computed as the weighted mean of the data points on both sides as $$r(i)=\sum_{j=i-k}^{j=i+k} \frac{(k-j) \times r(j)}{2k+1}$$ where $\Delta \theta=2k + 1$ is the span of the moving average scheme, $i$ is each node of data and $r(i)$ is radial coordinate of each node in $(r,\theta)$ format (Fig. \[r-theta\]). Two general techniques are available for boundary point treatment when using moving averages methods: 1) the span is reduced progressively while reaching the boundary points in a way that the equation holds for all the data points; and 2) data augmentation is performed to generate a new dataset equivalent to the size of the span at the endpoints (boundary points). Our results show that reducing the span cannot eliminate oscillations near the endpoints of reconstructed LV. Thus data augmentation is employed by reflecting data (the number of data points to be mirrored is equal to the span size) around the boundary point (Fig. \[smoothing\]-a). Different span sizes are tested and the effects of each can be seen in Fig. \[smoothing\]. Furthermore, Variable Span Weighted Moving Average (VSWMA) is employed, which enables us to apply larger span value near the endpoints and a smaller span near the apex. The span of the 