2.1.

Mouse Positioning and Anesthesia

All mice husbandry and handling were in accordance with protocols approved by the University of California Animal Care and Use Committee, which strictly adheres to all National Institutes of Health (NIH) guidelines and satisfies the Association for Research in Vision and Ophthalmology guidelines for animal use. Adult pigmented C57BL/6J and B6 albino mice were obtained from Jackson Laboratories and maintained on a 12:12, $\sim 100\text{-}\mathrm{lux}$ light cycle. During the experiments, mice were anesthetized with the inhalational anesthetic isoflurane (2% in ${\mathrm{O}}_2$), and their pupils were dilated with medical grade tropicamide and phenylephrine. A contact lens and gel (GelTeal Tears, Alcon) were used to maintain the cornea transparency for in-vivo retinal imaging.^26,28 A commercially available bite bar (Kopf Instruments, Inc., Model 1923-B Mouse Gas Anesthesia Head Holder) was used to stabilize the mouse head and deliver the oxygen and isoflurane.

2.2.

University of California Davis Multimodal SLO/OCT Mouse Retinal Imaging System

A combined SLO/OCT system was used for imaging the mouse retina.¹³ The beam at the mouse pupil was 0.5 mm, which corresponds to a NA of 0.12 for the imaging system.²⁹ The light power is $100\mu \mathrm{W}$ for SLO (488 nm laser, Coherent) and $600\mu \mathrm{W}$ for OCT (center wavelength 860 nm with a bandwidth of 132 nm, SLD, Superlum Ltd.). A custom spectrometer using Balser line scan camera with 2048 pixels was used to acquire the OCT spectra. OCT spectra were acquired at a 100-kHz A-scan rate using custom Labview software that can operate in continuous volume acquisition mode. The volume acquisition time was $\sim 3\mathrm{s}$ for OCT (512 A-scans/B-scan, 512 B-scans/volume—data in Fig. 6) and $\sim 6\mathrm{s}$ for OCTA (540A-scans/B-scan, 1080 B-scans/volume—data in Fig. 7). The OCT volumes were saved at $5\text{volumes}/\min$ ($\mathrm{\Delta }{T}_{\mathrm{OCT}}=12\mathrm{s}$), and OCTA volumes were acquired at $2.5\text{volumes}/\min$ ($\mathrm{\Delta }{T}_{\mathrm{OCTA}}=24\mathrm{s}$).

Initial postprocessing was implemented by custom Matlab™ code implementing standard OCT processing, including direct current subtraction, wavelength-to-$k$ (wave number) resampling and interpolation, dispersion compensation, Hann windowing, and FFT.^30,31 The OCTA data were processed by a custom phase-variance OCT (pv-OCT) C++ code to visualize blood flow and generate OCTA B-scans.^32,33 The serially recorded volumetric data sets, including intensity and OCTA data, were further coregistered to correct for motion artifacts and allow averaging.

The serial volume registration was implemented by image processing in MATLAB and ImageJ. As described in detail below, serially collected volumetric data set were initially processed with the custom MATLAB code for registration, and then the ImageJ “MultiStackReg” and “StackReg” plugins were applied to coregister prearranged series of stacks.

2.3.

