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Independent component analysis (ICA) is an unmixing method based on a linear model. It has previously been applied in in vivo multiwavelength photoacoustic imaging studies to unmix the components representing individual chromophores by assuming that they are statistically independent. Numerically simulated and experimentally acquired two-dimensional images of tissue-mimicking phantoms are used to investigate the conditions required for ICA to give accurate estimates of the relative chromophore concentrations. A simple approximate fluence correction was applied to reduce but not completely remove the nonlinear fluence distortion, as might be possible in practice. The results show that ICA is robust against the residual effect of the partially corrected fluence distortion. ICA is shown to provide accurate unmixing of the chromophores when the absorption coefficient is within a certain range of values, where the upper absorption threshold is comparable to the absorption of blood. When the absorption is increased beyond these thresholds, ICA abruptly fails to unmix the chromophores accurately. The ICA approach was compared to a linear spectroscopic inversion (SI) with known absorption spectra. In cases where the mixing matrix with the specific absorption spectra is ill-conditioned, ICA is able to provide accurate unmixing when SI results in large errors.

Estimating relative chromophore concentrations from multiwavelength photoacoustic images using independent component analysis