Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

3D k-space reflectance fluorescence tomography via deep learning

Not Accessible

Your library or personal account may give you access

Abstract

We report on the potential to perform image reconstruction in 3D k-space reflectance fluorescence tomography (FT) using deep learning (DL). Herein, we adopt a modified AUTOMAP architecture and develop a training methodology that leverages an open-source Monte-Carlo-based simulator to generate a large dataset. Using an enhanced EMNIST (EEMNIST) dataset as an embedded contrast function allows us to train the network efficiently. The optical strategy utilizes k-space illumination in a reflectance configuration to probe tissue in the mesoscopic regime with high sensitivity and resolution. The proposed DL model training and validation is performed with both in silico data and a phantom experiment. Overall, our results indicate that the approach can correctly reconstruct both single and multiple fluorescent embedding(s) in a 3D volume. Furthermore, the presented technique is shown to outperform the traditional approaches [least-squares (LSQ) and total-variation minimization (TVAL)], especially at higher depths. We, therefore, expect the proposed computational technique to have future implications in preclinical studies.

© 2022 Optica Publishing Group

Full Article  |  PDF Article
More Like This
Deep-learning-based 3D object salient detection via light-field integral imaging

Ying Li, Tianhao Wang, Yanheng Liao, Da-Hai Li, and Xiaowei Li
Opt. Lett. 47(7) 1758-1761 (2022)

3D deep encoder–decoder network for fluorescence molecular tomography

Lin Guo, Fei Liu, Chuangjian Cai, Jie Liu, and Guanglei Zhang
Opt. Lett. 44(8) 1892-1895 (2019)

Macroscopic fluorescence lifetime topography enhanced via spatial frequency domain imaging

Jason T. Smith, Enagnon Aguénounon, Sylvain Gioux, and Xavier Intes
Opt. Lett. 45(15) 4232-4235 (2020)

References

You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Supplementary Material (1)

NameDescription
Supplement 1       Supplement containing two figures to aid the main Letter

Data availability

Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Figures (5)

You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Metrics

Select as filters


Select Topics Cancel
© Copyright 2022 | Optica Publishing Group. All Rights Reserved