3D Mueller matrix mapping of layered distributions of depolarisation degree for analysis of prostate adenoma and carcinoma diffuse tissues

Abstract

Prostate cancer is the second most common cancer globally in men, and in some countries is now the most diagnosed form of cancer. It is necessary to differentiate between benign and malignant prostate conditions to give accurate diagnoses. We aim to demonstrate the use of a 3D Mueller matrix method to allow quick and easy clinical differentiation between prostate adenoma and carcinoma tissues with different grades and Gleason scores. Histological sections of benign and malignant prostate tumours, obtained by radical prostatectomy, were investigated. We map the degree of depolarisation in the different prostate tumour tissues using a Mueller matrix polarimeter set-up, based on the superposition of a reference laser beam with the interference pattern of the sample in the image plane. The depolarisation distributions can be directly related to the morphology of the biological tissues. The dependences of the magnitude of the 1st to 4th order statistical moments of the depolarisation distribution are determined, which characterise the distributions of the depolarisation values. To determine the diagnostic potential of the method three groups of histological sections of prostate tumour biopsies were formed. The first group contained 36 adenoma tissue samples, while the second contained 36 carcinoma tissue samples of a high grade (grade 4: poorly differentiated—4 + 4 Gleason score), and the third group contained 36 carcinoma tissue samples of a low grade (grade 1: moderately differentiated—3 + 3 Gleason score). Using the calculated values of the statistical moments, tumour tissues are categorised as either adenoma or carcinoma. A high level (> 90%) accuracy of differentiation between adenoma and carcinoma samples was achieved for each group. Differentiation between the high-grade and low-grade carcinoma samples was achieved with an accuracy of 87.5%. The results demonstrate that Mueller matrix mapping of the depolarisation distribution of prostate tumour tissues can accurately differentiate between adenoma and carcinoma, and between different grades of carcinoma. This represents a first step towards the implementation of 3D Mueller matrix mapping for clinical analysis and diagnosis of prostate tumours.

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

“…Thus, such a polarization-interference-based cultivation Eqs 13-15, 18, 19 of elements of the first-order differential matrix Eqs 1-10 provides layer-by-layer maps of linear and circular birefringence and dichroism of the myocardial fibrillary networks. This approach extends significantly the functionality of the 3D Mueller-matrix imaging technique for depolarization mapping of diffuse biological layers [48,49], that presently utilized only diagonal elements of the resulting Mueller matrix Eq. 19.…”

“…The key principle for the determination of a threedimensional layered series of distributions of Mueller matrix elements F ik (x, y, z) is the use of a reference laser radiation wave [48,49]. The six distinct polarization states are obtained in both the illuminating (Zond) and reference (Ref) beams: {Zond-Ref} > 0 °; 90 °; 45 °; 135 °; ⊗; ⊕ utilizing polarizer.…”

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].…”

Temporal dynamics of change of polycrystalline component of bioctisses and blood of death from poisoning by alcohol and carbon with different time since death

“…Існує велика різноманітність методів розв'язку цього питання, заснованих на оцінці динаміки ранніх посмертних змін. [1,2] Проте їхня точність може залежати від багатьох факторів, що не задовольняє судово-слідчі органи. Також слід мати на увазі, що більшість з них не дозволяє відтворити отриманий результат, тобто він повністю залежить від досвіду та рівня підготовки експерта, який проводить дослідження.…”

“…де T1 -час початку вимірювань величини статистичного моменту Wi (1) ; T2 -час завершення вимірювань лінійної зміни величини статистичного моменту Wi (1) . Було встановлене зростання лінійного діапазону зміни величини статистичних моментів 1-4-го порядків, що характеризують асиметрію й ексцес розподілів величини ЛД гістологічних зрізів мозку померлих за величиною ДНС до 20 год.…”