-interleaved undersampling patterns.
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Resting-state functional magnetic resonance imaging (rs-fMRI) is a non-invasive neuroimaging technique widely utilized in the research of Autism Spectrum Disorder (ASD), providing preliminary insights into the potential biological mechanisms underlying ASD. Deep learning techniques have demonstrated significant potential in the analysis of rs-fMRI. However, accurately distinguishing between healthy control group and ASD has been a longstanding challenge.
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View Article and Find Full Text PDFA Parkinson's disease-related nuclei segmentation network based on CNN-Transformer interleaved encoder with feature fusion.
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Academy for Engineering and Technology, Fudan University, Shanghai, 200433, China; Department of Radiology, Huashan Hospital, Fudan University, Shanghai, 200040, China; Shanghai Engineering Research Center of Intelligent Imaging for Critical Brain Diseases, Shanghai, 200040, China; Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, 200040, China. Electronic address:
Article Synopsis
- The study focuses on improving the segmentation of deep gray matter nuclei in brain MRIs for better diagnosis of Parkinson's disease (PD) due to challenges like low tissue contrast and small nucleus size.
- A new network uses a combination of convolutional neural networks and Transformers to enhance feature extraction and improve segmentation accuracy, particularly for small structures.
- Experimental results show the method's effectiveness across various datasets, indicating its potential to aid clinicians in diagnosing PD more quickly and accurately.
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