We introduce a spatial filtering method in the spherical harmonics domain for constraining magnetoencephalographic (MEG) multichannel measurements to any user-specified spherical region of interest (ROI) inside the head. The method relies on a linear transformation of the signal space separation inner coefficients that represent the MEG signal generated by sources located inside the head. The spatial filtering is achieved effectively by constructing a spherical harmonics basis vector that is dependent on the center of the targeted ROI and it does not require any discrete division of the headspace into grids like the traditional MEG spatial filtering approaches. The validation and the performance of the method are demonstrated through both simulated and actual bilateral auditory-evoked data experiments.
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http://dx.doi.org/10.1109/TBME.2009.2024760 | DOI Listing |
J Imaging Inform Med
January 2025
Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong Special Administrative Region, China.
Deep learning models have shown promise in diagnosing neurodevelopmental disorders (NDD) like ASD and ADHD. However, many models either use graph neural networks (GNN) to construct single-level brain functional networks (BFNs) or employ spatial convolution filtering for local information extraction from rs-fMRI data, often neglecting high-order features crucial for NDD classification. We introduce a Multi-view High-order Network (MHNet) to capture hierarchical and high-order features from multi-view BFNs derived from rs-fMRI data for NDD prediction.
View Article and Find Full Text PDFMed Phys
January 2025
Institut Curie, Université PSL, CNRS UMR3347, Inserm U1021, Signalisation Radiobiologie et Cancer, Orsay, France.
Background: Breast cancer is the leading cause of female cancer mortality worldwide, accounting for 1 in 6 cancer deaths. Surgery, radiation, and systemic therapy are the three pillars of breast cancer treatment, with several strategies developed to combine them. The association of preoperative radiotherapy with immunotherapy may improve breast cancer tumor control by exploiting the tumor radio-induced immune priming.
View Article and Find Full Text PDFLaryngoscope
January 2025
Department of Otolaryngology/Head & Neck Surgery, University of North Carolina School of Medicine, Chapel Hill, North Carolina, U.S.A.
Objectives: Bimodal cochlear implant (CI) users vary in speech recognition outcomes. This variability may be influenced partly by the CI and contralateral hearing aid (HA) programming procedures, which can result in mismatches in latency and frequency. We assessed the performance of bimodal listeners when latency mismatches were corrected and analyzed how frequency mismatches influenced outcomes.
View Article and Find Full Text PDFBMC Bioinformatics
January 2025
Centre for Big Data Research in Health, University of New South Wales, Sydney, Australia.
Background: Due to the complexity and cost of preparing histopathological slides, deep learning-based methods have been developed to generate high-quality histological images. However, existing approaches primarily focus on spatial domain information, neglecting the periodic information in the frequency domain and the complementary relationship between the two domains. In this paper, we proposed a generative adversarial network that employs a cross-attention mechanism to extract and fuse features across spatial and frequency domains.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
Vision transformer (ViT)and convolutional neural networks (CNNs) each possess distinct strengths in medical imaging: ViT excels in capturing long-range dependencies through self-attention, while CNNs are adept at extracting local features via spatial convolution filters. While ViT may struggle with capturing detailed local spatial information, critical for tasks like anomaly detection in medical imaging, shallow CNNs often fail to effectively abstract global context. This study aims to explore and evaluate hybrid architectures that integrate ViT and CNN to leverage their complementary strengths for enhanced performance in medical vision tasks, such as segmentation, classification, reconstruction, and prediction.
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