Retinal Image Movements in Anesthetized Mice during Imaging

Although the general anesthesia used during retinal imaging greatly helps to reduce eye movements that distort images, the remaining eye movements are not negligible, especially for long acquisition times if cellular resolution averaging is desired. Here we present a simple observation of the retinal movements that occur in anesthetized animals and their effects on volumetric image data. Figure 1(a) shows the Cartesian coordinate system on the retina used to represent the 3-D data. Here the $x$ and $y$ axes correspond to OCT galvo-scanners’ fast and slow scanning directions, respectively, while the $z$ axis is the OCT depth or A-scan direction (along which data are acquired within the pixel dwell time of $10\mu \mathrm{s}$). The fast OCT $x$-scanner, which generates B-scans, scans at $\sim 200\mathrm{Hz}$, while the slower $y$-scanner, which shifts the B-scans to generate the volumes, sweeps across the image field at $\sim 0.3\mathrm{Hz}$. Figure 1(b) depicts the most common image displacements that we observe. Slower motion artifacts that arise from small eye drift result in translation $(x,y,z)$ and rotation $(\phi ,\theta )$ between successive volumes, and can be easily corrected by rigid-body transformation using ImageJ stack registration. We previously used a similar framework to correct for motion artifacts within a single volume in human retinal imaging with AO-OCT,³⁴ but here we apply a multistep process to achieve volume-to-volume coregistration. In addition to the relatively small volume displacements just described, there are also rarely occurring distortions that create scaling changes between volumes [Fig. 1(c)]; these rare distortions are produced by rotations of the eye along the $x$ or $y$ axis or axial displacement of the eye as a whole. Unlike in most human retinal imaging, a contact lens and gels were used in our mouse retinal imaging system, which resulted in the creation of more degrees of freedom for image distortions. For example, moving the contact lens 1 mm away from the cornea (resulting in gel thickness changes) can easily increase the scaling factor to 30%.²⁶ If the gel thickness changes unevenly across the pupil (e.g., during eye rotation along $x$ or $y$ axis one side becomes thicker than the other side), nonisotropic scaling between $x$ and $y$ axes will occur. Although such displacements are rare, they can nonetheless be detected in the image analysis and corrected by the ImageJ affine transformation. However, based on our experience the most efficient approach is to examine registered volumes using en face intensity projection after first correcting them for the common motion artifacts, and simply discard these volumes whose en face projections show scaling deformations, rather than to use more complex registration. The interval between mouse breaths can be $\sim 1$ to 3 s, and the eye movements caused by the mouse breathing will be recorded during the OCT volume acquisition [Fig. 1(d)]. We noticed that the duration of the eye movement during breathing is much shorter than the interval between breaths; thus, only small portion of the data is distorted [Fig. 1(d) middle and left panels]. These distortions do not affect the registration between en face images and are not noticeable after averaging. Thus, the image distortions caused by mouse breathing were ignored in our method.

Fig. 1

Characterization of mouse eye movements during retinal imaging under anesthesia. (a) Schematic representation of the in vivo retina scanning through the eye’s optics (top panel); although the retina is a curved surface, the OCT volume data are flat for modest scan angles ($<30\mathrm{deg}$) (middle panel), and so can be displayed in a 3-D Cartesian coordinate system. For such a 3-D volume, there are 6 degrees-of-freedom of movement, taken to be translations along the three axes ($\overline{x}$, $\overline{y}$, and $\overline{z}$) and the corresponding rotations (bottom panel). (b) Visual representation of the most common distortions observed between OCT data sets: lateral displacements ${\overline{x}}_e$ and ${\overline{y}}_e$, and rotation $\phi$ in the $x\text{–}y$ plane; with respect to B-scans, the errors from consecutive volumes are displacements, ${\overline{x}}_B$ and ${\overline{z}}_B$ and rotation $\theta$ (for simplicity we depict rotation only in the $x$ and $y$ planes). All can be corrected efficiently by rigid-body transformations. (c) Other potential errors include rotations along the $x/y$ axis resulting in nonuniform scaling in $x$ and $y$ ($x^{\prime }$ and $y^{\prime }$, respectively) and overall scale changes (magnification); both can be corrected by affine transformations. (d) The mouse eye movement caused by breathing is much shorter than the interval between breaths. Thus, the corresponding distortion is relatively small and does not affect registration between en face images. Left panel shows timing of data acquisition (blue) with frame acquisition time ($T_1$), and mouse breathing (red) characterized by time interval between breaths ($T_2$) and duration of breathing artifacts ($T_3$); middle and right panels show two examples of OCT single volume en face projections for short (middle panel) and long (right panel) volume acquisition times indicated by $T_1$. Locations of breathing artifacts are marked by a dashed rectangle in the middle panel and arrows in the right panel, respectively.

2.4.

Correction of the Mouse Retinal Movements Present during Anesthesia Using Stepwise OCT/OCTA Volume Registration

To correct the retinal motion artifacts described above, a method of image registration between consecutive volumes is needed. However, no broadly accepted serial volume registration is commonly available. Thus, custom volume registration software had to be developed. In developing, we tried to use well vetted and simple freely available registration tools. We found that one of the simplest and most reliable 2-D image alignment tools in ImageJ—StackReg plugin³⁵—and its extensions such as MultiStackReg³⁶ work very well for registering successive B-scans, as well as en face projections of OCT Volumes. Notably, the MultiStackReg plugin aligns stacks of images to one single reference image, thereby eliminating the error accumulation that occurs with the StackReg plugin. Thus, we implemented a stepwise 2-D registration for serial OCT/OCTA volume registration. Importantly, when several imaging channels are acquired simultaneously (e.g., OCT intensity and OCTA), one needs to be able to align the data to one of the channels only (e.g., OCT intensity) and then apply the correcting transformations directly to the other channels. This important feature is also available in MultiStackReg plugin.

