## 3 Results  
### HSV for Multimodal Imaging: Degradation of Aortic Tissue  
To test the combination of optical properties obtained from multiple imaging systems, we used one control and one pathological aorta sample, from which we derived the \(\mu_{t}\) from the OCT measurements and the (adimensional, normalized) amount of elastin (\(c_{e}\)) as a clinical marker from the HSI-measured reflectances, with the methodology explained in Section 2. We obtained boxplots of \(c_{e}\) and \(\mu_{t}\) for both samples (Figure 1a) to visualize their distributions and overlap. All distributions were different between the healthy and the pathological sample, including some chromophores not considered for representation (collagen and water). Note that the HSV space still needs a third independent magnitude to be completely defined. Initially, we took the profilometry \(p\) of the samples as obtained with OCT imaging. The final magnitude-to-HSV encoding was \(c_{e}\to H\), \(p\to S\) and \(\mu_{t}\to V\).  
The selected optical properties were calculated for all ROIs of our samples, yielding the images shown in Figure 1b-d. The elastin maps obtained with HSI were up-scaled to the size of the \(\mu_{t}\) maps via linear interpolation for the posterior matching of the H, S and V dimensions. The OCT-based maps (c) and (d) were represented as en face maps to match the visualizations given by our HSI system.  
Figure 1e shows the colorimetric combination of the optical properties presented in the same figure for the pathological aorta. Given that we are now working with multiple parameters, the legend for the image should have as many dimensions as the parameters. However, we show the S = 1 plane in the portrayed legend (Figure 1f) since, at this scale, the samples are mostly flat and the profiles \(p\) have little to no effect on the final images. The difference in data structure between systems is clearly visible, especially between the \(c_{e}\) and \(\mu_{t}\) maps.  
To explore the opportunities that this visualization method brings, we combined the HSI-derived elastin and OCT-derived \(\mu_{t}\) in the maps shown in Figure 2 for both aortas. The color legend is the same one that was introduced in Figure 1. Color differences are immediately recognized, as well as structural variations upon closer inspection of the samples. By assigning the characteristics of the control samples to the most vibrant green (low \(c_{e}\)) and the pathological ones to the darkest red (high \(c_{e}\)), healthy regions are immediately recognized both in the control sample (Figure 2a,b) and in the pathological one (Figure 2c,d). Specifically, some heavily damaged aortic regions, marked by the red colors of \(c_{e}\), are visible in the rightmost column of Figure 2d, where the darker spots indicate the reduction in \(\mu_{t}\). Other parts of the mosaic combine some dark signatures given by \(\mu_{t}\) with cyan colors, indicating that there are structural changes that are not yet visible as a significant variation in the elastin content.  
Figure 1: HSV combination of the optical properties. The clinically relevant ones are portrayed as boxplots (**a**) for the control (green, C) and pathological (red, P) aorta samples. Regular colormap images of \(c_{e}\) (**b**), \(p\) (**c**) and \(\mu_{t}\) (**d**) are shown for the pathological aorta, as well as its HSV composition (**e**). The color legend should be three-dimensional, but, since samples at this scale were flat, only the legend for S = 1 is shown (**f**). There is no relevant image change for the different profilometry values for this dataset.