A Deep Learning Framework for Spatiotemporal Ultrasound Localization Microscopy Article Swipe
Léo Milecki
,
Jonathan Porée
,
Hatim Belgharbi
,
Chloé Bourquin
,
Rafat Damseh
,
Patrick Delafontaine-Martel
,
Frédéric Lesage
,
Maxime Gasse
,
Jean Provost
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/tmi.2021.3056951
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/tmi.2021.3056951
Ultrasound Localization Microscopy (ULM) can resolve the microvascular bed down to a few micrometers. To achieve such performance, microbubble contrast agents must perfuse the entire microvascular network. Microbubbles are then located individually and tracked over time to sample individual vessels, typically over hundreds of thousands of images. To overcome the fundamental limit of diffraction and achieve a dense reconstruction of the network, low microbubble concentrations must be used, which leads to acquisitions lasting several minutes. Conventional processing pipelines are currently unable to deal with interference from multiple nearby microbubbles, further reducing achievable concentrations. This work overcomes this problem by proposing a Deep Learning approach to recover dense vascular networks from ultrasound acquisitions with high microbubble concentrations. A realistic mouse brain microvascular network, segmented from 2-photon microscopy, was used to train a three-dimensional convolutional neural network (CNN) based on a V-net architecture. Ultrasound data sets from multiple microbubbles flowing through the microvascular network were simulated and used as ground truth to train the 3D CNN to track microbubbles. The 3D-CNN approach was validated in silico using a subset of the data and in vivo in a rat brain. In silico, the CNN reconstructed vascular networks with higher precision (81%) than a conventional ULM framework (70%). In vivo, the CNN could resolve micro vessels as small as 10 μ m with an improvement in resolution when compared against a conventional approach.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tmi.2021.3056951
- OA Status
- green
- Cited By
- 89
- References
- 53
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3127471220
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W3127471220Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/tmi.2021.3056951Digital Object Identifier
- Title
-
A Deep Learning Framework for Spatiotemporal Ultrasound Localization MicroscopyWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-02-03Full publication date if available
- Authors
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Léo Milecki, Jonathan Porée, Hatim Belgharbi, Chloé Bourquin, Rafat Damseh, Patrick Delafontaine-Martel, Frédéric Lesage, Maxime Gasse, Jean ProvostList of authors in order
- Landing page
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https://doi.org/10.1109/tmi.2021.3056951Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2310.08143Direct OA link when available
- Concepts
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Microbubbles, Convolutional neural network, Computer science, Artificial intelligence, Deep learning, Ground truth, Microscopy, Computer vision, In silico, Ultrasound, Biomedical engineering, Pattern recognition (psychology), Physics, Optics, Chemistry, Acoustics, Medicine, Biochemistry, GeneTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
89Total citation count in OpenAlex
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2025: 18, 2024: 24, 2023: 17, 2022: 21, 2021: 9Per-year citation counts (last 5 years)
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53Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.network, | 61, 121 |
| abstract_inverted_index.network. | 26 |
| abstract_inverted_index.networks | 108, 193 |
| abstract_inverted_index.overcome | 48 |
| abstract_inverted_index.reducing | 90 |
| abstract_inverted_index.vascular | 107, 192 |
| abstract_inverted_index.vessels, | 39 |
| abstract_inverted_index.approach. | 228 |
| abstract_inverted_index.currently | 79 |
| abstract_inverted_index.framework | 202 |
| abstract_inverted_index.overcomes | 95 |
| abstract_inverted_index.pipelines | 77 |
| abstract_inverted_index.precision | 196 |
| abstract_inverted_index.proposing | 99 |
| abstract_inverted_index.realistic | 117 |
| abstract_inverted_index.segmented | 122 |
| abstract_inverted_index.simulated | 153 |
| abstract_inverted_index.thousands | 44 |
| abstract_inverted_index.typically | 40 |
| abstract_inverted_index.validated | 171 |
| abstract_inverted_index.Microscopy | 2 |
| abstract_inverted_index.Ultrasound | 0, 141 |
| abstract_inverted_index.achievable | 91 |
| abstract_inverted_index.individual | 38 |
| abstract_inverted_index.processing | 76 |
| abstract_inverted_index.resolution | 222 |
| abstract_inverted_index.ultrasound | 110 |
| abstract_inverted_index.diffraction | 53 |
| abstract_inverted_index.fundamental | 50 |
| abstract_inverted_index.improvement | 220 |
| abstract_inverted_index.microbubble | 18, 63, 114 |
| abstract_inverted_index.microscopy, | 125 |
| abstract_inverted_index.Conventional | 75 |
| abstract_inverted_index.Localization | 1 |
| abstract_inverted_index.Microbubbles | 27 |
| abstract_inverted_index.acquisitions | 71, 111 |
| abstract_inverted_index.conventional | 200, 227 |
| abstract_inverted_index.individually | 31 |
| abstract_inverted_index.interference | 84 |
| abstract_inverted_index.microbubbles | 146 |
| abstract_inverted_index.micrometers. | 13 |
| abstract_inverted_index.performance, | 17 |
| abstract_inverted_index.architecture. | 140 |
| abstract_inverted_index.convolutional | 132 |
| abstract_inverted_index.microbubbles, | 88 |
| abstract_inverted_index.microbubbles. | 166 |
| abstract_inverted_index.microvascular | 7, 25, 120, 150 |
| abstract_inverted_index.reconstructed | 191 |
| abstract_inverted_index.concentrations | 64 |
| abstract_inverted_index.reconstruction | 58 |
| abstract_inverted_index.concentrations. | 92, 115 |
| abstract_inverted_index.three-dimensional | 131 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 99 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 9 |
| citation_normalized_percentile.value | 0.97396189 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |