Imperial College London
Alternate

DrAmandaFoust

Faculty of Engineering, Department of Bioengineering

Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 1055a.foust Website CV

 
 
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Location

 

RSM 4.05Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Foust:2022:10.1109/MSP.2021.3123557,
author = {Foust, A and Song, P and Verinaz, Jadan HI and Howe, C and Dragotti, PL},
doi = {10.1109/MSP.2021.3123557},
journal = {IEEE: Signal Processing Magazine},
title = {Light-field microscopy for optical imaging of neuronal activity: when model-based methods meet data-driven approaches},
url = {http://dx.doi.org/10.1109/MSP.2021.3123557},
volume = {39},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Understanding how networks of neurons process information is one of the key challenges in modern neuroscience.A necessary step to achieve this goal is to be able to observe the dynamics of large populations of neurons overa large area of the brain. Light-field microscopy (LFM), a type of scanless microscope, is a particularly attractivecandidate for high-speed three-dimensional (3D) imaging. It captures volumetric information in a single snapshot,allowing volumetric imaging at video frame-rates. Specific features of imaging neuronal activity using LFM callfor the development of novel machine learning approaches that fully exploit priors embedded in physics and opticsmodels. Signal processing theory and wave-optics theory could play a key role in filling this gap, and contributeto novel computational methods with enhanced interpretability and generalization by integrating model-driven anddata-driven approaches. This paper is devoted to a comprehensive survey to state-of-the-art of computational methods for LFM, with a focus on model-based and data-driven approaches
AU  - Foust,A
AU  - Song,P
AU  - Verinaz,Jadan HI
AU  - Howe,C
AU  - Dragotti,PL
DO  - 10.1109/MSP.2021.3123557
PY  - 2022///
SN  - 1053-5888
TI  - Light-field microscopy for optical imaging of neuronal activity: when model-based methods meet data-driven approaches
T2  - IEEE: Signal Processing Magazine
UR  - http://dx.doi.org/10.1109/MSP.2021.3123557
UR  - https://ieeexplore.ieee.org/document/9721178
UR  - http://hdl.handle.net/10044/1/94280
VL  - 39
ER  -
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