2.1.

Motion Tracking Line Scan Ophthalmoscope

To reduce/minimize the motion artifacts in the final OMAG/OCT images, a proprietary motion tracking system using an LSO was used to guide OCT-scans.³⁵ This motion tracking capability is already available in the commercial Cirrus HD-OCT 5000 system for OCT anatomical imaging (for details see Ref. 36). Very briefly, the initial LSO frame is first selected as a reference. The subsequent LSO frames are used to correlate with the reference frame, from which the eye motion signals, and thus eye fixation shift information was derived. The fixation shift information is used to modify the waveforms that drive the OCT galvanometer scanners to collect scans at the right location. Additionally, the tracking LSO drives the SD-OCT with a validity signal in case of tracking failures. Subthreshold correlation of the current frame with the reference frame is defined as tracking failure.¹⁸ Low correlation is possible when there is large drift, large saccade, vertical motion, blink or misalignments of the pupil. If this is the case, it is considered as an invalid signal. If an invalid signal is received, the SD-OCT discards the invalid scans and reacquires them.

2.2.

Data Processing

After the 3-D volume dataset is acquired, an OMAG algorithm is applied to extract blood flow information.^6,7,34,37 The algorithm is based on an OCT-complex signal differentiation approach that was recently published.^6,7 In brief, the OCT signals between adjacent B-scans are directly differentiated among the 4-repeated B-scans, and then averaged to achieve one cross-sectional blood flow image. After the B-scans at all steps in the slow scan direction are processed, the 3-D OMAG image is generated, representing the retinal vasculature map within the scanned tissue volume. Meanwhile, the residual displacement occurring between adjacent B-scans due to involuntary eye movement is compensated for by two-dimensional (2-D) cross correlation between two adjacent OMAG flow images.^38,37

2.3.

Segmentation and Definition of Retinal Layers

A semiautomated retinal layer segmentation algorithm recently published in Ref. ³⁹ was used to segment different layers from the OCT cross-sectional structural images based on intensity differences. Briefly, the segmentation is based on the automatic detection of the highest magnitude gradient in OCT intensity B-scans for specific tissue interfaces. When it is difficult to find the correct interface, the operator can interrupt the automatic algorithm and manually find the correct interfaces. Segmentation is conducted on the entire 3-D data volume. The positions of each interface are saved after tracing of the entire 3-D data is completed, from which physiological retinal layers are identified. The segmentation results are equally applicable to both the OCT structure images and the OMAG vascular images to produce the enface images of either microstructure or vasculature. The enface image of each layer can be generated by 2-D maximum projection of either OCT or OMAG signals. In the retina, three layers are segmented for normal subjects to represent the vascular networks at different depths, which include nerve fiber layer (NFL), inner retinal layer (including ganglion cell layer and inner plexiform layer), outer retinal layer (including inner nuclear layer and outer plexiform layer). The overlay angiograms are also produced and coded with different colors to give a distinct vasculature network at different depths. The segmentation would help us investigate the vascular changes in different layers, useful for identifying the early stages of diseases.

3.1.

Tracking Performance in the Eye Phantom

To test the performance of tracking LSO, we first used an eye phantom model (Carl Zeiss Meditec Inc. Dublin, California) to demonstrate the motion correction in the tracking system before imaging a human eye. The model eye was placed steadily in the sample arm. A square area of the macular was imaged, including 240 A-lines and 200 B-scans covering $\sim 2.4\times 2.4\mathrm{mm}$². Following the experimental procedure as described in Refs. 18 and19, three conditions were tested and the resulting enface images were generated. The three conditions included: (1) no-motion without tracking; (2) motion without tracking, and (3) motion with tracking. The motion was induced by lightly tapping on the phantom eye randomly during imaging. Figure 1 shows the results under the three conditions. Figure 1(a) shows the model eye imaged without motion in the retina and the tracking system turned off. The motion artifacts induced randomly in the model eye were imaged with the tracking off as shown in Fig. 1(b), where the distortion is obvious. The result under the 3rd condition [Fig. 1(c)] shows how tracking corrects the motion when the motion was introduced into the system. From the comparison between Figs. 1(a) and 1(c), the original structure image is recovered when the tracking system is enabled, demonstrating the efficiency of motion tracking to deliver undistorted OCT images.

