Model for light scattering in biological tissue and cells based on random rough nonspherical particles

T-matrix-based inverse algorithm for morphologic characterization of nonspherical particles using multispectral diffuse optical tomography

An inverse algorithm is presented for tomographically imaging morphologic characteristics of nonspherical particles in heterogeneous turbid media. The particles are assumed to have spheroidal shapes with random orientations. The inverse algorithm is based on a relationship of the particle scattering spectra, obtained from multispectral diffuse optical tomography, and the size, concentration, and aspect ratio of spheroidal particles through the T-matrix method. The algorithm is implemented based on Tikhonov-Marquardt regularization techniques that minimize the difference between the observed and calculated scattering spectra. Different statistical models are assumed for the suspended nonspherical particles and the performance of the inverse algorithm is tested using noise-corrupted data up to 50% noise added to the observed scattering spectra.

Dark-field scattering microscopy for spectral characterization of polystyrene aggregates

Light scattering measurements of particle aggregates contain complex information which is difficult to decrypt. Dark-field scattering microscopy in the visible range is used to characterize multi-arranged polystyrene beads. First, measured light scattering spectra of single spheres are compared with the Mie theory. Then, additional spectral measurements of three different sample sets of sphere aggregates are carried out. The aggregates consist of homogeneous spheres and differ in number of spheres, arrangement and contact area. Principal component analysis is used to reduce the number of variables and achieve an accurate classification regarding the aggregate characteristics.