2.1.

Nongated 4-D Imaging Strategy

The nongated 4-D imaging strategy used (illustrated in Fig. 1 ) is similar to that used by Liebling.²⁸ B-mode image sequences are collected at successive imaging planes ( $X\text{-}Z$ planes along the $Y$ direction in Fig. 1): starting at $y_1=0$ , OCT acquires a sequence of 2-D cross-sectional images ( $X\text{-}Z$ plane) for approximately five cardiac cycles, and then the imaging plane moves longitudinally an increment $h$ to the next cross section. Image acquisition is then repeated until the whole region of interest is spanned and $y_L$ is reached. The value of the distance $h$ between adjacent image planes is kept small so that structural imaging data at adjacent locations are similar. An image sequence of a longitudinal cardiac section ( $Y\text{-}Z$ plane in Fig. 1) is also acquired to adjust phase lags later. Therefore, a 4-D image data set consists of $L$ sets of B-mode cross-sectional image sequences acquired along the $Y$ direction and a B-mode image sequence of a longitudinal section. Because OCT image acquisition is nongated, acquired image sequences are out of phase. To reconstruct 4-D images of the heart, image sequences need to be synchronized; that is, the phase relationship between imaging sequences needs to be determined.

Fig. 1

Schematic representation of nongated 4-D image acquisition strategy.

2.2.

Postacquisition Synchronization Procedure

Our synchronization procedure assumes that (i) motion and deformation of the heart are periodic and (ii) changes in cardiac structural features are continuous (structural data in B-mode images acquired at adjacent locations are similar).

To synchronize the acquired image sequences, the following steps are performed:

Compared to Liebling’s synchronization procedure,²⁸ our procedure has three major differences. First, to achieve computational efficiency, rather than using a subset of the image wavelet series, our synchronization procedure analyzes M-mode images that are extracted from the image sequences (see Fig. 2 ). Representing a B-mode image sequence at a location $y_i$ by the intensity function $I_{y_i}(x,z,t)$ , an M-mode image extracted along a vertical line $x=x_k$ of the B-mode sequence is $I_{y_i}^{\prime }(z,t)=I_{y_i}(x_k,z,t)$ . Second, to improve the accuracy of the synchronization procedure, instead of keeping image frames in a sequence in the order they were acquired and cropping image data to a whole number of cardiac cycles (to apply the synchronization algorithm), data from image sequences (spanning approximately five cardiac cycles) are pooled into one normalized cardiac cycle. Third, to accurately recover the dynamic motion of the heart, we provide a procedure to estimate and adjust phase lags between adjacent image sequences introduced by peristaltic-like contractions of the heart wall. This later step may not be necessary, however, if imaging is performed at nontransversal planes of the heart (such as longitudinal planes). In Secs. 2.2.1, 2.2.2, 2.2.3, 2.2.4, 2.2.5, we describe each step of the synchronization procedure.

Fig. 2

Illustration of M-mode image extraction from a B-mode image sequence: (a) B-mode image sequence and (b) M-mode image extracted along a line $(x=x_k)$ from (a).

2.2.4.

Estimation and adjustment of phase lag for all image sequences

During early developmental stages, contractile cardiac waves travel from the heart atrium to the OFT,⁴⁰ producing a peristaltic-like motion of the heart wall and introducing phase lags among different regions of the heart. To estimate these phase lags $\left(p_{\text{lag}}\right)$ between adjacent image sequences, we use data from the acquired image sequence showing the longitudinal section of the heart. Two M-mode images are extracted at two locations a distance $d$ apart along the longitudinal cardiac section (see Fig. 3 ). The phases of maximal cardiac contraction, $p_1$ and $p_2$ , are then identified from the M-mode images, and the phase lag, $\Delta p$ , between these two locations is calculated. To minimize errors in the estimation of $\Delta p$ , we average $\Delta p$ over several cycles, i.e.,

Eq. 7

$\Delta p=\frac{1}{q}\sum _{i=1}^q(p_1^i-p_2^i),$

where $i$ represents the $i$ ’th cardiac cycle and $q$ is the number of sampled cardiac cycles. Assuming that the velocity of the contractile wave, $v$ , is constant in the cardiac region under consideration,

Eq. 8

$v=d∕\Delta p.$

Fig. 3

Illustration of how phase lags in OFT wall motion are estimated: (a) OCT image of a longitudinal section of the OFT and (b) M-mode images extracted from the longitudinal section at the locations I (close to the OFT inlet) and II (close to the OFT outlet) in (a). Note that M-mode images were magnified and cropped for better visualization. $d$ is the distance between locations I and II; $p_1$ and $p_2$ are the phases of maximal OFT wall contraction identified from the M-mode images extracted along lines I and II, respectively.

