Yasuno leads Computational Optics Group in the University of Tsukuba, and working for the research on optical coherence tomography (OCT) and its applications to ophthalmology and dermatology. Specifically, he is active for polarization sensitive and Doppler OCT. Recent work includes the application of adaptive optics for retinal imaging. He serves as a program committee of SPIE BiOS conference, OSA BIOMED conference and European Conference on Biomedical Optics. Since 2009, he is an associate editor of Optics Express.
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Label-free ex-vivo animal tissue metabolism investigation using dynamic optical coherence tomography
Motion-free optical coherence tomography imaging of retinal disease using Lissajous scanning pattern
In this study, we demonstrate the improvement of motion correction for en-face OCT imaging. The OCT signals are acquired with a Lissajous scanning pattern which has been modified from a standard Lissajous scan to enable OCT angiography (OCT-A) imaging. The lateral motion is estimated from several en-face images of OCT and OCT-A by using a motion estimation algorithm. Some diseased eyes exhibit abnormal patterns in OCT en-face images. Simultaneously using these images will enhance the motion estimation and will improve the motion correction at these abnormal regions. Motion-free imaging for retinal diseases is demonstrated.
The nonlinear effect of SNR to phase retardation and birefringence measurement was previously formulated in detail for a Jones matrix OCT (JM-OCT) [1]. Based on this, we had developed a maximum a-posteriori (MAP) estimator and quantitative birefringence imaging was demonstrated [2]. However, this first version of estimator had a theoretical shortcoming. It did not take into account the stochastic nature of SNR of OCT signal.
In this paper, we present an improved version of the MAP estimator which takes into account the stochastic property of SNR. This estimator uses a probability distribution function (PDF) of true local retardation, which is proportional to birefringence, under a specific set of measurements of the birefringence and SNR. The PDF was pre-computed by a Monte-Carlo (MC) simulation based on the mathematical model of JM-OCT before the measurement. A comparison between this new MAP estimator, our previous MAP estimator [2], and the standard mean estimator is presented. The comparisons are performed both by numerical simulation and in vivo measurements of anterior and posterior eye segment as well as in skin imaging. The new estimator shows superior performance and also shows clearer image contrast.
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