We now describe the stepwise volume registration method, which uses a series of 2-D registrations to correct the motion artifacts between volumes (Fig. 2). After acquisition of a series of OCT volumes, for each OCT volume we first create an en face intensity projection image from the whole volume or, in case when the vascular features are not visible enough, projection from the inner retina [from outer plexiform layer (OPL) to nerve fiber layer (NFL)] was used to improve the overall registration performance. Then we use the MultiStackReg ImageJ plugin to register the full set of en face images to a reference image from the middle of the series. Because the intravolume distortion only occupies a small portion of the data, the single middle frame (in the middle of the time series stack) is usually sufficient to be selected as a proper reference. The transformation matrix for the registration of each en face image was saved, and then applied to the entire volumes in the OCT series (including the intensity and other channels, e.g., OCTA). After registration and motion correction with the transformations, the volumes were next cropped to maintain only the overlapping regions, as shown by the smallest rectangle in Fig. 2(b); this removes empty regions to allow efficient second step of registration between the B-scans. In the next step, a temporal series of B-scans from the same $y$ location in the series of OCT volumes was extracted and enclosed in a stack for registration (BM stack). Then, the motion errors between successive B-scans in the BM stack were corrected with MultiStackReg and the transformations were recorded; the same transformation matrices were then applied to all other channels of the data sets (e.g., OCTA). Specifically, this latter step must be performed for each B-scan within the serial OCT volumes [steps in Figs. 2(d)–2(e)] until the entire overlapping volume region has been corrected. After BM-stack registration, we then averaged the series of B-scans from each $y$ location to achieve the TSA volume.

Fig. 2

Flowchart for stepwise multivolume registration. (a) Acquisition of multiple OCT volumes. (b) Extraction of a transformation matrix characterizing motion between volumes using en face OCT intensity projection images from each volume. (c) Application of the transformation derived in step (b) to shift/rotate all data in the multichannel volumes. (d) Visualization of a representative time series stack (BM stack) of OCT intensity data for one $y$ location. (e) The set of B-scans comprising the BM stack for each $y$ position is registered, correcting for transformations between scans in the stack. (f) TSA intensity (red) and OCTA (green) volumes are created by directly averaging for fine alignment. (g) En face images illustrating the effect of a final stack registration of TSA B-scans with the StackReg plugin, which corrects for fine motion artifacts between adjacent B-scans in the TSA volume.

However, it was noted that in the TSA volumes there were sometimes slight offsets (possibly arise from intravolume motions caused by mouse heartbeat or scanners off-axis vibration) between adjacent B-scans [as shown in Fig. 2(g) top image, arrows]. Usually, a simple registration using StackReg plugin with intensity OCT data assigned to one color (e.g., red) channel and OCTA information assigned to another color (e.g., green) channel was performed to correct for the fine position errors between adjacent TSA B-scans. This allowed the production of a smoothed TSA volume of OCT intensity and/or OCTA data. The core of this method relies on three serially performed registrations, marked by three dash-dotted rectangles in Fig. 2.

2.5.

Effects of TSA on OCT Data Sets

The average volume created by TSA can be expected to provide improved visualization of weakly scattering structures in the 3-D sample volume on the basis of general principles. For example, if backscattering from a structure spanning several adjacent voxels results in an average of $\overline{n}$ photons collected by the detector, Poisson fluctuations of magnitude $\sqrt{\overline{n}}$ from voxel to voxel could make the structure undetectable in a single volume. TSA with $N$ volumes would reduce the fluctuations to $\sqrt{\overline{n}/N}$, while the signal from all voxels remains $\overline{n}$, so that TSA results in increased sensitivity and dynamic range of OCT intensity images. Averaging without the precise alignment provided by TSA, however, would not confer these benefits because the nominally identical voxel in a series of poorly aligned volumes would have variations arising from sample-to-sample variation in the underlying structure due to motion. What would result in a reduction of both resolution and speckle contrast? Interestingly, averaging without the precise 3-D alignment provided by TSA, but with the simple 2-D BM-scan registration as it is done in most commercial clinical OCT systems, still confers most of the TSA benefits. This is due to the fact that even imperfectly coregistered B-scans in $y$ axis (slow scanning direction) have nominally similar voxel values in the same retinal layer. This allows achieving speckle contrast reduction and signal-to-noise ratio (SNR) benefit of averaging but at the cost of a reduction in lateral resolution.³⁷