The phase lag between two adjacent image sequences a distance $h$ apart is then estimated by

Eq. 9

$p_{\text{lag}}=h∕v.$

The adjusted absolute time shifts, ${\mathrm{S}}_i^{\prime }$ , are then calculated by

Eq. 10

$S_i^{\prime }=S_i+(i-1)p_{\text{lag}}.$

The assumption of constant $v$ holds approximately true in relatively small regions of the heart (such as the OFT), and hence to achieve accurate reconstruction, phase lags should be evaluated at each individual region.

3.1.

Embryo Preparation

Fertilized white leghorn eggs were incubated blunt-end up at $38°\mathrm{C}$ and 85% humidity in a horizontal rotation incubator to stage HH18.³² To expose the embryonic heart, the egg shell was opened and the membrane overlaying the embryo was removed. During egg manipulation and imaging, temperature of the embryo was allowed to drop below its normal physiological temperature $(\sim 38°\mathrm{C})$ , which allowed the heart rate to drop.

3.2.

Image Acquisition with OCT

A spectral domain OCT system was used to acquire images of the OFT of a chick embryo at HH18. OCT acquired 40 B-scans (2-D image frames, see Fig. 1) per second; with each B-scan composed of 250 A-scans (line scans). The OCT system employed has $\sim 10\mu \mathrm{m}$ axial ( $Z$ in Fig. 1) and $\sim 16\mu \mathrm{m}$ lateral ( $X$ and $Y$ in Fig. 1) spatial resolutions and a light penetration depth of $\sim 2.0\mathrm{mm}$ in tissue (assuming a refractive index of 1.35). Because at the early developmental stages under study the chick tissue is almost transparent, light penetration depth is further limited only by blood, such that the practical penetration depth when imaging the OFT is $\sim 1\mathrm{mm}$ (enough to image the OFT at HH18).

We used the nongated image acquisition strategy described in Sec. 2.1 to obtain a 4-D image data set of the OFT. A total of 65 B-mode image sequences of OFT cross sections were taken along the OFT, $12.5\mu \mathrm{m}$ apart, spanning the entire OFT region ( $\sim 800\mu \mathrm{m}$ in length). Each image was $250\times 430\text{pixels}$ , and each image sequence consisted of 185 frames (more than five cardiac cycles). A B-mode image sequence, corresponding to a longitudinal OFT section approximately perpendicular to the center cross section of the OFT, was also acquired to adjust phase lags due to peristaltic-like wall motion in the OFT.

Figures 4a, 4b, 4c, 4d show representative OCT images of the OFT. The structure of the OFT wall—the lumen interface, cardiac jelly, and myocardium—is clearly demarcated. We extracted an M-mode image from the dotted line in Fig. 4a. The M-mode image [Fig. 4e] traces the periodic displacements of the OFT wall over the acquired cardiac cycles.

Fig. 4

Representative OCT images (in B and M modes) of the heart OFT from an HH18 chick embryo. Images show the OFT when its walls are constricted: (a) cross section of the OFT at about its center and (b) longitudinal section approximately perpendicular to the cross section in (a); and when OFT walls are expanded: (c) center cross section and (d) longitudinal section. In (c) and (d), the dark areas in the lumen are due to a transient fading of signals when image acquisition rate is lower than the speed of blood flow. (e) An M-mode image generated from the cross-sectional image sequence along the dotted line in (a). M, myocardium; C, cardiac jelly; L, lumen. Scale $\text{bar}=100\mu \mathrm{m}$ .

3.3.

Testing the Synchronization Procedure

Using the OFT images acquired with OCT, we tested the accuracy of the synchronization procedure and the sensitivity of the procedure to the line chosen to extract M-mode images. We performed most of the tests on a representative image sequence of the OFT acquired approximately at the center cross section of the OFT [see Figs. 4a and 4c].

3.3.1.

