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Significance: Subretinal injection is an effective way of delivering transplant genes and cells to treat many degenerative retinal diseases. However, the technique requires high-dexterity and microscale precision of experienced surgeons, who have to overcome the physiological hand tremor and limited visualization of the subretinal space. Aim: To automatically guide the axial motion of microsurgical tools (i.e., a subretinal injector) with microscale precision in real time using a fiber-optic common-path swept-source optical coherence tomography distal sensor. Approach: We propose, implement, and study real-time retinal boundary tracking of A-scan optical coherence tomography (OCT) images using a convolutional neural network (CNN) for automatic depth targeting of a selected retinal boundary for accurate subretinal injection guidance. A simplified 1D U-net is used for the retinal layer segmentation on A-scan OCT images. A Kalman filter, combining retinal boundary position measurement by CNN and velocity measurement by cross correlation between consecutive A-scan images, is applied to optimally estimate the retinal boundary position. Unwanted axial motions of the surgical tools are compensated by a piezoelectric linear motor based on the retinal boundary tracking. Results: CNN-based segmentation on A-scan OCT images achieves the mean unsigned error (MUE) of ∼3 pixels (8.1 μm) using an ex vivo bovine retina model. GPU parallel computing allows real-time inference (∼2 ms) and thus real-time retinal boundary tracking. Involuntary tremors, which include low-frequency draft in hundreds of micrometers and physiological tremors in tens of micrometers, are compensated effectively. The standard deviations of photoreceptor (PR) and choroid (CH) boundary positions get as low as 10.8 μm when the depth targeting is activated. Conclusions: A CNN-based common-path OCT distal sensor successfully tracks retinal boundaries, especially the PR/CH boundary for subretinal injection, and automatically guides the tooltip’s axial position in real time. The microscale depth targeting accuracy of our system shows its promising possibility for clinical application.

Significance: Selective retina therapy (SRT) selectively targets the retinal pigment epithelium (RPE) and reduces negative side effects by avoiding thermal damages of the adjacent photoreceptors, the neural retina, and the choroid. However, the selection of proper laser energy for the SRT is challenging because of ophthalmoscopically invisible lesions in the RPE and different melanin concentrations among patients or even regions within an eye. Aim: We propose and demonstrate SRT monitoring based on speckle variance optical coherence tomography (svOCT) for dosimetry control. Approach: M-scans, time-resolved sequence of A-scans, of ex vivo bovine retina irradiated by 1.7-μs duration laser pulses were obtained by a swept-source OCT. SvOCT images were calculated as interframe intensity variance of the sequence. Spatial and temporal temperature distributions in the retina were numerically calculated in a 2-D retinal model using COMSOL Multiphysics. Microscopic images of treated spots were obtained before and after removing the upper neural retinal layer to assess the damage in both RPE and neural layers. Results: SvOCT images show abrupt speckle variance changes when the retina is irradiated by laser pulses. The svOCT intensities averaged in RPE and photoreceptor layers along the axial direction show sharp peaks corresponding to each laser pulse, and the peak values were proportional to the laser pulse energy. The calculated temperatures in the neural retina layer and RPE were linearly fitted to the svOCT peak values, and the temperature of each lesion was estimated based on the fitting. The estimated temperatures matched well with previously reported results. Conclusion: We found a reliable correlation between the svOCT peak values and the degree of retinal lesion formation, which can be used for selecting proper laser energy during SRT.

The optical attenuation coefficient (AC), an important tissue parameter that measures how quickly incident light is attenuated when passing through a medium, has been shown to enable quantitative analysis of tissue properties from optical coherence tomography (OCT) signals. Successful extraction of this parameter would facilitate tissue differentiation and enhance the diagnostic value of OCT. In this review, we discuss the physical and mathematical basis of AC extraction from OCT data, including current approaches used in modeling light scattering in tissue and in AC estimation. We also report on demonstrated clinical applications of the AC, such as for atherosclerotic tissue characterization, malignant lesion detection, and brain injury visualization. With current studies showing AC analysis as a promising technique, further efforts in the development of methods to accurately extract the AC and to explore its potential use for more extensive clinical applications are desired.

Age-related macular degeneration (AMD) is a vision-threatening disease that affects the outer retina and choroid of elderly adults. Because photoreceptors are found in the outer retina and rely primarily on the trophic support of the underlying choriocapillaris, imaging of flow or lack thereof in choriocapillaris by optical coherence tomography angiography (OCTA) has great clinical potential in AMD assessment. We introduce a metric using OCTA, named “focal perfusion loss” (FPL) to describe the effects of age and non-neovascular AMD on choriocapillaris flow. Because OCTA imaging of choriocapillaris is vulnerable to artifacts—namely motion, projections, segmentation errors, and shadows—they are removed by postprocessing software. The shadow detection software is a machine learning algorithm recently developed for the evaluation of the retinal circulation and here adapted for choriocapillaris analysis. It aims to exclude areas with unreliable flow signal due to blocking of the OCT beam by objects anterior to the choriocapillaris (e.g., drusen, retinal vessels, vitreous floaters, and iris). We found that both the FPL and the capillary density were able to detect changes in the choriocapillaris of AMD and healthy age-matched subjects with respect to young controls. The dominant cause of shadowing in AMD is drusen, and the shadow exclusion algorithm helps determine which areas under drusen retain sufficient signal for perfusion evaluation and which areas must be excluded. Such analysis allowed us to determine unambiguously that choriocapillaris density under drusen is indeed reduced.