Morphological characterization of cells in concentrated suspensions using multispectral diffuse optical tomography.

Area of Science:

• Biophysics
• Optical Imaging
• Computational Modeling

Background:

• Morphological characterization of cells is crucial for understanding biological processes and disease states.
• Accurate cell concentration and shape determination in suspensions are challenging, especially in turbid media.
• Diffuse optical tomography (DOT) offers non-invasive imaging but requires robust inverse algorithms for accurate parameter retrieval.

Purpose of the Study:

• To evaluate the capabilities and limitations of a T-matrix based inverse algorithm for morphological cell characterization.
• To simultaneously retrieve geometrical parameters (shape) and concentration of cells in suspensions.
• To assess the impact of non-spherical particle models and scattering orders on algorithm performance.

Main Methods:

• Utilized a non-spherical model of particle scattering, specifically randomly oriented spheroidal particles.
• Employed a T-matrix based inverse algorithm to analyze reduced scattering coefficient spectra.
• Applied multispectral diffuse optical tomography (MS-DOT) to obtain spectral data.
• Investigated both round and spheroidal cell models at various concentrations.

Main Results:

• The T-matrix inverse algorithm successfully retrieved geometrical parameters and cell concentration simultaneously.
• Algorithm performance was evaluated for different cell shapes (round and spheroidal) and concentrations.
• The study quantified the influence of multiple and higher-order scattering on the accuracy of morphological characterization.

Conclusions:

• The T-matrix based inverse algorithm shows significant potential for morphological cell characterization in concentrated suspensions.
• The non-spherical particle model and consideration of scattering effects are critical for accurate results.
• This approach advances cell analysis in complex biological media using MS-DOT.