Embossed topographic depolarisation maps of biological tissues with different morphological structures

Volodimir A. Ushenko, Benjamin T. Hogan, Alexander Dubolazov, Anastasiia V. Grechina, Tatiana V. Boronikhina, Mikhailo Gorsky, Alexander G. Ushenko, Yurii O. Ushenko, Alexander Bykov, Igor Meglinski*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Layered topographic maps of the depolarisation due to diffuse biological tissues are produced using a polarisation-holographic Mueller matrix method approach. Histological sections of myocardial tissue with a spatially structured optically anisotropic fibrillar network, and parenchymal liver tissue with a polycrystalline island structure are successfully mapped. The topography of the myocardium maps relates to the scattering multiplicity within the volume and the specific morphological structures of the biological crystallite networks. The overall depolarisation map is a convolution of the effects of these two factors. Parenchymal liver tissues behave broadly similarly, but the different biological structures present cause the degree of scattering multiplicity to increase more rapidly with increasing phase. Through statistical analysis, the dependences of the magnitudes of the first to fourth order statistical moments are determined. These moments characterise the changing distributions of the depolarisation values through the volume of biological tissues with different morphological structures. Parenchymal liver tissue depolarisation maps are characterised by larger mean and variance, and less skewness and kurtosis, compared to the distributions for the myocardium. This work demonstrates that a polarisation-holographic Mueller matrix method can be applied to the assessment of the 3D morphology of biological tissues, with applications in disease diagnosis.

Original languageEnglish
Article number3871
JournalScientific Reports
Issue number1
Publication statusPublished - 16 Feb 2021

Bibliographical note

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Funding: This work received funding from: National Research Foundation of Ukraine, Grant 0061; the ATTRACT project funded by the EC under Grant Agreement 777222; Academy of Finland (Grants 314639 and 325097); and INFOTECH strategic funding. I.M. also acknowledges partial support from MEPhI Academic Excellence Project (Contract No. 02.a03.21.0005), and the National Research Tomsk State University Academic D.I. Mendeleev Fund Program.


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