ENGS 88 Honors Thesis (AB Students)
Degree Program
A.B.
Year of Graduation
2020
Faculty Advisor
Geoffrey Luke
Document Type
Thesis (Senior Honors)
Publication Date
Spring 6-10-2020
Abstract
Photoacoustic (PA) imaging is a non-invasive diagnostic imaging technique that gives images of photoabsorbers based on their absorption of optical energy. These optical absorption properties can then be linked to important tissue properties. For the method to be quantitative, however, it is necessary to have an accurate estimation of the light fluence in the tissue. The current gold standard in addressing the fluence estimation problem, a Monte Carlo Simulation, is costly in time and computation. In this work, we developed a deep neural network to quickly and accurately estimate light fluence in arbitrary tissue types and geometries. The network was trained on light fluence estimations from Monte Carlo simulations of light propagation through pseudo-random tissue structures. The network estimated light fluence with only 0.65\% error in less than 2 ms per tissue. We hope these results bring real-time PA imaging closer to clinical relevancy.
Dartmouth Digital Commons Citation
Blasey, Nicholas and Luke, Geoffrey P., "A Convolutional Neural Network for Fast Fluence Estimation in Complex Tissues" (2020). ENGS 88 Honors Thesis (AB Students). 19.
https://digitalcommons.dartmouth.edu/engs88/19
