Optical coherence tomography (OCT) is an imaging modality that acquires high-resolution cross-sectional images of living tissues and it has become the standard in ophthalmological diagnoses. However, most quantitative morphological measurements are based on the raw OCT images which are distorted by several mechanisms such as the refraction of probe light in the sample and the scan geometries and thus the analysis of the raw OCT images inevitably induced calculation errors. In this paper, based on Fermat's principle and the concept of inverse light tracing, image distortions due to refraction occurred at tissue boundaries in the whole-eye OCT imaging of mouse by telecentric scanning were corrected. Specially, the mathematical correction models were deducted for each interface, and the high-precision whole-eye image was recovered segment by segment. We conducted phantom and in vivo experiments on mouse and human eyes to verify the distortion correction algorithm, and several parameters of the radius of curvature, thickness of tissues and error, were calculated to quantitatively evaluate the images. Experimental results demonstrated that the method can provide accurate and reliable measurements of whole-eye parameters and thus be a valuable tool for the research and clinical diagnosis.
Keyphrases
- optical coherence tomography
- high resolution
- diabetic retinopathy
- deep learning
- optic nerve
- cross sectional
- mass spectrometry
- gene expression
- endothelial cells
- convolutional neural network
- machine learning
- emergency department
- high speed
- quantum dots
- photodynamic therapy
- magnetic resonance imaging
- induced pluripotent stem cells