We report design and construction of an FPGA-based high-speed swept-source polarization-sensitive optical coherence
tomography (SS-PS-OCT) system for clinical retinal imaging. Clinical application of the SS-PS-OCT system is accurate
measurement and display of thickness, phase retardation and birefringence maps of the retinal nerve fiber layer (RNFL)
in human subjects for early detection of glaucoma. The FPGA-based SS-PS-OCT system provides three incident
polarization states on the eye and uses a bulk-optic polarization sensitive balanced detection module to record two
orthogonal interference fringe signals. Interference fringe signals and relative phase retardation between two orthogonal
polarization states are used to obtain Stokes vectors of light returning from each RNFL depth. We implement a
Levenberg-Marquardt algorithm on a Field Programmable Gate Array (FPGA) to compute accurate phase retardation
and birefringence maps. For each retinal scan, a three-state Levenberg-Marquardt nonlinear algorithm is applied to 360
clusters each consisting of 100 A-scans to determine accurate maps of phase retardation and birefringence in less than 1
second after patient measurement allowing real-time clinical imaging-a speedup of more than 300 times over previous
implementations. We report application of the FPGA-based SS-PS-OCT system for real-time clinical imaging of patients
enrolled in a clinical study at the Eye Institute of Austin and Duke Eye Center.
Segmentation of the retinal nerve fiber layer (RNFL) from swept source polarization-sensitive optical coherence
tomography (SS-PSOCT) images is required to determine RNFL thickness and calculate birefringence. Traditional
RNFL segmentation methods based on image processing and boundary detection algorithms utilize only optical
reflectivity contrast information, which is strongly affected by speckle noise. We present a novel approach to segment
the retinal nerve fiber layer (RNFL) using SS-PSOCT images including both optical reflectivity and phase retardation
information. The RNFL anterior boundary is detected based on optical reflectivity change due to refractive index
difference between the vitreous and inner limiting membrane. The posterior boundary of the RNFL is a transition zone
composed of birefringent axons extending from retinal ganglion cells and may be detected by a change in birefringence.
A posterior boundary detection method is presented that segments the RNFL by minimizing the uncertainty of RNFL
birefringence determined by a Levenberg-Marquardt nonlinear fitting algorithm. Clinical results from a healthy
volunteer show that the proposed segmentation method estimates RNFL birefringence and phase retardation with lower
uncertainty and higher continuity than traditional intensity-based approaches.
Retinal nerve fiber layer (RNFL) thickness, a measure of glaucoma progression, can be measured in images acquired
by spectral domain optical coherence tomography (OCT). The accuracy of RNFL thickness estimation, however, is
affected by the quality of the OCT images. In this paper, a new parameter, signal deviation (SD), which is based on the
standard deviation of the intensities in OCT images, is introduced for objective assessment of OCT image quality. Two
other objective assessment parameters, signal to noise ratio (SNR) and signal strength (SS), are also calculated for each
OCT image. The results of the objective assessment are compared with subjective assessment. In the subjective
assessment, one OCT expert graded the image quality according to a three-level scale (good, fair, and poor). The OCT
B-scan images of the retina from six subjects are evaluated by both objective and subjective assessment. From the
comparison, we demonstrate that the objective assessment successfully differentiates between the acceptable quality
images (good and fair images) and poor quality OCT images as graded by OCT experts. We evaluate the performance
of the objective assessment under different quality assessment parameters and demonstrate that SD is the best at
distinguishing between fair and good quality images. The accuracy of RNFL thickness estimation is improved
significantly after poor quality OCT images are rejected by automated objective assessment using the SD, SNR, and
SS.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.