Projects per year
Abstract
This paper reports a bespoke adder-based deep learning network for time-domain fluorescence lifetime imaging (FLIM). By leveraging the l1-norm extraction method, we propose a 1-D Fluorescence Lifetime AdderNet (FLAN) without multiplication-based convolutions to reduce the computational complexity. Further, we compressed fluorescence decays in temporal dimension using a log-scale merging technique to discard redundant temporal information derived as log-scaling FLAN (FLAN+LS). FLAN+LS achieves 0.11 and 0.23 compression ratios compared with FLAN and a conventional 1-D convolutional neural network (1-D CNN) while maintaining high accuracy in retrieving lifetimes. We extensively evaluated FLAN and FLAN+LS using synthetic and real data. A traditional fitting method and other non-fitting, high-accuracy algorithms were compared with our networks for synthetic data. Our networks attained a minor reconstruction error in different photon-count scenarios. For real data, we used fluorescent beads' data acquired by a confocal microscope to validate the effectiveness of real fluorophores, and our networks can differentiate beads with different lifetimes. Additionally, we implemented the network architecture on a field-programmable gate array (FPGA) with a post-quantization technique to shorten the bit-width, thereby improving computing efficiency. FLAN+LS on hardware achieves the highest computing efficiency compared to 1-D CNN and FLAN. We also discussed the applicability of our network and hardware architecture for other time-resolved biomedical applications using photon-efficient, time-resolved sensors
Original language | English |
---|---|
Article number | 025002 |
Number of pages | 16 |
Journal | Methods and Applications in Fluorescence |
Volume | 11 |
Issue number | 2 |
Early online date | 2 Mar 2023 |
DOIs | |
Publication status | Published - 20 Mar 2023 |
Keywords
- computational imaging
- time-resolved biomedical imaging
- deep learning
- reconfigurable hardware
- fluorescence lifetime
Fingerprint
Dive into the research topics of 'Compact and robust deep learning architecture for fluorescence lifetime imaging and FPGA implementation'. Together they form a unique fingerprint.Projects
- 2 Finished
-
SPRINT: A SuPer-Resolution time-resolved ImagiNg and specTroscopy facility for rapid biomolecular analysis
Li, D. (Principal Investigator), Chamberlain, L. (Co-investigator), Chen, Y. (Co-investigator), Cunningham, M. R. (Co-investigator), Gould, G. (Co-investigator), Hoskisson, P. (Co-investigator), McConnell, G. (Co-investigator), Rattray, Z. (Co-investigator) & Van de Linde, S. (Co-investigator)
BBSRC (Biotech & Biological Sciences Research Council)
1/07/21 → 31/05/23
Project: Research
-
A new approach for imaging RNA at the single cell level
Chen, Y. (Principal Investigator), Birch, D. (Co-investigator) & Yu, J. (Co-investigator)
BBSRC (Biotech & Biological Sciences Research Council)
29/07/13 → 28/01/15
Project: Research