Fig. 1

Enface optical coherence tomography images of the model eye under three conditions: (a) no motion is introduced and tracking system disabled, (b) motion is introduced randomly but tracking is turned off, and (c) motion is the same as in (b) but tracking is enabled to compensate motion. The original structure image of model eye is totally recovered when the tracking is on. The image size is $2.4\times 2.4{\mathrm{mm}}^2$.

3.2.

Tracking Performance in Human Eye

Previous reports have shown that the eye motion can create severe artifacts and induce discontinuities and distortions of blood vessels.^40,41 Tracking improves the ability to make repeatable measurements based on OCT structural information. For example, Hu et al.⁴² had demonstrated improved repeatability of retinal thickness measurements using a Zeiss Cirrus HD-OCT LSO-based tracking system. Without motion tracking, Figs. 2(a) and 2(b) demonstrate the OMAG angiogram of the inner and outer retinal vasculature in the macula region (fovea) of a healthy volunteer, where motion artifacts are obvious. The white horizontal line artifacts in the angiograms are generated by the microsaccades [highlighted by arrows as examples in Fig. 2(a)]. This is because the eye movement causes the adjacent B-scans to not be acquired at the exact same location, leading to decorrelation among the repeated B-scans at that location. Further, these motion artifacts are responsible for the discontinuities of blood vessels in the angiogram [highlighted by boxes in Fig. 2(a)]. Depending on the direction of the microsaccades, these discontinuities often give rise to a repetition, or loss, of a certain blood vessel (or a group of vessels). However, it is difficult to identify the drift caused by the positional changes of the eye from individual angiograms. The eye drift causes a slow variation in position over time, which leads to displacements in the angiogram and affects the accuracy of the spatial position of the vasculature. It is necessary to correct the drifts to provide more precise vasculature distribution. The color-coded enface angiogram in Fig. 2(c) is the whole retinal vasculature under the nontracking condition, in which the red indicates the inner retinal layer and the green the outer retinal layer. The connections of vessels between the inner and outer retinal layers are hard to observe due to the artifacts. The foveal avascular zone (FAZ) is also affected by the artifacts [Fig. 2(c)], which would impact our ability to accurately quantify the FAZ area.

Fig. 2

Enface retinal vasculatures obtained by optical microangiography (OMAG) under the conditions of without tracking (top) and with tracking (bottom): (a) and (b) the enface angiograms of inner and outer retinal layers with the artifacts of horizontal strips (arrows) and the vessel discontinuities of vessel (boxes), (c) the false color enface vascular map after merging (a) with (b), (d)–(f) corresponding enface angiograms with the motion tracking enabled. The tracking system works well in eliminating the motion-caused artifacts. The red color in (c) and (f) indicates the inner retinal layer, and the green color the outer retinal layer. The image size is $2.4\times 2.4{\mathrm{mm}}^2$.

The resulting enface retinal angiograms, when the motion tracking feature in the system was enabled, are shown in Figs. 2(d)–2(f), respectively. It can be seen that the artifacts caused by microsaccades, e.g., the white lines and vessel discontinuities, are effectively corrected, giving a smooth and precise FAZ. Meanwhile, the vascular connections of different layers are observed in Fig. 2(f).

The clear and artifact-free angiograms would be important to provide precise vascular information to aid in the diagnosis and treatment of retinal diseases. For example, in the assessment of the progression as well as the therapeutic treatment of diabetic retinopathy, one of the important clinical parameters is to evaluate the variations of FAZ over time. In this regard, the system must provide reliable and undistorted macular vascular maps every time when the patient visits the clinic. This requirement can be fulfilled with the motion-tracking OCT-based microangiography described herein.

