Reconstructing in vivo spatially offset Raman spectroscopy of human skin tissue using a GPU-accelerated Monte Carlo platform

As one type of spatially offset Raman spectroscopy(SORS), inverse SORS is particularly suited to in vivo biomedical measurements due to its ring-shaped illumination scheme. To explain inhomogeneous Raman scattering during in vivo inverse SORS measurements, the light–tissue interactions when excitation and regenerated Raman photons propagate in skin tissue were studied using Monte Carlo simulation. An eight-layered skin model was first built based on the latest transmission parameters. Then, an open-source platform, Monte Carlo e Xtreme(MCX), was adapted to study the distribution of 785 nm excitation photons inside the model with an inverse spatially shifted annular beam. The excitation photons were converted to emission photons by an inverse distribution method based on excitation flux with spatial offsets Δs of 1 mm, 2 mm, 3 mm and 5 mm. The intrinsic Raman spectra from separated skin layers were measured by continuous linear scanning to improve the simulation accuracy. The obtained results explain why the spectral detection depth gradually increases with increasing spatial offset, and address how the intrinsic Raman spectrum from deep skin layers is distorted by the reabsorption and scattering of the superficial tissue constituents. Meanwhile, it is demonstrated that the spectral contribution from subcutaneous fat will be improved when the offset increases to 5 mm, and the highest detection efficiency for dermal layer spectral detection could be achieved when Δs = 2 mm. Reasonably good matching between the calculated spectrum and the measured in vivo inverse SORS was achieved, thus demonstrating great utility of our modeling method and an approach to help understand the clinical measurements.

Assessing pathological features of breast cancer via the multimodal information of multiphoton and Raman imaging

For unveiling the pathological evolution of breast cancer, nonlinear multiphoton microscopic(MPM) and confocal Raman microspectral imaging(CRMI) techniques were both utilized to address the structural and constitutional characteristics of healthy(H), ductal carcinoma in situ(DCIS), and invasive ductal carcinoma(IDC) tissues. MPM-based techniques,including two-photon excited fluorescence(TPEF) and second harmonic generation(SHG), visualized label-free and the fine structure of breast tissue. Meanwhile, CRMI not only presented the chemical images of investigated samples with the K-mean cluster analysis method(KCA), but also pictured the distribution of components in the scanned area through univariate imaging. MPM images illustrated that the cancer cells first arranged around the basement membrane of the duct,then proliferated to fill the lumens of the duct, and finally broke through the basement membrane to infiltrate into the stroma.Although the Raman imaging failed to visualize the cell structure with high resolution, it explained spectroscopically the gradual increase of nucleic acid and protein components inside the ducts as cancer cells proliferated, and displayed the distribution pattern of each biological component during the evolution of breast cancer. Thus, the combination of MPM and CRMI provided new insights into the on-site pathological diagnosis of malignant breast cancer, also ensured technical support for the development of multimodal optical imaging techniques for precise histopathological analysis.

NOISE REDUCTION METHOD FOR OCT IMAGES BASED ON EMPIRICAL MODE DECOMPOSITION

In this paper,the new method for OCT images denoizing based on empirical mode decomposition(EMD)is proposed.The noise reduction is a very important process for following operations to analyze and recognition of tissue structure.Our method does not require any additional operations and hardware modifications.The basics of proposed method is described.Quality improvement of noise suppression om example of edge detection procedure using the classical Canny's algorithm without any additional pre-and post-proc essing operations is demonstrated.Improvement of raw-segmentation in the automatic diagnostic process between a tissue and a mesh implant is shown.

Medical images classification for skin cancer using quantitative image features with optical coherence tomography

Optical coherence tomography(OCT)is employed in the diagnosis of skin cancer.Particularly,quantitative image features extracted from OCT images might be used as indicators to classify the skin tumors.In the present paper,we investigated intensity-based,texture-based and fractalbased features for automatically classifying the melanomas,basal cell carcinomas and pigment nevi.Generalized estimating equations were used to test for differences between the skin tumors.A modified p value of<0.001 was considered statistically significant.Significant increase of mean and median of intensity and significant decrease of mean and median of absolute gradient were observed in basal cell carcinomas and pigment nevi as compared with melanomas.Significant decrease of contrast,entropy and fractal dimension was also observed in basal cell carcinomas and pigment nevi as compared with melanomas.Our results suggest that the selected quantitative image features of OCT images could provide useful information to differentiate basal cell carcinomas and pigment nevi from the melanomas.Further research is warranted to determine how this approach may be used to improve the classification of skin tumors.

COMPLEX OPTICAL CHARACTERIZATION OF MESHIMPLANTS AND ENCAPSULATION AREA

Complex investigation of mesh implants was per formed involving laser confocal microscopy,backscattered probing and OCT imaging methods.The growth of endomysium and fat tissue with microcirculation vessels was observed in the mesh encapsulation region.Confocal microscopy analysis shows that such pat hologies complications such as necrosis formation and microcavities were localized in the area near implant fibers with the size compatible with fiber diameter.And the number of such formnations increase with the increase of the size,number and density of microdefects on the implant surface.Results of mumerical sinulations show that it is possible to control implant installation up to the depth to 4mm with a help of bac kscattering probing.The applicability of OCT imaging for mesh implant control was demonstrated.Special two stage OCT image noise reduction algorithm,including empirical mode decomposition,was proposed for contrast increase and better abnormalities visualization by halving the signal-to-noise ratio.Joint usage of backscattered probing and OCT allows to accurately ascertain implant and surrounding tissue conditions,which reduces the risk of relapse probability.