absO2luteU-Net: Tissue Oxygenation Calculation Using Photoacoustic Imaging and Convolutional Neural Networks

Authors

Kevin Hoffer-HawlikFollow
Geoffrey P. Luke, Dartmouth College

Degree Program

A.B.

Year of Graduation

2019

Sponsor Name, City, and State

Kaminsky Family Fund, Hanover, NH Neukom Institute, Hanover, NH

Faculty Advisor

Geoffrey P. Luke

Document Type

Thesis (Senior Honors)

Publication Date

2019

Abstract

Photoacoustic (PA) imaging uses incident light to generate ultrasound signals within tissues. Using PA imaging to accurately measure hemoglobin concentration and calculate oxygenation (sO₂) requires prior tissue knowledge and costly computational methods. However, this thesis shows that machine learning algorithms can accurately and quickly estimate sO₂. absO₂luteU-Net, a convolutional neural network, was trained on Monte Carlo simulated multispectral PA data and predicted sO₂ with higher accuracy compared to simple linear unmixing, suggesting machine learning can solve the fluence estimation problem. This project was funded by the Kaminsky Family Fund and the Neukom Institute.

Dartmouth Digital Commons Citation

Hoffer-Hawlik, Kevin and Luke, Geoffrey P., "absO2luteU-Net: Tissue Oxygenation Calculation Using Photoacoustic Imaging and Convolutional Neural Networks" (2019). ENGS 88 Honors Thesis (AB Students). 10.
https://digitalcommons.dartmouth.edu/engs88/10