Assessment of the optic nerve, optic disc, and perineural area using shear-wave elastography in patients with multiple sclerosis

Changes in stiffness of the optic nerve and involvement of neurofilament light chains in the course of experimental autoimmune encephalomyelitis, an animal model of multiple sclerosis

Multiple sclerosis and its animal model, experimental autoimmune encephalomyelitis (EAE), are often accompanied by optic neuritis associated with neurofilament disruption. In this study, the stiffness of the optic nerve was investigated by atomic force microscopy (AFM) in mice with induced EAE in the successive phases of the disease: onset, peak, and chronic. AFM results were compared with the intensity of the main pathological processes in the optic nerve: inflammation, demyelination, and axonal loss, as well as with the density of astrocytes, assessed by quantitative histology and immunohistochemistry. Optic nerve tissue and serum levels of neurofilament light chain protein (NEFL) were also examined by immunostaining and ELISA, respectively. The stiffness of the optic nerve in EAE mice was lower than that in control and naïve animals. It increased in the onset and peak phases and sharply decreased in the chronic phase. Serum NEFL level showed similar dynamics, while tissue NEFL level decreased in the onset and peak phases, indicating a leak of NEFL from the optic nerve to body fluids. Inflammation and demyelination gradually increased to reach the maximum in the peak phase of EAE, and inflammation slightly declined in the chronic phase, while demyelination did not. The axonal loss also gradually increased and had the highest level in the chronic phase. Among these processes, demyelination and especially axonal loss most effectively decrease the stiffness of the optic nerve. NEFL level in serum can be regarded as an early indicator of EAE, as it rapidly grows in the onset phase of the disease.

A Generic Pixel Pitch Calibration Method for Fundus Camera via Automated ROI Extraction

Pixel pitch calibration is an essential step to make the fundus structures in the fundus image quantitatively measurable, which is important for the diagnosis and treatment of many diseases, e.g., diabetes, arteriosclerosis, hereditary optic atrophy, etc. The conventional calibration approaches require the specific parameters of the fundus camera or several specially shot images of the chess board, but these are generally not accessible, and the calibration results cannot be generalized to other cameras. Based on automated ROI (region of interest) and optic disc detection, the diameter ratio of ROI and optic disc (ROI-disc ratio) is quantitatively analyzed for a large number of fundus images. With the prior knowledge of the average diameter of an optic disc in fundus, the pixel pitch can be statistically estimated from a large number of fundus images captured by a specific camera without the availability of chess board images or detailed specifics of the fundus camera. Furthermore, for fundus cameras of FOV (fixed field-of-view), the pixel pitch of a fundus image of 45° FOV can be directly estimated according to the automatically measured diameter of ROI in the pixel. The average ROI-disc ratio is approximately constant, i.e., 6.404 ± 0.619 in the pixel, according to 40,600 fundus images, captured by different cameras, of 45° FOV. In consequence, the pixel pitch of a fundus image of 45° FOV can be directly estimated according to the automatically measured diameter of ROI in the pixel, and results show the pixel pitches of Canon CR2, Topcon NW400, Zeiss Visucam 200, and Newvision RetiCam 3100 cameras are 6.825 ± 0.666 μm, 6.625 ± 0.647 μm, 5.793 ± 0.565 μm, and 5.884 ± 0.574 μm, respectively. Compared with the manually measured pixel pitches, based on the method of ISO 10940:2009, i.e., 6.897 μm, 6.807 μm, 5.693 μm, and 6.050 μm, respectively, the bias of the proposed method is less than 5%. Since our method doesn't require chess board images or detailed specifics, the fundus structures on the fundus image can be measured accurately, according to the pixel pitch obtained by this method, without knowing the type and parameters of the camera.