Application of speckle analysis techniques in OCT for optical diagnosis in vivo

• Grant number: 16/17342-0
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate
• Start date: May 01, 2017
• End date: April 30, 2018
• Field of knowledge: Engineering - Biomedical Engineering
• Principal Investigator: Anderson Zanardi de Freitas
• Grantee: Lucas Ramos de Pretto
• Supervisor: James G. Fujimoto
• Host Institution: Instituto de Pesquisas Energéticas e Nucleares (IPEN). Secretaria de Desenvolvimento Econômico (São Paulo - Estado). São Paulo , SP, Brazil
• Institution abroad: Massachusetts Institute of Technology (MIT), United States
• Associated to the scholarship: 15/15775-3 - Optical Coherence Tomography signal autocorrelation analysis algorithm applied to blood flow monitoring in mice with Metabolic Syndrome, BP.DR

Abstract

Optical Coherent Tomography (OCT), as a non invasive and high resolution technique has wide biological application as a diagnostic tool, especially in ophthalmology. Recently, OCT adaptations for flowmetry using speckle have been reported in the literature, and our group at IPEN has developed a novel approach to this end. The group at Massachusetts Institute of Technology (MIT) has also studied speckle with their own algorithm, applied to Exudative Age-Related Macular Degeneration (AMD), a disease that causes severe vision loss. This study intends to extend the knowledge in both groups, by comparing and applying different speckle analysis techniques to AMD, establishing the OCT as a diagnostic tool for this scenario. However, as speckle analysis usually are time-consuming tasks, we further propose the use of machine learning algorithms, such as Support Vector Machines, as flowmetry techniques in OCT. To enable this, a study on the characteristics of the OCT's speckle needs to be conducted with a system with high acquisition rate (because the speckle pattern is rapidly varying in time), as the one available at the MIT laboratory. With such a study, the appropriate features will be selected for the machine learning algorithms. After validation, all three techniques will be applied to AMD, and their results compared, originating a novel study.

Scientific publications

(4)

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

ALIBHAI, A. YASIN; DE PRETTO, LUCAS R.; MOULT, ERIC M.; OR, CHRIS; ARYA, MALVIKA; MCGOWAN, MITCHELL; CARRASCO-ZEVALLOS, OSCAR; LEE, BYUNGKUN; CHEN, SIYU; BAUMAL, CAROLINE R.; et al. QUANTIFICATION OF RETINAL CAPILLARY NONPERFUSION IN DIABETICS USING WIDE-FIELD OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY. RETINA-THE JOURNAL OF RETINAL AND VITREOUS DISEASES, v. 40, n. 3, p. 412-420, MAR 2020. (16/17342-0, 15/15775-3)

DE PRETTO, LUCAS R.; MOULT, ERIC M.; ALIBHAI, A. YASIN; CARRASCO-ZEVALLOS, OSCAR M.; CHEN, SIYU; LEE, BYUNGKUN; WITKIN, ANDRE J.; BAUMAL, CAROLINE R.; REICHEL, ELIAS; DE FREITAS, ANDERSON ZANARDI; et al. Controlling for Artifacts in Widefield Optical Coherence Tomography Angiography Measurements of Non-Perfusion Area. SCIENTIFIC REPORTS, v. 9, JUN 24 2019. (16/17342-0, 15/15775-3)

ALIBHAI, A. YASIN; DE PRETTO, LUCAS R.; MOULT, ERIC; SCHOTTENHAMML, JULIA; OR, CHRIS; ARYA, MALVIKA; MCGOWAN, MITCHELL; BAUMAL, CAROLINE R.; WITKIN, ANDRE J.; DUKER, JAY S.; et al. Quantitative Analysis of Capillary Non-Perfusion in Diabetic Retinopathy using Widefield OCT-Angiography. INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, v. 59, n. 9, p. 7-pg., 2018-07-01. (16/17342-0)

ISHIBAZAWA, AKIHIRO; DE PRETTO, LUCAS R.; ALIBHAI, A. YASIN; MOULT, ERIC M.; ARYA, MALVIKA; SOROUR, OSAMA; MEHTA, NIHAAL; BAUMAL, CAROLINE R.; WITKIN, ANDRE J.; YOSHIDA, AKITOSHI; et al. Retinal Nonperfusion Relationship to Arteries or Veins Observed on Widefield Optical Coherence Tomography Angiography in Diabetic Retinopathy. INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, v. 60, n. 13, p. 4310-4318, OCT 2019. (16/17342-0)