To show the usefulness of the motion tracking to provide reliable measurement of FAZ, experiments were conducted to collect the OMAG image over the fovea multiple times. In the experiment, the subject was imaged four times, of which three scans were performed approximately 2 h apart, and one more scan was collected the next day. In each scan session, two OMAG images were collected: one with and another without motion tracking enabled. The results are shown in Fig. 3 without tracking and in Fig. 4 with tracking enabled, respectively, where it is clear that the imaging without motion tracking gives distorted images from one scan to another, leading to difficulty in the interpretation of the vascular images, and more importantly inaccurate measurement of the FAZ over time. However, with motion tracking enabled, the OMAG images were quite repeatable, providing almost identical vascular appearance and connectivity. From the images, we also quantified the FAZ. In the quantification, we first manually drew the contour line that encloses the FAZ (see the dashed line in the false-color images), upon which an ellipse (shown in the right column of Figs. 3 and 4) was fitted by the use of least square fitting algorithm to provide the long (horizontal) and short (vertical) axis lengths. In addition, we also provided the measurements of the FAZ area upon each visit, defined by the enclosure of the manually drawn contour line. The results are provided in Figs. 3 and 4, respectively, and are also tabulated in the Table 1, demonstrating the excellent repeatability and reliability of the FAZ measurements over time by the use of motion tracking features in the system. However, it must be noted that this study is not meant to provide a meaningful comparative performance of tracking versus untracked data acquisition as only a single subject was imaged. Rather, the focus has been to demonstrate that the benefits of tracking-based OCT acquisition could be extended to its functional extension of OCT angiography.

Fig. 3

Reliability of retinal vasculatures obtained by OMAG without tracking. The results were obtained from the same subject at four different time intervals. Shown from left to right are the enface angiograms of (a) inner retina, (b) outer retina, (c) depth-color encoded enface whole retina, and (d) maximum intensity projection of whole retina, respectively. In the color images, the red color indicates the inner retinal layer; and the green color the outer retinal layer. The image size is $2.4\times 2.4{\mathrm{mm}}^2$.

Fig. 4

Reliability of retinal vasculatures obtained by OMAG with tracking. The results were obtained from the same subject as in Fig. 3 at four different time intervals. Shown from left to right are the enface angiograms of (a) inner retina, (b) outer retina, (c) depth-color encoded enface whole retina, and (d) maximum intensity projection of whole retina, respectively. In the color images, the red color indicates the inner retinal layer, and the green color the outer retinal layer. The image size is $2.4\times 2.4{\mathrm{mm}}^2$.

Table 1

Quantitative assessment of foveal avascular zone against multiple scans.

3.3.

Ultrawide-Field Retinal Imaging Based on Tracking Line Scan Ophthalmoscope

Wide-field OCT angiography visualization has been generated previously by mosaicking multiple volume acquisitions.^43,38 However, due to lack of tracking, the prior approaches involved complex postprocessing steps such as splitting a vasculature enface image into segments of artifact free bands and using an acquisition scheme that requires two acquisitions over the same area with orthogonal fast axis scans. In addition to the complexity in postprocessing, this method may not work well enough in situations where the subject is not able to refixate at the same location after the involuntary motion. In our approach, we address these challenges by the use of LSO-based tracking that makes it feasible to acquire a large FOV of retinal vasculature. To achieve this, a montage scanning protocol was implemented under the motion tracking mode to acquire multiple cube scans. In this protocol, the multiple cube scans proceeded one after another at predefined locations (grid) on the retina. There was 10% overlap between adjacent cubes, providing enough space to stitch the images together and avoiding the missing information after all the grids were scanned. It is important to note that during the montage scanning, subjects are not restricted to maintain fixation or to not blink during the entire acquisition. They can have a rest in between scans, and then place their head back onto the chin-rest again for the next scan. The tracking LSO will automatically correct for eye motion for the next cube scan. In the current study, the maximum scanning region for one montage scan is set to $6\times 7\mathrm{grids}$ (or 42 cube scans in total), covering approximately $12\times 16{\mathrm{mm}}^2$ on the retina (roughly 67 degrees of view).