Determination of cardiac period

We used the SLM algorithm, applied to M-mode images, to determine the cardiac period, $T$ , of the chick embryonic heart from each acquired image sequence (see Sec. 2.2.1). Because during imaging embryo temperature was lower than physiological temperature (see Sec. 3.1), calculated $T$ ( $\sim 0.8\mathrm{s}$ , see below) was higher than normal (usually $<0.5\mathrm{s}$ for an HH18 embryo⁸). To quantify $T$ we used Eq. 3, with a search range of cardiac periods from $0.7\text{to}0.9\mathrm{s}$ and searching step size of ${10}^{-4}\mathrm{s}$ .

Accuracy

To study the accuracy of the SLM algorithm applied to M mode, we compared cardiac periods calculated using SLM applied to M- and B-mode images, as well as calculated using fast Fourier transform⁴¹ (FFT) (FFT was performed on a curve showing the correlation of the images in a sequence with respect to a reference image). To this end, we used the representative OCT image sequence of the OFT (Sec. 3.3). The cardiac period calculated using FFT $(T=0.7708\mathrm{s})$ was not expected to be accurate due to the small data sampling size (only approximately five cycles). The SLM algorithm, in contrast, was accurate and predicted approximately the same solution $(T=0.8146\mathrm{s})$ regardless of whether M- or B-mode images were used (the difference was less than the searching step). SLM applied to M-mode images, however, was $>200$ times faster than SLM applied to B-mode images.

To verify the accuracy of the calculated $T$ , we pooled all the images in the sequence into one cardiac cycle, arranging them by phase according to the calculated $T$ . From these pooled sequences, we generated M-mode images along the dotted line shown in Fig. 4a. Figure 5 shows a comparison of M-mode images extracted from an acquired image sequence [Fig. 5a] and from pooled images [Figs. 5b and 5c]. The M-mode image from the acquired image sequence [Fig. 5a] shows a pixelated view due to the low image acquisition rate $\left(40\mathrm{fps}\right)$ . The M-mode image obtained from pooled images arranged according to the $T$ calculated from FFT [Fig. 5c] shows that if $T$ is inaccurate, the M-mode image looks discontinuous. The M-mode image obtained from pooled images arranged according to the $T$ calculated using the SLM [Fig. 5b] is smooth looking and resembles Fig. 5a, indicating that the $T$ calculated using SLM is accurate.

Fig. 5

M-mode images extracted from cross-sectional image sequences of the OFT along the dotted line in Fig. 4a. Shown M-mode images were extracted from (a) the acquired OCT image sequence, (b) the same OCT image sequence pooled to one cycle using the cardiac period calculated by SLM $(T=0.8146\mathrm{s})$ , and (c) the same sequence pooled to one cycle using the cardiac period calculated by FFT $(T=0.7708\mathrm{s})$ . Note that (a) corresponds to one cardiac cycle of Fig. 4e.

Sensitivity to M-mode line

To test the sensitivity of the SLM algorithm to the line selected to extract the M-mode image from the 2-D image sequences, we extracted M-mode images along horizontal and vertical lines (lines were $\sim 130\text{-}\mu \mathrm{m}$ apart, see Fig. 6 ) from the representative OCT image sequence. Lines were grouped according to the following criteria: (i) lines that overlay the OFT region over the cardiac cycle (H3–H5 and V3–V5); (ii) lines that overlay the OFT region only for a time interval during the cardiac cycle (V2, H2, V6, and H6); and (iii) lines that lie outside the OFT region (H1, V1, H7, and V7). We calculated $T$ using the SLM algorithm applied to these M-mode images (see Table 1 ).

Fig. 6

Lines chosen to perform sensitivity study in determining cardiac periods using SLM algorithm applied to M-mode (from the representative sequence shown in Fig. 4). The panels show the positions of the lines when the OFT walls were most (a) constricted and (b) expanded.

Table 1

Cardiac periods (in seconds) calculated from M-mode images extracted from different lines (see Fig. 6).

	1	2	3	4	5	6	7
H	0.8180	0.8146	0.8146	0.8146	0.8146	0.8206	0.8146
V	0.8206	0.8146	0.8146	0.8153	0.8146	0.8146	0.8125

Bold numbers show calculated periods that are within 0.01% accuracy with respect to the centerline [shown in Fig. 4a] used to determine the accuracy of SLM.

