Fluorescence diffuse optical monitoring of bioreactors: a hybrid deep learning and model-based approach for tomography.

Autor:	Cao J; School of Computer Science, University of Birmingham, Birmingham B15 2TT, United Kingdom., Gorecki J; Department of Bioengineering, Imperial College London, London SW7 2AZ, United Kingdom., Dale R; School of Computer Science, University of Birmingham, Birmingham B15 2TT, United Kingdom., Redwood-Sawyerr C; Department of Chemical Engineering, Imperial College London, London SW7 2AZ, United Kingdom., Kontoravdi C; Department of Chemical Engineering, Imperial College London, London SW7 2AZ, United Kingdom., Polizzi K; Department of Chemical Engineering, Imperial College London, London SW7 2AZ, United Kingdom., Rowlands CJ; Department of Bioengineering, Imperial College London, London SW7 2AZ, United Kingdom., Dehghani H; School of Computer Science, University of Birmingham, Birmingham B15 2TT, United Kingdom.
Jazyk:	angličtina
Zdroj:	Biomedical optics express [Biomed Opt Express] 2024 Aug 02; Vol. 15 (9), pp. 5009-5024. Date of Electronic Publication: 2024 Aug 02 (Print Publication: 2024).
DOI:	10.1364/BOE.529884
Abstrakt:	Biosynthesis in bioreactors plays a vital role in many applications, but tools for accurate in situ monitoring of the cells are still lacking. By engineering the cells such that their conditions are reported through fluorescence, it is possible to fill in the gap using fluorescence diffuse optical tomography (fDOT). However, the spatial accuracy of the reconstruction can still be limited, due to e.g. undersampling and inaccurate estimation of the optical properties. Utilizing controlled phantom studies, we use a two-step hybrid approach, where a preliminary fDOT result is first obtained using the classic model-based optimization, and then enhanced using a neural network. We show in this paper using both simulated and phantom experiments that the proposed method can lead to a 8-fold improvement (Intersection over Union) of fluorescence inclusion reconstruction in noisy conditions, at the same speed of conventional neural network-based methods. This is an important step towards our ultimate goal of fDOT monitoring of bioreactors. Competing Interests: The authors declare no conflicts of interest. (© 2024 The Author(s).)
Databáze:	MEDLINE
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