Enhancing Fluorescence Lifetime Parameter Estimation Accuracy with Differential Transformer Based Deep Learning Model Incorporating Pixelwise Instrument Response Function Article Swipe
İsmail Erbaş
,
Vikas Pandey
,
Navid Ibtehaj Nizam
,
Nanxue Yuan
,
Amit Verma
,
Margarida Barosso
,
Xavier Intes
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2411.16896
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2411.16896
Fluorescence Lifetime Imaging (FLI) is a critical molecular imaging modality that provides unique information about the tissue microenvironment, which is invaluable for biomedical applications. FLI operates by acquiring and analyzing photon time-of-arrival histograms to extract quantitative parameters associated with temporal fluorescence decay. These histograms are influenced by the intrinsic properties of the fluorophore, instrument parameters, time-of-flight distributions associated with pixel-wise variations in the topographic and optical characteristics of the sample. Recent advancements in Deep Learning (DL) have enabled improved fluorescence lifetime parameter estimation. However, existing models are primarily designed for planar surface samples, limiting their applicability in translational scenarios involving complex surface profiles, such as \textit{in-vivo} whole-animal or imaged guided surgical applications. To address this limitation, we present MFliNet (Macroscopic FLI Network), a novel DL architecture that integrates the Instrument Response Function (IRF) as an additional input alongside experimental photon time-of-arrival histograms. Leveraging the capabilities of a Differential Transformer encoder-decoder architecture, MFliNet effectively focuses on critical input features, such as variations in photon time-of-arrival distributions. We evaluate MFliNet using rigorously designed tissue-mimicking phantoms and preclinical in-vivo cancer xenograft models. Our results demonstrate the model's robustness and suitability for complex macroscopic FLI applications, offering new opportunities for advanced biomedical imaging in diverse and challenging settings.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2411.16896
- https://arxiv.org/pdf/2411.16896
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W4404988192Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2411.16896Digital Object Identifier
- Title
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Enhancing Fluorescence Lifetime Parameter Estimation Accuracy with Differential Transformer Based Deep Learning Model Incorporating Pixelwise Instrument Response FunctionWork title
- Type
-
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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2024-11-25Full publication date if available
- Authors
-
İsmail Erbaş, Vikas Pandey, Navid Ibtehaj Nizam, Nanxue Yuan, Amit Verma, Margarida Barosso, Xavier IntesList of authors in order
- Landing page
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https://arxiv.org/abs/2411.16896Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2411.16896Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2411.16896Direct OA link when available
- Concepts
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Biological system, Fluorescence, Transformer, Differential (mechanical device), Function (biology), Computer science, Materials science, Physics, Optics, Engineering, Biology, Voltage, Electrical engineering, Thermodynamics, Evolutionary biologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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| abstract_inverted_index.existing | 84 |
| abstract_inverted_index.improved | 78 |
| abstract_inverted_index.lifetime | 80 |
| abstract_inverted_index.limiting | 93 |
| abstract_inverted_index.modality | 9 |
| abstract_inverted_index.offering | 192 |
| abstract_inverted_index.operates | 25 |
| abstract_inverted_index.phantoms | 172 |
| abstract_inverted_index.provides | 11 |
| abstract_inverted_index.samples, | 92 |
| abstract_inverted_index.surgical | 110 |
| abstract_inverted_index.temporal | 39 |
| abstract_inverted_index.Network), | 121 |
| abstract_inverted_index.acquiring | 27 |
| abstract_inverted_index.alongside | 137 |
| abstract_inverted_index.analyzing | 29 |
| abstract_inverted_index.features, | 157 |
| abstract_inverted_index.intrinsic | 48 |
| abstract_inverted_index.involving | 99 |
| abstract_inverted_index.molecular | 7 |
| abstract_inverted_index.parameter | 81 |
| abstract_inverted_index.primarily | 87 |
| abstract_inverted_index.profiles, | 102 |
| abstract_inverted_index.scenarios | 98 |
| abstract_inverted_index.settings. | 203 |
| abstract_inverted_index.xenograft | 177 |
| abstract_inverted_index.Instrument | 129 |
| abstract_inverted_index.Leveraging | 142 |
| abstract_inverted_index.additional | 135 |
| abstract_inverted_index.associated | 37, 57 |
| abstract_inverted_index.biomedical | 22, 197 |
| abstract_inverted_index.histograms | 32, 43 |
| abstract_inverted_index.influenced | 45 |
| abstract_inverted_index.instrument | 53 |
| abstract_inverted_index.integrates | 127 |
| abstract_inverted_index.invaluable | 20 |
| abstract_inverted_index.parameters | 36 |
| abstract_inverted_index.pixel-wise | 59 |
| abstract_inverted_index.properties | 49 |
| abstract_inverted_index.rigorously | 169 |
| abstract_inverted_index.robustness | 184 |
| abstract_inverted_index.variations | 60, 160 |
| abstract_inverted_index.Transformer | 148 |
| abstract_inverted_index.challenging | 202 |
| abstract_inverted_index.demonstrate | 181 |
| abstract_inverted_index.effectively | 152 |
| abstract_inverted_index.estimation. | 82 |
| abstract_inverted_index.histograms. | 141 |
| abstract_inverted_index.information | 13 |
| abstract_inverted_index.limitation, | 115 |
| abstract_inverted_index.macroscopic | 189 |
| abstract_inverted_index.parameters, | 54 |
| abstract_inverted_index.preclinical | 174 |
| abstract_inverted_index.suitability | 186 |
| abstract_inverted_index.topographic | 63 |
| abstract_inverted_index.(Macroscopic | 119 |
| abstract_inverted_index.Differential | 147 |
| abstract_inverted_index.Fluorescence | 0 |
| abstract_inverted_index.advancements | 71 |
| abstract_inverted_index.architecture | 125 |
| abstract_inverted_index.capabilities | 144 |
| abstract_inverted_index.experimental | 138 |
| abstract_inverted_index.fluorescence | 40, 79 |
| abstract_inverted_index.fluorophore, | 52 |
| abstract_inverted_index.quantitative | 35 |
| abstract_inverted_index.whole-animal | 106 |
| abstract_inverted_index.applicability | 95 |
| abstract_inverted_index.applications, | 191 |
| abstract_inverted_index.applications. | 23, 111 |
| abstract_inverted_index.architecture, | 150 |
| abstract_inverted_index.distributions | 56 |
| abstract_inverted_index.opportunities | 194 |
| abstract_inverted_index.translational | 97 |
| abstract_inverted_index.distributions. | 164 |
| abstract_inverted_index.time-of-flight | 55 |
| abstract_inverted_index.characteristics | 66 |
| abstract_inverted_index.encoder-decoder | 149 |
| abstract_inverted_index.time-of-arrival | 31, 140, 163 |
| abstract_inverted_index.\textit{in-vivo} | 105 |
| abstract_inverted_index.tissue-mimicking | 171 |
| abstract_inverted_index.microenvironment, | 17 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |