@inproceedings{9a687572831a4d9eb1e12a1ac04445a7,
title = "Fusing Sparsity with Deep Learning for Rotating Scatter Mask Gamma Imaging",
abstract = "Many nuclear safety applications need fast, portable, and accurate imagers to better locate radiation sources. The Rotating Scatter Mask (RSM) system is an emerging device with the potential to meet these needs. The main challenge is the under-determined nature of the data acquisition process: the dimension of the measured signal is far less than the dimension of the image to be reconstructed. To address this challenge, this work aims to fuse model-based sparsity-promoting regularization and a data-driven deep neural network denoising image prior to perform image reconstruction. An efficient algorithm is developed and produces superior reconstructions relative to current approaches.",
author = "Yilun Zhu and Scott, {Clayton D.} and Holland, {Darren E.} and Landon, {George V.} and Fjeldsted, {Aaron P.} and Lintereur, {Azaree T.}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE Nuclear Science Symposium, Medical Imaging Conference, and Room Temperature Semiconductor Detector Conference, IEEE NSS MIC RTSD 2022 ; Conference date: 05-11-2022 Through 12-11-2022",
year = "2022",
doi = "10.1109/NSS/MIC44845.2022.10399334",
language = "English (US)",
series = "2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium, Medical Imaging Conference and Room Temperature Semiconductor Detector Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium, Medical Imaging Conference and Room Temperature Semiconductor Detector Conference",
address = "United States",
}