Adaptive scanning OCT with scene prediction and dynamics-aware scans

Dhyey Manish Rajani; Federico Seghizzi; Yang-Lun Lai; Koerner Gray Buchta; Mark Draelos

doi:10.1117/12.3005458

13 March 2024 Adaptive scanning OCT with scene prediction and dynamics-aware scans

Dhyey Manish Rajani, Federico Seghizzi, Yang-Lun Lai, Koerner Gray Buchta, Mark Draelos

Proceedings Volume PC12830, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVIII; PC128302O (2024) https://doi.org/10.1117/12.3005458
Event: SPIE BiOS, 2024, San Francisco, California, United States

Abstract

Conventional Optical Coherence Tomography (OCT) suffers from the frame-rate/resolution tradeoff, whereby increasing image resolution leads to decreases in the maximum achievable frame rate. We extended the conventional probabilistic adaptive scanning technique that overcomes this tradeoff with machine-learning-based scene prediction and kinodynamic path planning based on the Clustered Traveling Salesperson Problem. In online imaging, we found that our new technique produces an equivalent frame rate speed-up as previously reported while creating higher quality output OCT images. These results generalized across scenes of varying types, including those of intrasurgical relevance.

Conference Presentation

Citation Download Citation

Dhyey Manish Rajani, Federico Seghizzi, Yang-Lun Lai, Koerner Gray Buchta, and Mark Draelos "Adaptive scanning OCT with scene prediction and dynamics-aware scans", Proc. SPIE PC12830, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVIII, PC128302O (13 March 2024); https://doi.org/10.1117/12.3005458

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available