Sparse channel sampling for ultrasound localization microscopy (SPARSE-ULM) Article Swipe
Erwan Hardy
,
Jonathan Porée
,
Hatim Belgharbi
,
Chloé Bourquin
,
Frédéric Lesage
,
Jean Provost
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1088/1361-6560/abf1b6
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1088/1361-6560/abf1b6
Ultrasound localization microscopy (ULM) has recently enabled the mapping of the cerebral vasculature in vivo with a resolution ten times smaller than the wavelength used, down to ten microns. However, with frame rates up to 20000 frames per second, this method requires large amount of data to be acquired, transmitted, stored, and processed. The transfer rate is, as of today, one of the main limiting factors of this technology. Herein, we introduce a novel reconstruction framework to decrease this quantity of data to be acquired and the complexity of the required hardware by randomly subsampling the channels of a linear probe. Method performance evaluation as well as parameters optimization were conducted in silico using the SIMUS simulation software in an anatomically realistic phantom and then compared to in vivo acquisitions in a rat brain after craniotomy. Results show that reducing the number of active elements deteriorates the signal-to-noise ratio and could lead to false microbubbles detections but has limited effect on localization accuracy. In simulation, the false positive rate on microbubble detection deteriorates from 3.7% for 128 channels in receive and 7 steered angles to 11% for 16 channels and 7 angles. The average localization accuracy ranges from 10.6 μ m and 9.93 μ m for 16 channels/3 angles and 128 channels/13 angles respectively. These results suggest that a compromise can be found between the number of channels and the quality of the reconstructed vascular network and demonstrate feasibility of performing ULM with a reduced number of channels in receive, paving the way for low-cost devices enabling high-resolution vascular mapping.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1361-6560/abf1b6
- OA Status
- green
- Cited By
- 9
- References
- 64
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3138256807
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3138256807Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1088/1361-6560/abf1b6Digital Object Identifier
- Title
-
Sparse channel sampling for ultrasound localization microscopy (SPARSE-ULM)Work title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-03-24Full publication date if available
- Authors
-
Erwan Hardy, Jonathan Porée, Hatim Belgharbi, Chloé Bourquin, Frédéric Lesage, Jean ProvostList of authors in order
- Landing page
-
https://doi.org/10.1088/1361-6560/abf1b6Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2310.13117Direct OA link when available
- Concepts
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Imaging phantom, Computer science, Channel (broadcasting), Artificial intelligence, Frame rate, Software, Microbubbles, Computer vision, Biomedical engineering, Ultrasound, Algorithm, Physics, Optics, Acoustics, Medicine, Telecommunications, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
9Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1, 2023: 4, 2022: 3, 2021: 1Per-year citation counts (last 5 years)
- References (count)
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64Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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