ENGS 88 Honors Thesis (AB Students)
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