Alessandro Calore
Design and Implementation of an Automated Python Pipeline for the Analysis of Tissue Dynamics using Dynamic OCT.
Rel. Kristen Mariko Meiburger, Mengyang Liu, Giulia Rotunno, Adam Apro. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Biomedica, 2026
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Abstract
Optical Coherence Tomography (OCT) is a non-invasive imaging technique based on low-coherence interferometry. The device reconstructs the internal structures of biological tissues achieving micrometric resolution. Over the years, new functional applications have been developed, most notably Optical Coherence Tomography Angiography (OCTA). This technique is designed to map vascular networks without contrast agents by detecting rapid signal decorrelation specifically caused by intravascular blood flow. However, OCTA stays limited in capturing slower, non-vascular dynamics. To overcome this limitation, Dynamic OCT (D-OCT) emerges as a label-free technique that analyzes time-dependent signal fluctuations, adding a temporal dimension to the spatial data to uncover a wider range of biological motility.
This thesis presents the development and implementation of an automated Python pipeline for the extraction and quantification of such motility through the analysis of single locations or volumes over time by using D-OCT
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