Transformer-based Deep Learning Model for Fluorescence Lifetime Parameter Estimations using Pixelwise Instrument Response Function Article Swipe
İsmail Erbaş
,
Vikas Pandey
,
Navid Ibtehaj Nizam
,
Nanxue Yuan
,
Amit Verma
,
Margarida Barroso
,
Xavier Intes
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-5151657/v1
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-5151657/v1
Fluorescence lifetime imaging (FLI) is an important molecular imaging modality that can provide unique information for biomedical applications. FLI is based on acquiring and processing photon time of arrival histograms. The shape and temporal offset of these histograms depends on many factors, such as the instrument response function (IRF), optical properties, and the topographic profile of the sample. Several inverse solver analytical methods have been developed to compute the underlying fluorescence lifetime parameters, but most of them are computationally expensive and time-consuming. Thus, deep learning (DL) algorithms have progressively replaced computation methods in fluorescence lifetime parameter estimation. Often, DL models are trained with simple datasets either generated through simulation or a simple experiment where the fluorophore surface profile is mostly flat; therefore, DL models often do not perform well on samples with complex surface profiles such as ex-vivo organs or in-vivo whole intact animals. Herein, we introduce a new DL architecture, MFliNet (Macroscopic FLI Network), that takes an additional input of IRF together with TPSF, addressing discrepancies in the photon time-of-arrival distribution. We demonstrate the model’s performance through carefully designed, complex tissue-mimicking phantoms and preclinical in-vivo cancer xenograft experiments.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-5151657/v1
- OA Status
- gold
- Cited By
- 1
- References
- 32
- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W4403035043Canonical identifier for this work in OpenAlex
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https://doi.org/10.21203/rs.3.rs-5151657/v1Digital Object Identifier
- Title
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Transformer-based Deep Learning Model for Fluorescence Lifetime Parameter Estimations using Pixelwise Instrument Response FunctionWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-10-01Full publication date if available
- Authors
-
İsmail Erbaş, Vikas Pandey, Navid Ibtehaj Nizam, Nanxue Yuan, Amit Verma, Margarida Barroso, Xavier IntesList of authors in order
- Landing page
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https://doi.org/10.21203/rs.3.rs-5151657/v1Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.21203/rs.3.rs-5151657/v1Direct OA link when available
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Function (biology), Transformer, Fluorescence, Computer science, Biological system, Artificial intelligence, Mathematics, Physics, Optics, Biology, Voltage, Quantum mechanics, Evolutionary biologyTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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