An algorithm is derived and demonstrated that reconstructs an EPR spectral-spatial image from projections with arbitrarily selected gradients. This approach permits imaging wide spectra without the use of the very large sweep widths and gradients that would be required for spectral-spatial imaging with filtered back projection reconstruction. Each projection is defined as the sum of contributions at the set of locations in the object. At each location gradients shift the spectra in the magnetic field domain, which is equivalent to a phase change in the Fourier-conjugate frequency domain. This permits solution of the problem in the frequency domain. The method was demonstrated for 2D images of phantoms consisting of (i) two tubes containing (14)N and (15)N nitroxide and (ii) two tubes containing a pH sensitive trityl radical at pH 7.0 and 7.2. In each case spectral slices through the image agree well with the full spectra obtained in the absence of gradient.
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http://dx.doi.org/10.1016/j.jmr.2014.05.013 | DOI Listing |
Neuroimage
February 2025
Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA; College of Medicine, University of Nebraska Medical Center, Omaha, NE, USA; Department of Pharmacology & Neuroscience, Creighton University, Omaha, NE, USA. Electronic address:
Noninvasive brain stimulation of the primary motor cortex has been shown to alter therapeutic outcomes in stroke and other neurological conditions, but the precise mechanisms remain poorly understood. Determining the impact of such neurostimulation on the neural processing supporting motor control is a critical step toward further harnessing its therapeutic potential in multiple neurological conditions affecting the motor system. Herein, we leverage the excellent spatio-temporal precision of magnetoencephalographic (MEG) imaging to identify the spectral, spatial, and temporal effects of high-definition transcranial direct current stimulation (HD-tDCS) on the neural responses supporting motor control.
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December 2024
School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China.
The bioactive components of chrysanthemum tea are an essential indicator in evaluating its nutritive and commercial values. Combining hyperspectral imaging (HSI) with key wavelength selection and pattern recognition methods, this study developed a novel approach to estimating the content of bioactive components in chrysanthemums, including the total flavonoids (TFs) and chlorogenic acids (TCAs). To determine the informative wavelengths of hyperspectral images, we introduced a variable similarity regularization term into particle swarm optimization (denoted as VSPSO), which can focus on improving the combinatorial performance of key wavelengths and filtering out the features with higher collinearity simultaneously.
View Article and Find Full Text PDFMagn Reson Med
December 2024
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts, USA.
Purpose: Proton magnetic resonance spectroscopic imaging ( -MRSI) provides noninvasive spectral-spatial mapping of metabolism. However, long-standing problems in whole-brain -MRSI are spectral overlap of metabolite peaks with large lipid signal from scalp, and overwhelming water signal that distorts spectra. Fast and effective methods are needed for high-resolution -MRSI to accurately remove lipid and water signals while preserving the metabolite signal.
View Article and Find Full Text PDFPLoS One
December 2024
Department of Computer Science and Engineering, Rajshahi University of Engineering & Technology, Rajshahi, Bangladesh.
The classification of land cover objects in hyperspectral imagery (HSI) has significantly advanced due to the development of convolutional neural networks (CNNs). However, challenges such as limited training data and high dimensionality negatively impact classification performance. Traditional CNN-based methods predominantly utilize 2D CNNs for feature extraction, which inadequately exploit the inter-band correlations in HSIs.
View Article and Find Full Text PDFSci Rep
November 2024
Shandong Provincial Engineering and Technical Center of Light Manipulation, Shandong Provincial Key Laboratory of Optics and Photonic Devices, School of Physics and Electronics, Shandong Normal University, Jinan, 250014, China.
Patch features obtained by fixed convolution kernel have become the main form in hyperspectral image (HSI) classification processing. However, the fixed convolution kernel limits the weight learning of channels, which results in the potential connections between pixels not being captured in patches, and seriously affects the classification performance. To tackle the above issues, we propose a novel Adaptive Pixel Attention Network, which can improve HSI classification by further mining the connections between pixels in patch features.
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