We found that SLM was, in general, robust, as long as the lines from which M-mode images were extracted overlaid the OFT during the cardiac cycle (i.e., H3–H5 and V3–V5); the maximum deviation from $T$ occurred at V4 and was within 0.1%. The deviation at V4, however, was mainly due to transient fading of signals during imaging (wash-out phenomenon),⁴² which occurs when blood flow velocity is much faster than image acquisition rates, resulting in interference signals that average out and appear as a dark region in the lumen [see Figs. 4c and 4d]. Better reproducibility of results was achieved when SLM was applied to M-mode images extracted from horizontal rather than vertical lines. The inferior performance of the M-mode images from vertical lines was due to both attenuation of signals along the tissue depth and transient fading of signals.

3.3.2.

Determination of relative phase shift

To calculate phase shifts, $S_{i,i}{}_{+1}$ , between adjacent image sequences, we used a similarity algorithm that searched for $S_{i,i}{}_{+1}$ by maximizing the correlation between M-mode images [Eq. 5]. Because each image sequence consisted of 185 frames, $S_{i,i}{}_{+1}$ was expected to be at least accurate within one frame in a pooled sequence (or $\sim 0.005T$ ).

Accuracy

To study the accuracy of the similarity algorithm applied to M-mode images, we first tested the algorithm using synthetically generated image sequences with known relative phase shifts. To obtain the image sequences, we chose the representative image sequence [see Fig. 4a and 4c] and, using linear interpolation between frames, we then generated 10 image sequences that were shifted in phase from the representative sequence. Phase shifts were randomly chosen from 0 to $T$ . Using these sequences, we compared the performance of the similarity algorithms applied to B- and M-mode images [extracted along the line shown in Fig. 4a] in terms of accuracy and computational efficiency.

We found small errors—with respect to known shifts (maximum $\sim 0.3\%$ )—when phase shifts were calculated using similarity on either B- or M-mode images. Calculation of the phase shift using M-mode images, however, was $\sim 400$ times faster than when using B-mode images.

Sensitivity to M-mode line

To study the sensitivity of calculated phase shifts to the line selected to extract the M-mode images, we used the representative image sequence [see Fig. 4a and 4c] together with its adjacent image sequence, and applied the similarity algorithm to M-mode images extracted from the two image sequences. According to Sec. 3.3.1, $T$ was more accurate when lines that overlaid the OFT over the cardiac cycle were used to extract M-mode images; therefore, we focused on M-mode images extracted from these lines. We generated 16 M-mode images extracted from horizontal and vertical lines ( $50\text{-}\mu \mathrm{m}$ apart, see Fig. 7 ), and found the phase shift between the sequences by applying similarity, Eq. 5, to these M-mode images. Calculated phase shifts, $S_{i,i}{}_{+1}=(0.25\pm 0.005)T$ , for all M-mode lines were within the expected accuracy.

Fig. 7

Lines chosen to perform sensitivity study in determining relative phase shifts using similarity algorithms applied to M-mode images (from the representative sequence shown in Fig. 4). The panels show the positions of the lines when the OFT walls were most (a) constricted and (b) expanded.

3.3.3.

Reconstruction of 4-D images of the OFT

To reconstruct 4-D images of the OFT, we used the 4-D data set obtained as described in Sec. 3.2. For each OFT cross-sectional image sequence, we generated an M-mode image from a horizontal line within the center region of the OFT cross section (e.g., line H4 in Fig. 6). Then, using the SLM algorithm [Eqs. 2, 3], we determined $T$ for each image sequence. Once $T$ was determined, we used similarity [Eqs. 4, 5], to determine the relative phase shift between each adjacent image sequence pair. Finally, we estimated the phase lag among image sequences due to the peristaltic-like motion of the OFT wall [Eqs. 8, 9] from the acquired sequence of OFT longitudinal section images. This phase lag was found to be $\sim 0.32T$ from the inlet to the outlet of the OFT, and thus was non-negligible. After calculating absolute phase shifts with respect to a reference sequence [Eq. 6], we corrected for this phase lag [Eq. 10], and then synchronized image sequences according to the adjusted absolute phase shifts. We then reassembled the 2-D images of the OFT into 3-D image data sets at 180 phases of the cardiac cycle, and thus reconstructed 4-D images of the OFT. To better visualize the structure and motion of the OFT walls and their interaction with blood flow over the cardiac cycle, 4-D images were further analyzed using the image software Amira 3.1 (see Videos 1 ).

