We present the nanoparticle-enabled experimentally trained wavelet-domain denoising method for optical coherence tomography (OCT). It employs an experimental training algorithm based on imaging of a test-object, made of the colloidal suspension of the monodisperse nanoparticles and contains the microscale inclusions. The geometry and the scattering properties of the test-object are known a priori allowing us to set the criteria for the training algorithm. Using a wide set of the wavelet kernels and the wavelet-domain filtration approaches, the appropriate filter is constructed based on the test-object imaging. We apply the proposed approach and chose an efficient wavelet denoising procedure by considering the combinations of the decomposition basis from five wavelet families with eight types of the filtration threshold. We demonstrate applicability of the wavelet-filtering for the in vitro OCT image of human brain meningioma. The observed results prove high efficiency of the proposed OCT image denoising technique.
Optical coherence tomography (OCT) of the ex vivo rat and human brain tissue samples is performed. The set of samples comprises intact white and gray matter, as well as human brain gliomas of the World Health Organization (WHO) Grades I–IV and glioma model 101.8 from rats. Analysis of OCT signals is aimed at comparing the physically reasonable properties of tissues, and determining the attenuation coefficient, parameter related to effective refractive index, and their standard deviations. Data analysis is based on the linear discriminant analysis and estimation of their dispersion in a four-dimensional principal component space. The results demonstrate the distinct contrast between intact tissues and low-grade gliomas and moderate contrast between intact tissues and high-grade gliomas. Particularly, the mean values of attenuation coefficient are 7.56±0.91, 3.96±0.98, and 5.71±1.49 mm−1 for human white matter, glioma Grade I, and glioblastoma, respectively. The significant variability of optical properties of high Grades and essential differences between rat and human brain tissues are observed. The dispersion of properties enlarges with increase of the glioma WHO Grade, which can be attributed to the growing heterogeneity of pathological brain tissues. The results of this study reveal the advantages and drawbacks of OCT for the intraoperative diagnosis of brain gliomas and compare its abilities separately for different grades of malignancy. The perspective of OCT to differentiate low-grade gliomas is highlighted by the low performance of the existing intraoperational methods and instruments.
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