There are additional benefits of TSA that goes beyond this classical improvement in image sensitivity and dynamic range. One arises from improved phase stability associated with each voxel: this increases the fidelity of the OCTA data set.²⁴ A second is a visible reduction of speckle contrast, which greatly improves the visibility of cellular and subcellular features on OCT intensity data.³⁸ To quantify these benefits, we calculated cross correlations between intensity BM stacks to estimate the time needed for full decorrelation of the speckle fields. We also measured changes in speckle contrast and intensity histograms for TSA performed on a large number ($N=150$) of volumes. Here, we introduce three parameters that are used to quantify changes in speckle contrast.

3.1.

Experimental Analysis of Mouse Retinal Movements during Serial Volume Acquisition

To characterize the motion artifacts (described above, Fig. 1) that underlie motions in the images of anesthetized mice, we acquired and subsequently analyzed a multimodal volumetric data set centered on a single fluorescent retinal ganglion cell (RGC) (Fig. 3). This thy1-YFP-16 transgenic mouse (The Jackson Laboratory) expresses a yellow fluorescent protein (YFP) at high levels in motor and sensory neurons, as well as in subsets of central neurons. The complex, unique pattern of the RGC’s submicrometer fluorescent dendrites provides an ideal target for examining motions over an extended imaging session. During the $\sim 14\text{-}\min$ imaging session, 400 consecutive SLO images and 73 consecutive OCT volumes were simultaneously collected at the same retina location, which scanned $\sim 300\times 300{\mu \mathrm{m}}^2$ with $400\times 400\text{pixels}$ sampling. To illustrate the effect of motion between volumes, Fig. 3(a) displays the SLO fluorescence images of a single RGC color-coded by time: motions are manifest as blurring of the image and color fringes. Figure 3(b) shows the result of combining the two images after assigning them with red and green for rigid body and affine registration images, respectively. The improvement in sharpness reveals that these registrations have removed much of the motions of the unregistered average image [Fig. 3(a)], while the uniform color of the combined image indicates that there was little difference between corrections obtained with the two registration methods. Figure 3(c) shows an image of the absolute value of the pixel-by-pixel difference between the images obtained with two different registrations, further revealing that there are no major differences. Nonetheless, a contrast-stretched 32-bit difference between them reveals a small residual difference [Fig. 3(d)]. The pseudo-3-D relief appearance of the cell dendrites is due to slightly sharper and brighter local fluorescence after registration by rigid-body transformation as compared to that with affine. Figure 3(e) provides the averaged line profiles from nine different locations across the GC dendrites [Fig. 3(b) arrows] extracted from two averaged images, one obtained by the rigid body and one by affine registrations. Note that rigid-body registration creates a sharper line profile. This result also suggests that the shadow near the dendrites seen in Fig. 3(d) is mainly caused by the difference in sharpness and brightness, and not by a lateral mismatch between two averaged images. This comparison also reveals that there is a negligible scaling transformation [Fig. 1(c)], because rigid-body correction (no scaling correction) works better than affine correction (correction with scaling transformation). Figures 3(f)–3(g) show the result of coregistration and correction of OCT volumes using the same visualization method as in Fig. 3(b) but applied to the OCT en face and BM stack, respectively. No visible differences (colors) on en face intensity projection and B-scan can be seen. This confirms that the rigid-body registration is sufficient to correct for retinal motion artifacts present in serial volumetric OCT data sets acquired in anesthetized animals. Finally, Figs. 3(h)–3(i) shows translational and rotational errors extracted from the transformations to generate Figs. 3(f)–3(g). Overall, the positional variation of the mouse retina during this 14-min imaging session had a range of $\pm 5\mu \mathrm{m}$ and rotation variation of $\pm 0.3\mathrm{deg}$. In summary, the analysis of this multimodal volumetric data set reveals the nature of the motions that occur in an imaging session and shows that the method developed corrects them successfully.

