## 4 Discussion  
We proposed a novel and easy-to-deploy wED that applies a moving window to the repeated scanned data. Compared to other popular OCTA reconstruction algorithms (SV, aED, bED), wED was less sensitive to motion artifacts and could better reduce background noise intensity. To have wED at its best performance, the optimization of the moving window size played a vital role. The optimization result in Figure 3 indicates that the correlation coefficient between the raw data and the fitted normal distribution decreased as the axial window size increased over 65 pixels, though it also declined when the window size was smaller than 65 pixels. Therefore, a large depth range can affect the data distribution. However, the wED still requires static signals to be the dominant component. As a result, an extremely small window size would not be suitable either. Hence, the optimized window size was 65 pixels indicated in Figure 3, where the average normal-distribution correlation coefficient was 0.94. Figure 4 shows the direct comparison of the cross-sectional B-frame. The arrows show how high-density blood vessels were resolved by different algorithms. Due to the high motion effects within the raw data, SV and bED could barely resolve any blood-flow information. Although both aED and wED could resolve the blood vessels, wED contains 18% less background noise according to the background noise intensity calculations. In Figure 5, the en face and quantitative comparisons among four-time registration, SV, aED, bED, and wED are shown. With the four-time registration result as the ground truth, the PSNR was calculated for each algorithm to be evaluated (shown in Figure 5). The proposed wED had the highest PSNR value, 12.01, while the second highest is from aED, 11.48, which indicates the proposed approach wED has improved the reconstruction performance by 4.6%. Note that sometimes the aED results could have a higher PSNR when wED results were visually better, because the four-time registration result was not a perfect ground truth. Due to its long acquisition time (four times of a normal OCTA scan), the four-time registration result could have transformations (moving, translation, rigid, affine, and B-spline) of the vessel structure. As a result, the aED with higher background intensity could have a higher PSNR value instead. Visual judgment in Figure 5 could also match the quantitative results. To demonstrate the detailed visual difference among these algorithms, three areas in all en face results were enlarged as green, yellow, and red regions. SV could hardly resolve any blood-flow signals due to high motion effects. Although bED had some blood signals underneath the horizontal motion artifacts, these artifacts were too many to present any vessel networks. Matching with the B-scan comparison, only aED and wED were able to resolve most of the blood-flow signals. For the green region, wED could still generate less background noise, which provided better contrast of the blood vessels. Within the yellow region, a breathing motion artifact occurred. The bright horizontal lines could be observed in the aED result. In the wED result, while the breathing artifact did not disappear, it was reduced compared to the aED. In the red region, wED had better contrast of small vessels compared to aED. In addition, some motion artifacts still existed in the aED result, while they could hardly be seen from wED. Although our approach of wED can outperform other algorithms, the tradeoff is the computation cost, which was measured as the processing time. To measure the processing time, the aED and wED algorithms were run in MATLAB on a Windows 10 computer using an i9-10980 XE (3.00 GHz) processor with RAM of 128 GB. In total, 20 datasets of 340 \(\times\) 600 \(\times\) 600 \(\times\) 4 (z-x-y-N) were processed, whose processing time was averaged.