3D Mueller Matrix Reconstruction of the Optical Anisotropy Parameters of Myocardial Histopathology Tissue Samples

Abstract

Diseases affecting myocardial tissues are currently a leading cause of death in developed nations. Fast and reliable techniques for analysing and understanding how tissues are affected by disease and respond to treatment are fundamental to combating the effects of heart disease. A 3D Mueller matrix method that reconstructs the linear and circular birefringence and dichroism parameters has been developed to image the biological structures in myocardial tissues. The required optical data is gathered using a Stokes polarimeter and then processed mathematically to recover the individual optical anisotropy parameters, expanding on existing 2D Mueller matrix implementations by combining with a digital holography approach. Changes in the different optical anisotropy parameters are rationalised with reference to the general tissue structure, such that the structures can be identified from the anisotropy distributions. The first to fourth order statistical moments characterising the distribution of the parameters of the optical anisotropy of the polycrystalline structure of the partially depolarising layer of tissues in different phase sections of their volumes are investigated and analysed. The third and fourth order statistical moments are found to be the most sensitive to changes in the phase and amplitude anisotropy. The possibility of forensic medical differentiation of death in cases of acute coronary insufficiency (ACI) and coronary heart disease (CHD) is considered as a diagnostic application. The optimal phase plane (θ∗=0.7rad) has been found, in which excellent differentiation accuracy is achieved ACI and CHD -Ac(ΔZ4(θ∗,ΦL,ΔL))=93.05%÷95.8%. A comparative analysis of the accuracy of the Mueller-matrix reconstruction of the parameters of the optical anisotropy of the myocardium in different phase planes (θ=0.9rad and θ=1.2rad), as well as the 2D Mueller-matrix reconstruction method was carried out. This work demonstrates that a 3D Mueller matrix method can be used to effectively analyse the optical anisotropy parameters of myocardial tissues with potential for definitive diagnostics in forensic medicine.

Optical anisotropy composition of benign and malignant prostate tissues revealed by Mueller-matrix imaging

“…The use of complex polarization-interference and digital holographic reconstruction of layer-by-layer spatial distribution of the fields of complex amplitudes induced by optical anisotropy of biological tissues at different depths is well known for a while [ 38 – 40 ]. We adopted the layered tomography mapping of optical anisotropy of the histological sections of miocard [ 33 ]. Briefly, the reconstruction of spatial layered distribution of the optical anisotropy is based on the following steps: The polarizers are used to get six distinct polarization states in both the sampling ( ) and reference ( ) beams: ; ; ; ; ; .…”

“…Whereas, utilizing a statistical analysis of the obtained layered MM images and their derivatives – depolarization maps the accuracy of cancerous tissues demarcation was assessed. While the obtained depolarization maps do not carry direct information about the optical anisotropy of cancerous tissues, the development of this technique was the digital computational reconstruction of optical anisotropy maps of myocardial fibrillar networks and their successful use to differentiate the degree of necrotic changes [ 33 ]. The developed approach is using the differential components of the Müller matrices Ossikowski - Devlaminck [ 21 – 25 ] that provides an opportunity for reconstruction of layered maps of average values of linear and circular birefringence and dichroism of partially depolarizing layers of biological tissues with another type of pathology - cancerous tissues.…”

Polarization-Based Histopathology Classification of Ex Vivo Colon Samples Supported by Machine Learning

“…Such an inevitable obstacle could be overcome by adopting various multimodal optical techniques for providing adequate support to clinicians [15][16][17][18][19]. It was shown earlier that tissue polarimetry techniques could be effectively combined to juxtapose polarization and depolarization parameters from different health conditions after scanning, embrace the Poincaré sphere visualization for qualitative differentiation, and construct various depolarization spaces [20][21][22][23][24][25][26][27][28]. Ample diagnostic information related to the morphology of the tissue specimens under study is encoded in their Mueller matrices [26,[29][30][31][32].…”

Differentiation of Human GBM From Non-GBM Brain Tissue With Polarization Imaging Technique

“…In the past few years, based on histological examination of the detected sample as the gold standard, various PIT images of cancers have been reported to differentiate between healthy and cancerous tissue ( 13 , 24 – 30 ). In particular, tissue polarimetry was used to effectively analyze how the myocardial tissues were affected by disease for definitive diagnostics in forensic medicine ( 31 ). Machine learning algorithms were used to extract particular features for human ex vivo colon specimen classification between healthy and tumor zones ( 32 ).…”