Video 1

Voxel view of a beating chick-embryo OFT. The movie shows a frontal view of the OFT for the first cardiac cycle and then a $180\text{-}\mathrm{deg}$ rotation of the OFT along the $Z$ -axis for the second cardiac cycle. The dotted line (in the still image) encircles the OFT region that connects the ventricle proximally and aortic sac distally. M: myocardium, L: lumen (Quick-Time, 2 MB).

10.1117/1.3184462.1

Accuracy

To test the accuracy of the 4-D reconstruction, we extracted image sequences showing an OFT longitudinal section over the cardiac cycle from the reconstructed 4-D images with and without phase-lag adjustment. By visual inspection, the plane at which OFT longitudinal sections were extracted from reconstructed images was close to the plane where the OFT longitudinal image sequence was acquired directly with OCT. We then extracted M-mode images from three lines of the reconstructed and imaged OFT longitudinal sections (see Fig. 8 ): (i) line I, which was close to the ventricle (the OFT inlet); (ii) line M, approximately at the middle of the OFT; and (iii) line O, which was close to the aortic sac (the OFT outlet). The extracted M-mode images showed very similar wall-motion patterns. To better visualize differences in wall-motion phase relations, we manually traced the upper interface between the myocardium and the cardiac jelly from the M-mode images and plotted the displacement of this interface over time (see Fig. 9 ). We found that when the OFT is reconstructed without phase-lag adjustment, phase relations between lines I and O— $(0.02\pm 0.02)T$ —did not resemble those observed from direct OCT imaging— $(0.11\pm 0.02)T$ . When phase-lag adjustment was introduced in the reconstruction, however, the phase relations between lines I and O were recovered. Thus, our results indicate that phase-lag adjustment is needed to properly capture the dynamics of the OFT wall motion.

Fig. 8

Comparison of M-mode images extracted from imaged and reconstructed longitudinal sections of the OFT. Lines selected to extract M-mode images are show in (a) and (b): close to the OFT inlet (I), middle OFT (M), and close to the outlet (O). (a) Longitudinal sections acquired directly with OCT and (b) reconstructed from synchronized 4-D image data. (c–e) M-mode images from a sequence acquired directly from OCT; (f–h) M-mode images from a reconstructed image sequence without phase lag adjustment; (i–k) M-mode images from the reconstructed image sequence with phase lag adjustment.

Fig. 9

OFT wall displacements close to the inlet and outlet. Wall displacements (showing the motion of the interface between the myocardium and cardiac jelly) were traced manually from the M-mode images shown in Fig. 8: (a) from images acquired directly with OCT [Figs. 8c and 8e]; (b) from reconstructed images without phase adjustment [Figs. 8f and 8h]; and (c) from reconstructed images with phase adjustment [Figs. 8i and 8k]. For ease of visualization, we present five cardiac cycles extracted directly from the acquired image sequence (a), and in (b) and (c) generated by circular repetition of the reconstructed pooled cardiac cycle.

4.1.

Application Realm of the Synchronization Procedure

Application of our procedure to 4-D image reconstruction is subjected to two underlying assumptions. The first assumption is the periodicity of cardiac motion and deformation. For chick embryos at early developmental stages, the central neural system, which regulates heart rate, has not yet developed and the heart rate is only a function of embryo temperature. Although during our imaging acquisition the temperature of the embryo was lower than physiological, temperature was stable within $1°\mathrm{C}$ and temperature-induced variations in the heart rate were small ( $<9\%$ among image sequences). Thus, during imaging, motion of the OFT wall was approximately periodic. The second assumption is structural similarity of image sequences acquired at adjacent locations. To ensure structural similarity, the distance $h$ $\left(12.5\mu \mathrm{m}\right)$ between acquired cross-sectional image sequences was less than the lateral spatial resolution of our OCT system $\left(16\mu \mathrm{m}\right)$ , and much smaller than the length of the OFT $(\sim 800\mu \mathrm{m})$ . Our nongated 4-D imaging strategy and synchronization procedure can be generalized to applications involving other animal models or organ systems, as well as other imaging modalities.

4.2.

Accuracy of the Synchronization Procedure

The accuracy of our synchronization procedure depends on the line chosen to extract M-mode images from B-mode images. We found that calculations of period and relative phase shift from M-mode images are not too sensitive to the locations or orientations of the lines as long as the lines overlaid the region of interest (e.g., the OFT) over the entire cardiac cycle. For OCT images, however, attenuation of the imaging signal along the tissue depth and the transient fading of signal due to fast motion of blood flow affect the accuracy of calculations. Therefore, lines containing points within the same depth in tissue (horizontal lines in Figs. 6 and 7) and within the center region of the OFT are preferred.

