Optimal tomography of dynamically evolving objects using machine learning algorithms

Mahshad Javidan; Hadi Esfandi; Ramin Pashaie

doi:10.1117/12.2650861

7 March 2023 Optimal tomography of dynamically evolving objects using machine learning algorithms

Mahshad Javidan, Hadi Esfandi, Ramin Pashaie

Proceedings Volume 12376, Optical Tomography and Spectroscopy of Tissue XV; 123760C (2023) https://doi.org/10.1117/12.2650861
Event: SPIE BiOS, 2023, San Francisco, California, United States

Abstract

In this article we introduce a systematic approach for optimal scanning of dynamically evolving objects, including cases where the dynamics is unknown. The method is specifically designed to optimize each measurement and engineer illumination patterns with the goal of reducing the uncertainty left in our estimation of the sample. Concurrently, the algorithm uses system identification techniques to develop a mathematical model for the dynamics under test based on the acquired data and it uses the model to predict changes in the distribution and optimize upcoming measurements. The theory is developed and simulations are provided to better display discussed concepts.

Conference Presentation

Citation Download Citation

Mahshad Javidan, Hadi Esfandi, and Ramin Pashaie "Optimal tomography of dynamically evolving objects using machine learning algorithms", Proc. SPIE 12376, Optical Tomography and Spectroscopy of Tissue XV, 123760C (7 March 2023); https://doi.org/10.1117/12.2650861

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