Multivariate image analysis (MIA) is a methodology for analyzing multivariate images, where the image coordinates are position (two‐ or three‐dimensions) and variable number. Multivariate images can have typical sizes 1024 × 1024, 512 × 512, 256 × 256 etc. and have between two and many hundreds of variables. The variables can be wavelength, electron energy, particle mass and many others. Image analysis concentrates mainly on spatial relationships between pixels in a grey level image. MIA concentrates on the correlation of structure between the variables to provide extra information useful for exploring images and classifying regions in them. The many variables can be transformed into a few latent variable images containing condensed information. The sheer size of the data arrays necessitates visualization of raw data, intermediate data and analysis results. All physical techniques for measuring materials can be made into imaging techniques, describing not only a property, but also its position in a plane or volume. All imaging techniques can be expanded to become multivariate. Multivariate imaging is used in three major fields: remote sensing, medical imaging and microscopy (including macroscopy). In microscopy it can be used to study materials and biological processes by optical, electron and charged particle techniques.
The large gradient coils used in MRI generate, simultaneously with the pulsed radiofrequency (RF) wave, acoustic noise of high intensity that has raised concern regarding hearing safety. The sound pressure levels (SPLs) and power spectra of MRI acoustic noise were measured at the position of the human head in the isocenter of five MRI systems and with 10 different pulse sequences used in clinical MR scanning. Each protocol, including magnetization-prepared rapid gradient echo (MP-RAGE; 113 dB SPL linear), fast gradient echo turbo (114 dB SPL linear), and spin echo T1/2 mm (117 dB SPL linear), was found to have the high SPLs, rapid pulse rates, amplitude-modulated pulse envelopes, and multipeaked spectra. Since thickness and SPL were inversely related, the T1-weighted images generated more intense acoustic noise than the proton-dense T2-weighted measures. The unfiltered linear peak values provided more accurate measurements of the SPL and spectral content of the MRI acoustic noise than the commonly used dB A-weighted scale, which filters out the predominant low frequency components. Fourier analysis revealed predominantly low frequency energy peaks ranging from .05 to approximately 1 kHz, with a steep high frequency cutoff for each pulse sequence. Ear protectors of known attenuation ratings are recommended for all patients during MRI testing.
Endothelin-1 (ET-1) was unilaterally applied onto the surface of the dorsal frontoparietal cortex of the rat. Cortical blood flow measurements using laser-Doppler flowmetry demonstrated dose-dependent reductions of frontoparietal cortical blood flow. Histological analysis demonstrated dose-related lesions and the time course was followed using MRI. The lesions appear to be associated with a large penumbra area indicated by morphological characteristics. Thus, cortical surface exposure to ET-1 may produce graded lesions of the frontoparietal cortex related to local ischemia.
One possible way of monitoring plastic particles in sea water is by imaging spectroscopic measurements on filtrates. The idea is that filters from seawater sampling can be imaged in many wavelengths and that a multivariate data analysis can give information on (1) spatial location of plastic material on the filter and (2) composition of the plastic materials. This paper reports on simulated samples, with spiked reference plastic particles and real seawater filtrates containing microplastic pollutants. These real samples were previously identified through visual examination in a microscope. The samples were imaged using three different imaging systems.The different wavelength ranges were 375-970 nm, 960-1662 nm and 1000-2500 nm. Data files from all three imaging systems were analysed by hyperspectral image analysis. The method using the wavelength span 1000-2500 nm was shown to be the most applicable to this specific type of samples and gave a 100% particle recognition on reference plastic, above 300 µm, and an 84% pixel recognition on household polyethylene plastic. When applied to environmental samples the technique showed an increase in identified particles compared with visual investigations. These initial tests indicate a potential underestimation of microplastics in environmental samples. This is the first study to demonstrate that hyperspectral imaging techniques can be used to study microplastics down to 300 µm, which is a common size limit used in microplastic surveys.
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