Fast unmixing of multispectral optoacoustic data with vertex component analysis

摘要: Abstract Multispectral optoacoustic tomography enhances the performance of single-wavelength imaging in terms sensitivity and selectivity measurement biodistribution specific chromophores, thus enabling functional molecular applications. Spectral unmixing algorithms are used to decompose multi-spectral data into a set images representing distribution each individual chromophoric component while particular algorithm employed determines speed visualization. Here we suggest using vertex analysis (VCA), method with demonstrated good hyperspectral imaging, as fast blind for multispectral tomography. The is subsequently compared previously reported procedure based on combination principal (PCA) independent (ICA). As most practical cases absorption spectrum imaged chromophores contrast agents known or can be determined e.g. spectrophotometer, further investigate so-called semi-blind approach, which priori spectral profiles included modified version termed constrained VCA. this approach also analysed numerical simulations experimental measurements. It has been that, standard VCA attain similar PCA–ICA have robust faster performance, measured information within does not generally render improvements detection