Fig. 3

Visualization of motion artifacts present during serial mouse retinal imaging, for over 800 s, and their correction. (a) A temporally color-coded fluorescent SLO image of a retinal GC expressing YFP. (b) Average composited RGC image created by placing the results from rigid-body and affine registrations into red and green channels, respectively. (c) Difference between grayscale intensity images obtained with two different registration methods. (d) The same image displayed using ImageJ 32-bit operation of subtraction with stretched intensity. (e) Comparison of dendrites’ line profiles extracted from averaged images obtained using two different methods; dendrites locations were marked in (b) with arrows. Errors represent standard error of the mean (SEM). (f), (g) Merged OCT en face, and B-scan generated the same way as (b). (h), (i) Translational and rotational errors extracted from the registrations of (f) and (g).

3.2.

Direct Comparison of Images from Single and Temporal Speckle-Averaged OCT Volumes

Because OCT involves the interference of partially coherent light, OCT images necessarily have high speckle contrast, which arises from scattering particles in the sample that are smaller than the 3-D PSF.⁴¹ Speckle contrast degrades image quality, making it impossible to resolve fine structures near diffraction-limited resolution. Despite its spatially random appearance, the speckle pattern of a nonvarying sample is fully determined by the underlying spatial distribution of scatterers and would not vary from scan to scan if nothing in the sample moves. However, in-vivo sub-PSF scale scattering particles (e.g., mitochondria and other organelles) are not static, resulting in variation in the speckle pattern between consecutive volumes.⁴⁴ At the same time, larger morphological structures such as cell bodies and vasculature are static on the timescales of image acquisition, allowing image registration. As a consequence, averaging of serial volumes reduces speckle contrast, while maintaining structural information from objects larger than the PSF, yielding images with improved SNR and image contrast. Comparison of single and TSA volumes provides a means of visualizing the speckle-reducing effect of averaging (Fig. 4). Figure 4 shows the comparison between single OCT and OCTA volume and TSA OCT and OCTA volumes (from 150 and 50 serially acquired OCT and OCTA volumes, respectively). In the TSA OCT B-scan, one can distinguish the walls of two large blood vessels in the NFL (arrows). The TSA OCT en face image clearly shows much better blood vessel contrast (including blood vessel walls, arrows), nerve fiber bundles, and novel structures resembling cell somas (arrowhead).

Fig. 4

Comparison of images from single and TSA OCT/OCTA volumes: (a) and (c) individual B-scan and en face images from a single volume; (b) and (d) individual B-scan and en face images from a TSA OCT volume reconstructed from 150 serially acquired volumes; (e) and (g) individual B-scan and en face OPL images from single OCTA volume; (f) and (h) B-scan and en face images from a TSA OCTA volume reconstructed from 50 serially acquired volumes. Scale bars: $100\mu \mathrm{m}$. The en face images (c) and (d) were single layer (one axial pixel) images from the corresponding volumes; the en face images (g) and (h) were maximum projections from the layers around OPL (50 axial pixels). Abbreviations: NFL/GCL, nerve fiber layer/ganglion cell layer; IPL, inner plexiform layer; OPL, outer plexiform layer; ONL, outer nuclear layer.

TSA also greatly improves OCTA data. A comparison between a single volume and TSA OCTA is shown in Figs. 4(e)–4(h), where even smallest capillaries are clearly visible in the OCTA B-scan (red arrows). Similarly, the en face projection of capillaries from OPL shows great improvement of signal (capillaries)-to-noise (random noise between capillaries) ratio. For example, even some parts of the capillaries hidden in the shadows of larger NFL blood vessels in single volumes appear continuous (arrows) in the TSA volume.

3.3.

