Adaptive Optics Optical Coherence Tomography For In Vivo Retinal Imaging

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ISBN 13 :
Total Pages : 4 pages
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Download or read book Characterization of an AO-OCT System written by and published by . This book was released on 2007 with total page 4 pages. Available in PDF, EPUB and Kindle. Book excerpt: Adaptive optics (AO) and optical coherence tomography (OCT) are powerful imaging modalities that, when combined, can provide high-volumetric-resolution, images of the retina. The AO-OCT system at UC Davis has been under development for 2 years and has demonstrated the utility of this technology for microscopic, volumetric, in vivo retinal imaging [1]. The current system uses an AOptix bimorph deformable mirror (DM) for low-order, high-stroke correction [2] and a 140-actuator Boston Micromachines DM for high-order correction [3]. We are beginning to investigate the potential for increasing the image contrast in this system using higher-order wavefront correction. The first step in this analysis is to quantify the residual wavefront error (WFE) in the current system. Developing an error budget is a common tool for improved performance and system design in astronomical AO systems [4, 5]. The process for vision science systems is also discussed in several texts e.g. [6], but results from this type of analysis have rarely been included in journal articles on AO for vision science. Careful characterization of the AO system will lead to improved performance and inform the design of a future high-contrast system. In general, an AO system error budget must include an analysis of three categories of residual WFE: errors in measuring the phase, errors caused by limitations of the DM(s), and errors introduced by temporal variation. Understanding the mechanisms and relative size of these errors is critical to improving system performance. In this paper we discuss the techniques for characterizing these error sources in the AO-OCT system. It is useful to first calculate an error budget for the simpler case using a model eye, and then add the additional errors introduced for the case of a human subject. Measurement error includes calibration error, wavefront sensor (WFS) CCD noise, and sampling errors. Calibration errors must be measured by an external system. Typically this error is inferred from measurements of the point spread function (PSF). It can also be estimated by measuring known wavefront errors and comparing to the WFS measurement. Both methods will be used in the AO-OCT system. In this particular system measurement error introduced by the WFS can be caused by low light levels, poor camera sensitivity at the operating wavelength and noise introduced by heat in the uncooled CCD. Also, the gaussian beam profile of the system causes centroids near the edges of the pupil to be dimmer, and thus noisier. The easiest way to estimate measurement error is to compare successive wavefront measurements when the system is stable. This techniques will include vibrations and other systematic errors. Alternatively the measurement error can be estimated from measured signal to noise. This is more complicated but will decouple measurement errors from stability measurements. Ultimately, even if the phase is measured perfectly, performance will still be limited by the fitting error [7]. This error is inversely proportional to the number of actuators of the DM. Basically wavefront errors with spatial frequencies greater than half the number of actuators across the aperture cannot be corrected. For DMs with modal influence functions (like the AOptix Bimorph in the AO-OCT system), this translates to the number of modes which can be corrected. The AO-OCT system over-samples the wavefront, so to some extent, we can measure these out-of-band errors directly. In addition to fitting error, the DM will introduce errors based on the ability of each individual actuator to go to the position demanded by the control system. Generally this voltage step size is limited by the resolution of the drive electronics and can be calculated analytically.