TY - GEN
T1 - High-Speed Fluorescence Molecular Tomography Reconstructions through a Sparsity Constrained Neural Network
AU - Wang, Fay
AU - Hielscher, Andreas H.
AU - Kim, Stephen Hyunkeol
N1 - Funding Information:
This research was developed with funding from the National Science Foundation Graduate Research Fellowship under Grant No. DGE-2036197. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors(s) and do not necessarily reflect the views of the National Science Foundation, the Department of Defense, or the U.S. Government.
Publisher Copyright:
© 2023 SPIE.
PY - 2023
Y1 - 2023
N2 - Fluorescence molecular tomography (FMT) has gained prominence in recent years as a viable optical imaging technique for non-invasive, high-sensitivity, tomographic imaging of the brain. While optical imaging methods have demonstrated promising results for quantitative imaging of functional changes in the brain, they are still limited in their abilities to achieve high spatial and temporal resolution. To address these challenges, we present here a deep learning solution for FMT reconstructions, which implements a neural network with our novel asymptotic sparse function from our previously introduced sensitivity equation-based non-iterative sparse optical reconstruction (SENSOR) code to achieve high-resolution and sparse reconstructions using only learned parameters. We evaluate the proposed network through numerical phantom experiments. Furthermore, once the network is trained, the total reconstruction time is independent of the number of sources and wavelengths used.
AB - Fluorescence molecular tomography (FMT) has gained prominence in recent years as a viable optical imaging technique for non-invasive, high-sensitivity, tomographic imaging of the brain. While optical imaging methods have demonstrated promising results for quantitative imaging of functional changes in the brain, they are still limited in their abilities to achieve high spatial and temporal resolution. To address these challenges, we present here a deep learning solution for FMT reconstructions, which implements a neural network with our novel asymptotic sparse function from our previously introduced sensitivity equation-based non-iterative sparse optical reconstruction (SENSOR) code to achieve high-resolution and sparse reconstructions using only learned parameters. We evaluate the proposed network through numerical phantom experiments. Furthermore, once the network is trained, the total reconstruction time is independent of the number of sources and wavelengths used.
KW - Optical imaging
KW - fluorescence molecular tomography
KW - neural network
KW - sparsity
KW - time domain
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U2 - 10.1117/12.2649022
DO - 10.1117/12.2649022
M3 - Conference contribution
AN - SCOPUS:85159762176
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - High-Speed Biomedical Imaging and Spectroscopy VIII
A2 - Tsia, Kevin K.
A2 - Goda, Keisuke
PB - SPIE
T2 - High-Speed Biomedical Imaging and Spectroscopy VIII 2023
Y2 - 28 January 2023 through 30 January 2023
ER -