OCT images of the OFT (B and M modes) contain periodic data from the OFT (our region of interest), but also data from other parts of the heart and surrounding organs, such as the head. To estimate the percentage of the image that contains OFT (useful) data, we can use the average surface of the image occupied by the OFT over total surface area for B-mode images, and the average OFT length over total line length for M-mode images. For cross-sectional images, we can assume that the OFT external layer is a circle with radius $R$ , and that the image is a square with side length $L$ (with $R<L$ ). Then, the ratio of OFT data to total imaging data goes as ${(R∕L)}^2$ for 2-D images and $R∕L$ for line scans. Because $R∕L>{(R∕L)}^2$ , using properly chosen lines for M-mode extraction, the algorithms to calculate period and phase shift could achieve higher accuracy when applied to M-mode rather than B-mode images.

We compared the performance of the algorithms used to calculate cardiac period and relative phase shifts applied to B- and M-mode images obtained from a horizontal line within the center region of the OFT (see Figs. 6 and 7). We found that, regardless of whether the algorithms were applied to B- or M-mode images, similar accuracy was achieved (see Secs. 3.3.1, 3.3.2). Moreover, calculations were significantly faster ( $\sim 400$ times faster) when performed using M-mode rather than B-mode images. Employing M-mode images dramatically improved the efficiency of the synchronization procedure without compromising accuracy.

Other factors that affect accuracy of our synchronization procedure are the number of frames per cardiac cycle that the system can acquire and the total number of cardiac cycles acquired in a sequence. Our reconstruction is based on $\sim 30$ frames per cycle and $\sim 5$ cardiac cycles, thus about 150 frames in total to accurately reconstruct a cardiac cycle (as all the images are pooled to one cycle). We verified that this number of frames is sufficient for correct reconstruction. We also checked the accuracy of the reconstruction if more or less frames were considered (results not shown in the manuscript) and our results suggest that 150 frames are probably close to optimal. Thus, for other systems and faster cardiac rates, image acquisition should be such that at least 150 frames are acquired. A caveat, however, is that if the number of frames acquired per cardiac cycle is $⪡30$ , then each image frame might not be considered “instantaneous” (because it takes time for the system to scan the frame) and this would introduce inaccuracies. Our results indicate that acquiring OCT images at a rate of $\sim 30\text{frames}∕\text{cardiac}$ cycle for about five cardiac cycles is sufficient to predict cardiac cycle and time shifts with reasonable accuracy.

4.3.

4-D Reconstruction (Phase-Lag Correction)

We found that synchronization of images based on structural similarity alone is not sufficient to capture the peristaltic-like motion of the heart walls of chick embryos at early developmental stages. During early development, the embryonic heart does not have valves and, to maintain unidirectional blood flow, contractile waves travel along the heart.⁴⁰ These traveling waves produce peristaltic-like wall motion and thus introduce phase lags between different regions of the heart. In our synchronization procedure, to correctly recover the dynamics of the OFT wall, we adjusted the phases of image sequences from those obtained by structural similarity.

Because contractile waves travel from the inlet to the outlet of the heart, the need to correct for the phase lag in our procedure largely depends on our choice of imaging cross-sectional (transverse) planes. For our procedure, we acquired cross-sectional images because they ensure similarity between adjacent image sequences (i.e., the OFT always shows in the image frame, no matter whether the OFT is expanded or contracted), whereas with longitudinal images, there are imaging planes from which the OFT “disappears” when its walls contract and “reappears” when its walls expand, making similarity algorithms more difficult to implement and more prone to inaccuracies.

Currently, there is controversy about the nature of cardiac motion at early stages of development, with some authors arguing that heart wall motion is peristaltic,^{43, 44} whereas others argue that the heart acts as a suction pump.⁴⁵ We certainly found that in reconstructing the OFT motion from the nongated OCT images we acquired, phase-lags in wall motion between the inlet and outlet of the OFT (not accounted for by structural similarity) need to be considered. In other regions of the heart, and/or at later stages, this phase lag might be negligible. Application of our synchronization procedure, however, will certainly aid in the characterization of cardiac mechanics during early development.