Quantification of the Effect of Temporal Speckle-Averaging

As described above, the improved cellular contrast offered by TSA relies on averaging uncorrelated speckle patterns to suppress the speckle contrast and improve the SNR of resulting images, what can be quantified by CNR. To estimate the effect of TSA, we analyzed OCT intensity data from the experiment, as shown in Figs. 4(a)–4(d). First, we calculated the cross correlation between B-scans from the BM stack as a function of the time interval between scans [Fig. 5(a)]: the correlation is seen to decline to about 0.2 over 120 s (10 volume scans). Figure 5(b) shows the average ($\pm \mathrm{SEM}$) correlations calculated for 15 pairs of B-scans separated by different times for three different retinal locations with the fitted cross-correlation curves matching the rectangles depicting corresponding retinal locations in Fig. 4(a): while generally true that the longer time interval between two B-scans, the lower the correlation, the results suggest that the maximum decorrelation is achieved in about 2 min. While this implies that waiting 2 min between scans might be optimum for reducing speckle, it needs to be kept in mind that TSA of volumes obtained more frequently, nonetheless, improves the visibility of weakly scattering structures according to $1/\sqrt{N}$, as discussed above. This decrease in the noise of the background ${\sigma }_{\mathrm{bg}}$ as a function of a number of averaged volumes, $N$, is shown in Fig. 5(c). However, the actual data visibility defined by CNR in Eq. (1) exhibits mostly dependence on NSC. NSC [Eq. (2)] declines with increasing $N$, but as expected this decline does not follow the simple relation [Eq. (3)], showing that the number of independent speckle patterns $M$ is less than $N$ for this volume acquisition rate [Fig. 5(d)]. The combined effect of speckle-averaging and SNR is clearly depicted by CNR [Fig. 5(c)], where after the initial reduction of speckle contrast and improvement of SNR for the first 50 volumes, the remaining improvement is mostly due to the reduction of speckle contrast. To further evaluate the change in speckle contrast, we estimated $m$ by fitting the full $K$-distribution [Eq. (4)] to the intensity histogram data from ROIs of TSA B-scans [Fig. 5(e)]. Figures 5(f) shows the estimation of uncorrelated speckle patterns in averaged B-scans implementing fitting of the full $K$-distribution to intensity histograms [Eq. (4)]. As expected, the number of uncorrelated speckle patterns increases steadily with $N$ but clearly deviates from the theoretical limit. The potential benefit of increasing the time interval between acquisition of consecutive volumes, what results in number of uncorrelated speckle fields, is also presented (square and star). Figure 5(f) also shows the comparison between independent speckle patters present in $N$-averaged volumes calculated using Eq. (3) for $M$ and Eq. (4) for $m$. It is evident that simple Eq. (3) underestimates the number of independent speckle patterns compared to results obtained with Eq. (4). This suggests the possible underestimation of the CNR.

Fig. 5

Quantification of the effect of TSA by using the OCT intensity data from Fig. 4: (a) cross correlation between the first B-scan and consecutive B-scans from a single BM stack; (b) averaged cross correlation between B-scans from three different locations in a BM stack separated by different time intervals [the range depicted by the rectangle in (a), and the locations were marked by the corresponding dashed rectangles in Fig. 4(a)]; (c) noise and CNR [calculated from data obtained from the regions depicted by the top two rectangles in Fig. 4(a)] as a function of the number $N$ of averaged volumes; (d) NSC changes calculated for the IPL rectangle in Fig. 4(a) as a function of the number of averaged volumes; (e) example intensity histogram for single and TSA images from the data in the IPL rectangle in Fig. 4(a); (f) estimated number of uncorrelated speckle patterns after averaging shown as a function of the number of averaged volumes $N$.

3.4.

Example Results of TSA of OCT and OCTA

To explore the effect of TSA on the visualization of neuronal and vascular structures in mouse retinal OCT data sets, we applied our technique to imaging pigmented C57BL/6 and B6 albino mice, which are two commonly used mouse genotypes. The C57BL/6 mouse line is one of the most popular mouse strains used in biomedical research. Addition of B6-albino mice allows the investigation of the effect of melanin on retinal data acquired with TSA OCT. Lack of melanin allows the imaging light to penetrate deeper into the choroid in albino mouse, which makes the imaging of choroidal vasculature less challenging than it is in pigmented C57BL/6 mice.

3.4.1.

