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Scanning the Future of Medical Imaging

Putting Numbers into Biology: The Combination of Light Sheet Fluorescence Microscopy

and Fluorescence Spectroscopy

Abyss Processing – Exploring the Deep in Medical Images

Accurate cap thickness quantification is of fundamental importance for vulnerable plaque detection in cardiovascular research. A segmentation method for intracoronary optical coherence tomography (OCT) image based on least squares support vector machine (LS-SVM) was performed to characterize plaque component borders and quantify fibrous cap thickness. Manual segmentation of OCT images were performed by experts based on combination of virtual-histology intravascular ultrasound (VH-IVUS) and OCT images and used as gold standard. The segmentation methods based on LS-SVM provided accurate plaque cap thickness (an 8.6% error by LS-SVM vs. 71% error by IVUS50) serving as solid basis for plaque modeling and assessment.

Optical coherence tomography angiography (OCTA) has emerged as an advanced in vivo imaging modality, which is widely used for the clinic ophthalmology and neuroscience research in the rodent brain cortex among others. Based on the high numerical aperture (NA) probing lens and the motion-corrected algorithms, a high-resolution imaging technique called OCT micro-angiography is applied to resolve the small blood capillary vessels ranging from 5$\mu$m to 10$\mu$m in diameter. As OCT-based techniques are recently evolving further from the structural imaging of capillaries toward spatio-temporal dynamic imaging of blood flow in capillaries, here we present a review on the latest techniques for the dynamic flow imaging. Studies on capillary blood flow using these techniques will help us better understand the roles of capillary blood flow for normal functioning of the brain as well as how it malfunctions in diseases.

It is necessary to investigate the wavelength-dependent variation rules of the refractive index of edible oils so as to explore the specificity of the dispersion in light propagation, imaging, and interference processes among different types of edible oil products. In this study, by deriving the refractive index equations of the double glass sheet holding device and oil, the reflectance spectra of three different types of oil samples, namely, peanut oil, colza oil, and kitchen waste oil, were measured via a spectrometer. Furthermore, the refractive index model of these different types of oil samples was investigated. Additionally, based on the oil dispersion characteristics, the dispersion of oil in optical coherence tomography (OCT) was compensated via deconvolution. In the wavelength range of $\lambda \in$ (380, 1500)nm, the analytical expressions of the double glass sheet holding device and oils are featured by practical reliability. The refractive indexes of three different types of oils $n\in$ (1.38, 1.52) show normal dispersion characteristics. The Cauchy coefficient matrix of the oil refractive index can be used for oil identification; in particular, the healthy oil and waste oil differ significantly in terms of the Cauchy coefficient matrix in the infrared band. Oil dispersion has almost no influence on the phase spectra of oils but can enhance their amplitude spectra. The dispersion mismatch can be eliminated by calculating the convolution kernel. The envelope broadening factors of OCT interference signals of oil products are 0.84, 0.64, and 0.91, respectively. According to the present research results, the refractive index model of oil can effectively remove the influence of the holding device. The refractive indexes of three different types of oil samples show similar wavelength-dependent variation characteristics, which confirms the existence of many correlated components in these oil samples. The established refractive index model of oil in a wide spectral range, from the ultraviolet to the infrared band, can be adequately employed for identifying different types of oils. The numerical dispersion compensation based on the established refractive index model can enhance the axial resolution in OCT imaging.

Age-related Macular Degeneration (AMD) and Diabetic Macular Edema (DME) are two common retinal diseases for elder people that may ultimately cause irreversible blindness. Timely and accurate diagnosis is essential for the treatment of these diseases. In recent years, computer-aided diagnosis (CAD) has been deeply investigated and effectively used for rapid and early diagnosis. In this paper, we proposed a method of CAD using vision transformer to analyze optical coherence tomography (OCT) images and to automatically discriminate AMD, DME, and normal eyes. A classification accuracy of 99.69% was achieved. After the model pruning, the recognition time reached 0.010 s and the classification accuracy did not drop. Compared with the Convolutional Neural Network (CNN) image classification models (VGG16, Resnet50, Densenet121, and EfficientNet), vision transformer after pruning exhibited better recognition ability. Results show that vision transformer is an improved alternative to diagnose retinal diseases more accurately.

As changes in hard or soft oral tissues normally have a microbiological component, it is important to develop diagnostic techniques that support clinical evaluation, without destroying microbiological formation. The optical coherence tomography (OCT) represents an alternative to analyze tissues and microorganisms without the need for processing. This imaging technique could be defined as a fast, real-time, in situ, and non-destructive method. Thus, this study proposed the use of the OCT to visualize biofilm by Candida albicans in reline resins for removable prostheses. Three reline resins (Silagum-Comfort, Coe-Comfort, and Soft-Confort), with distinct characteristics related to water sorption and fungal inhibition were used. A total of 30 samples (10 for each resin group) were subjected to OCT scanning before and 96 h after inoculation with Candida albicans (URM 6547). The biofilm analysis was carried out through a 2D optical Callisto SD-OCT (930 nm) operated in the spectral domain. Then, the images were preprocessed using a $3\times 3$ Gaussian filter to remove the noise, and then Otsu binarization, allowing segmentation and pixel counting. The layer’s biofilm formed was clearly defined and, indeed, its visualization is modified by water sorption of each material. Silagum-Comfort and Soft-Confort showed some similarities in the scattering of light between the clean and inoculated samples, in which, the latter samples presented higher values of light signal intensity. Coe-Comfort samples were the only ones that showed no differences between the clean or inoculated images. Therefore, the results of this study suggest that OCT is a viable technique to visualize the biofilm in reline materials. Because findings in the literature are still scarcely using the OCT technique to visualize biofilm in reline resins, further studies are encouraged. It should not contain any references or displayed equations.

Adaptive optics (AO) and optical coherence tomography (OCT) are powerful imaging modalities that, when combined, can provide high-resolution, 3-D, in vivo images of the retina. We will discuss general techniques for characterizing a vision science AO system, and we will describe the results of applying these techniques to measure the residual wavefront errors for the UC Davis AO-OCT system. Careful characterization of the AO system will lead to improved performance and inform the design of future systems.

Optical coherence tomography angiography (OCTA) has emerged as an advanced invivo imaging modality, which is widely used for the clinic ophthalmology and neuroscience research in the rodent brain cortex among others. Based on the high numerical aperture (NA) probing lens and the motion-corrected algorithms, a high-resolution imaging technique called OCT micro-angiography is applied to resolve the small blood capillary vessels ranging from 5 μm to 10 μm in diameter. As OCT-based techniques are recently evolving further from the structural imaging of capillaries toward spatio-temporal dynamic imaging of blood flow in capillaries, here we present a review on the latest techniques for the dynamic flow imaging. Studies on capillary blood flow using these techniques will help us better understand the roles of capillary blood flow for normal functioning of the brain as well as how it malfunctions in diseases.

In dermatology, the optical coherence tomography (OCT) is used to visualize the skin over few millimeters depth. These images are affected by speckle, which can alter their interpretation, but which also carries information that characterizes locally the visualized tissue. In this paper, we propose to differentiate the skin layers by modeling locally the speckle in OCT images. The performances of four probability density functions (Rayleigh, Lognormal, Nakagami and Generalized Gamma) to model the distribution of speckle in each skin layer are analyzed. From this study, we propose to classify the pixels of OCT images using the estimated parameters of the most appropriate distribution. Quantitative results with 30 images are compared to the manual delineations of five experts. The results confirm the potential of the method to generate useful data for robust segmentation.