For demonstration, a female volunteer (28 years old) was imaged by the use of the montage protocol. After all 42 cube scans were collected, postprocessing was completed to obtain the retinal vascular images for all the cubes, which were then stitched together to form a large FOV image. Three layers (described in Sec. 2.3) were segmented to give a better demonstration of retinal vasculature according to depth. Figure 5(a) shows the results of the wide field OMAG angiogram ($\sim 12\times 16{\mathrm{mm}}^2$), which includes NFL, inner retinal layer, and outer retinal layer. The color-coded information is as follows: red represents NFL, green represents inner retinal layer, and blue is outer retinal layer. The tracking LSO provided a large FOV microvascular image almost free of motion artifacts. But it is noted that there seem to be some artifacts (vertical or horizontal) still present, which are due to the imperfections caused by stitching the cubes together, not to the motion artifacts.

Fig. 5

(a) Wide field retinal vasculature of a healthy volunteer coded with different colors according to three layers obtained by montage scanning protocol of OMAG, (b)–(d) the magnified OMAG angiograms corresponding to the white rectangles in (a) to demonstrate the detail of blood vessel in different region, including (b) optic nerve head, (c) fovea, and (d) temporal region. The size of (a) is $12\times 16{\mathrm{mm}}^2$. The size of (b)–(d) is $2.0\times 2.4{\mathrm{mm}}^2$.

As is known, the NFL is formed by the expansion of the fibers of the optic nerve. Physiologically, the thickest part is located near the optic disk, gradually diminishing toward the ora serrate. The fiber bundles have an almost straight horizontal course and form an arch around the macula. The retinal vessels lie superficially to the nerve fiber bundles.⁴⁴ This feature of blood vessel appearance within the NFL is clearly observed on the OMAG angiogram (reddish color). The arch shaped region is located around the macula, while the change to an almost straight horizontal course takes place in the temporal region as shown in Fig. 5(a) (the red color). This is the first time that this feature is captured by OCT-based angiography, and the ability to provide imaging of such a vascular feature may be useful in the investigation of the retinal nerve fiber layer.

The magnified OMAG angiograms are selected to demonstrate the detail of blood vessels in different regions, including the optic nerve head [Fig. 5(b)], fovea [Fig. 5(c)], and temporal region [Fig. 5(d)]. The avascular region of the fovea is clearly seen in Fig. 3(c). In the temporal region, there are fewer dense fiber bundles and vessels [Fig. 5(d)].

To give a clear exhibition of the retinal vessel networks, we removed the NFL from the dataset and then displayed only the inner and outer retinal layers, as shown in Fig. 6(a). Red and green represent the inner and outer retinal layers, respectively. Excluding the NFL, the details of the retinal vessel can be appreciated. For comparison, the LSO image was also acquired and shown in Fig. 6(b). The magnified OMAG retinal angiogram is given in Fig. 6(c). A better visualization of the branches of the vascular tree is observed in the OMAG retinal angiogram compared to the LSO image. In addition, depth resolved capillary plexus can be clearly appreciated. The tacking LSO combined with OCT angiography provides better and much more precise images of the vasculature network of the retina than the existing clinical approaches. As is known, most eye diseases of the posterior segment, e.g., AMD and diabetic retinopathy, involve some changes to the vasculature. It is, therefore, expected that OMAG, performed with motion tracking, could be particularly useful in the diagnosis, treatment, and management of these eye diseases.

Fig. 6

(a) Wide field retinal vasculature of a healthy volunteer coded with different colors according to two layers [excluding nerve fiber layer (NFL)] obtained by montage scanning protocol of OMAG. Excluding the NFL, the details of retinal vessels can be more appreciated. For comparison, (b) the scanning laser ophthalmoscope (SLO) image, and (c) the corresponding OMAG angiograms. A much better visualization of vascular tree is observed when compared to SLO image. The size of (a) is $12\times 16{\mathrm{mm}}^2$. The size of (b) and (c) is $11\times 9{\mathrm{mm}}^2$.