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@@ -93,6 +93,12 @@ tl;dr RKHS make subsequent optimization learning easier to implement and more li
 [Ronald?] -- ~ 4 paragraphs summarize impact of retinal OCT (Glaucoma, occulomics, etc.),  background on VFMD, background on metadata, imaging physics. 
 Key recent deep learning papers [Schuman et al.]. Other learning approaches [NNMF et al.].
 
+-------
+
+Optical coherence tomography (OCT) is a non-invasive diagnostic imaging tool which employs principles of optical interferometry to obtain cross-sectional images from biological tissue \cite[]{huang1991}.
+
+-------
+
 Key observation -- inherent anisotropy of imaging implements Frangi-like plate filter along the high resolution axis.
 
 \subsection{Kolmogorov complexity and the normalized information distance (NID)}
diff --git a/manuscript/rsf.bib b/manuscript/rsf.bib
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@@ -250,3 +250,13 @@
    type = {Conference Proceedings}
 }
 
+@article{huang1991,
+  title={Optical coherence tomography},
+  author={Huang, David and Swanson, Eric A and Lin, Charles P and Schuman, Joel S and Stinson, William G and Chang, Warren and Hee, Michael R and Flotte, Thomas and Gregory, Kent and Puliafito, Carmen A},
+  journal={Science},
+  volume={254},
+  number={5035},
+  pages={1178--1181},
+  year={1991},
+  doi={10.1126/science.1957169}
+}