Inverse Problems and Machine Learning

Date: September 25, 2019

Time: 12:00PM - 1:00PM

Location: BLOC628

Speaker: Weston Baines, Texas A&M

Title: Deep neural network for source detection in 2D high noise emission problems

Abstract: Source detection in high noise environments is crucial for single-photon emission computed tomography (SPECT) medical imaging and especially for homeland security applications. In the latter case, one deals with detection of low emission nuclear sources in the presence of significant background noise (with SNR < 0.01). Direction sensitivity is needed to achieve this goal. Collimation, used for that purpose in standard gamma-cameras is not an option. Instead, Compton cameras are used. Backprojection methods enable detection in a random uniform background. In most practical applications, however, the presence of cargo violates this assumption and renders backprojection methods ineffective. A deep neural network is implemented for the task that exhibits higher sensitivity and specificity than the backprojection techniques in a low scattering case and works well when presence of cargo makes those techniques fail. This is joint work with P. Kuchment (Math) and J. Ragusa (Nuclear Eng.)