TSA of 150 intensity volumes from a C57BL/6 mouse retina

To further explore the value of TSA in retinal imaging, in Fig. 6, we present the results from applying TSA to 150 serially collected OCT volumes from a region of the retina of a pigmented (C75BL/6) mouse spanning a $600\times 600{\mu \mathrm{m}}^2$ and sampled by $512\times 512\text{pixels}$. The OCT beam was focused on the superficial retinal layers to better visualize inner retina. Figure 6 shows the results from seven representative layers extracted from the TSA OCT volume, clearly illustrating the improved visibility and contrast. Specifically, the nerve fiber bundles in the NFL, the blood vessels and capillaries in NFL, inner plexiform layer (IPL), and OPL are clearly visible. In addition, one can identify bright circular objects in ONL whose size appears to match the size of the photoreceptor cell bodies by comparing the OCT image with histology data⁴⁵ [Fig. 6(f) zoom-in inset], microglia-like structures on top of the retina [Fig. 6(b) arrows. Inset shows an example of retinal SLO fluorescence image with green fluorescent protein (GFP)-labeled microglia acquired at the same scale, where several microglia cell bodies and corresponding processes are visible], and the cell soma-like structures in the ganglion cell layer (GCL) [Figs. 6(d)–6(e)]. The putative cell somas in the GCL could belong to GCs and/or amacrine cells, and the specific cellular origin of these structures needs to be further tested. The ability to observe cell somas opens the door to quantitative assessment of several retina cell types in vivo, going beyond the classic measurement of retina layer thickness commonly used in the presentation of OCT results till date. Together, these results provide a first glimpse of the improvement in the visualization of cellular morphology achievable by the application of TSA to OCT volumes.

Fig. 6

Visualization of a TSA volume from a 150 serially acquired OCT volumes from a pigmented (C57Bl/6J) mouse. (a) the central B-scan from the TSA volume; (b)–(d) three representative en face images extracted from the volume with depth ($z$) locations indicated by the correspondingly arrows in (a); (e) a magnified depiction of the region in (d) indicated by the dashed square; (b) inset: example fluorescence image of GFP-labeled microglia cells; (f) the “Max Intensity” B-scan projection of the entire TSA volume, inset: zoom-in image of the structures in ONL; (g)–(j) four representative en face images extracted from the TSA volume with depth locations indicated by the corresponding arrows in (f). Scale bars: $100\mu \mathrm{m}$.

3.4.2.

TSA of 50 OCTA volumes from a B6 albino mouse retina

OCTA is a powerful technique that allows the visualization of blood vessels without an extrinsic contrast agent.^25,32 Here we illustrate the application of TSA to serially acquired OCTA volumes. As mentioned above, one of the features of our volume registration method (Fig. 2) is that it allows multichannel registration based on the structural (intensity) OCT data alone. Here we demonstrate this multimodal functionality by coregistering 50 OCTA volumes acquired over $1\times 1{\mathrm{mm}}^2$ with $540\times 1080$ ($360\times 3$) A-scans (Fig. 7). In this data acquisition protocol, three B-scans are collected consecutively at each $y$ position to extract the phase variance that arises from the blood cells scattering as they transit the vasculature.^32,33 For this experiment, a B6 albino mouse was imaged, and the optical focus of the system was set in the middle of the retina near the ONL to achieve good quality imaging of both the retinal and the choroidal vasculature. Similar to the previous example (Fig. 6), large structures, including blood vessels and nerve fiber bundles, are visualized with very good quality by the TSA OCT intensity data. The cell somas in the GCL [Fig. 7(c)] are barely visible due to the shift in the optical focus away from the GCL and lower spatial sampling from the wider PSF. The TSA OCTA volume offers clear visualization of the choroid [Fig. 7(f)] and, more strikingly, of the choriocapillaris [Fig. 7(g)], which has not been previously visualized in the mouse in vivo without contrast agent.^46,47 OCTA data sets can be used to produce very high contrast maps of retinal vasculature, as illustrated with a depth-color-coded projection of the inner retina vessels [Fig. 7(h)].

Fig. 7

Results from the application of TSA to data created from 50 serially acquired OCTA volumes: (a) the central B-scan from the TSA OCT intensity volume; (b)–(d) three representative en face images extracted from the averaged volume with depth ($z$) locations indicated by the correspondingly arrows in (a), and the lateral location of (c) indicated by the green rectangle in (b); (e) the central B-scan of the TSA OCTA volume; (f)–(h) three representative en face projections from the TSA OCTA volume with axial location indicated by the corresponding arrows in (e). Scale bars: $100\mu \mathrm{m}$.