## 2 Materials and Methods  
### Datasets  
The proposed method can be applied to any combination of two or three different magnitudes that have complementary information about the data. However, since the focus of our research lies in the optical properties of biological tissues, we recovered two datasets to test the applicability of the proposed visualization methodology in two case studies: one composed of human samples of aortic tissue and another of dystrophic mice muscles. For the aorta dataset, we used two ex vivo samples of the human ascending thoracic aorta: a healthy one as a control (AR26, healthy transplant donor) and a pathological aneurysmic one (AR21, with presence of atheroma plaques). Aortas were measured with optical coherence tomography (OCT) to evaluate their structures, and their composition was studied with hyperspectral imaging (HSI). From the OCT measurements, the overall attenuation coefficient (\(\mu_{t}\)) was derived by comparing its response to that of a reference material, as proposed by [PERSON] et al. [27]. On the other hand, the model proposed by [PERSON] et al. [28] was used to obtain an estimation for the elastin, water, and lipids, using the spectra measured by [PERSON] et al. [29, 30] as a reference. Specifically, we used the fraction of elastin \(c_{e}\) as a marker of an aortic aneurysm. The muscle dataset contained three dystrophic mice muscles of differently aged mice (control, 1-month-old dystrophic and 3-month-old dystrophic mice). The muscles were measured and treated as described in our previous work [31], where we obtained the \(\mu_{t}\) with the same technique as for the aortas, but also their birefringence (\(\Delta n\)) by measuring their polarization responses with polarization-sensitive OCT (PS-OCT). The method proposed to calculate the \(\Delta n\) was first described by [PERSON] et al. in [32]. Since both OCT modalities (regular and PS) are captured with the same device, \(\Delta n\) and \(\mu_{t}\) have the same spatial resolution and are implicitly co-registered. These optical properties, namely \(\mu_{t}\), \(\Delta n\) and \(c_{e}\), can be used to characterize the samples from the structural and chemical points of view. Specifically, \(\mu_{t}\) is a measurement of how quickly light decays inside the sample; therefore, smaller \(\mu_{t}\) values indicate that light can travel to further tissue depths without being completely attenuated. Given that \(\mu_{t}\) is dominated by scattering, its value can be an indicator of the cell size, and therefore of the tissue morphology, which might change with different tissue structures. Additionally, \(\Delta n\) is affected by tissue anisotropy, with highly anisotropic structures (e.g., collagen/fiber bundles) yielding higher values of \(\Delta n\). The number of tissue chromophores (collagen, elastin, fat), and specifically \(c_{e}\), at different points on the sample can complement the information given by \(\mu_{t}\) and \(\Delta n\), since many structural alterations are caused by the sub- or overexpression of some tissue components, such as elastin. Combining structural-/chemical-based information can be extremely useful for the understanding of multiple pathologies. The derivation of the attenuation coefficient \(\mu_{t}\) and the birefringence \(\Delta n\) and the chromophore modeling are beyond the scope of this work and have been thoroughly described in the literature [27, 28, 29, 30, 31, 32]. The measurement procedure, on the other hand, does depend on the type of sample and, specifically, on its size. Our OCT system has an FOV of 10 \(\times\) 10 mm\({}^{2}\), which makes the imaging of the entire aortas impossible in a single measurement. To maintain a spatial reference between measurements and instrumental systems, a 9-by-9 square grid with squares of 10 \(\times\) 110 mm\({}^{2}\) was designed and 3D-printed for the posterior mosaicking of the aorta measurements. We refer to each of the squares in the grid as a region of interest (ROI).