EEG forward calculation in realistic volume conductors using the boundary element method suffers from the fact that the solutions become inaccurate for superficial sources. Here we propose to correct an analytical approximation of the respective lead fields with series of spherical harmonics with respect to multiple expansion points. The necessary correction depends very much on the chosen analytical approximation. We constructed the latter such that the correction can be modelled adequately within the chosen basis. Simulations for a 3-shell prolate spheroid demonstrate the accurate modelling of the lead fields. Explicit comparison with analytically known solutions was done for the 3-shell spherical volume conductor showing that relative errors are mostly far below 1% even for the most superficial sources placed directly on the innermost surface.
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http://dx.doi.org/10.1088/0031-9155/50/16/010 | DOI Listing |
J Stat Theory Pract
September 2024
Statistics Online Computational Resource, University of Michigan, 426 North Ingalls Str, Ann Arbor, Michigan 48109-2003.
In this paper, we propose a novel deep neural network (DNN) architecture with fractal structure and attention blocks. The new method is tested to identify and segment 2D and 3D brain tumor masks in normal and pathological neuroimaging data. To circumvent the problem of limited 3D volumetric datasets with raw and ground truth tumor masks, we utilized data augmentation using affine transformations to significantly expand the training data prior to estimating the network model parameters.
View Article and Find Full Text PDFPlast Reconstr Surg Glob Open
December 2024
From Division of Plastic and Reconstructive Surgery, Rutgers New Jersey Medical School, Newark, N.J.
Background: Given the public's tendency to overestimate the capability of artificial intelligence (AI) in surgical outcomes for plastic surgery, this study assesses the accuracy of AI-generated images for breast augmentation and reduction, aiming to determine if AI technology can deliver realistic expectations and can be useful in a surgical context.
Methods: We used AI platforms GetIMG, Leonardo, and Perchance to create pre- and postsurgery images of breast augmentation and reduction. Board-certified plastic surgeons and plastic surgery residents evaluated these images using 11 metrics and divided them into 2 categories: realism and clinical value.
Brain Stimul
December 2024
Department of Electrical and Computer Eng., Worcester Polytechnic Inst., Worcester MA USA; Department of Mathematical Sciences, Worcester Polytechnic Inst., Worcester MA USA.
Background: Modeling brain stimulation at the microscopic scale may reveal new paradigms for various stimulation modalities.
Objective: We present the largest map to date of extracellular electric field distributions within a layer L2/L3 mouse primary visual cortex brain sample. This was enabled by the automated analysis of serial section electron microscopy images with improved handling of image defects, covering a volume of 250 × 140 × 90 μm³.
Comput Biol Med
December 2024
Department of Computer Science, University of Toronto, 40 St George St., Toronto, M5S 2E4, ON, Canada; Neurosciences & Mental Health Research Program, The Hospital for Sick Children, 686 Bay St., Toronto, M5G 0A4, ON, Canada; Department of Diagnostic and Interventional Radiology, The Hospital for Sick Children, 170 Elizabeth St., Toronto, M5G 1H3, ON, Canada; Institute of Medical Science, University of Toronto, 1 King's College Circle, Toronto, M5S 1A8, ON, Canada; Department of Medical Imaging, University of Toronto, 263 McCaul St., Toronto, M5T 1W7, ON, Canada; Department of Mechanical and Industrial Engineering, University of Toronto, 5 King's College Road, Toronto, M5S 3G8, ON, Canada. Electronic address:
Medical image analysis has significantly benefited from advancements in deep learning, particularly in the application of Generative Adversarial Networks (GANs) for generating realistic and diverse images that can augment training datasets. The common GAN-based approach is to generate entire image volumes, rather than the region of interest (ROI). Research on deep learning-based brain tumor classification using MRI has shown that it is easier to classify the tumor ROIs compared to the entire image volumes.
View Article and Find Full Text PDFPLoS One
December 2024
Physical Sciences Platform, Sunnybrook Research Institute, Toronto, ON, Canada.
MRI of patients with Deep Brain Stimulation (DBS) implants is constrained due to radiofrequency (RF) heating of the implant lead. However, "RF-shimming" parallel transmission (PTX) has the potential to reduce DBS heating during MRI. As part of using PTX in such a "safe mode", maps of the RF transmission field (B1+) are typically acquired for calibration purposes, with each transmit coil excited individually.
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