Evidence map›Paper›PMID 38575577›Full record

ArticleNature communications2024

Context-aware deep learning enables high-efficacy localization of high concentration microbubbles for super-resolution ultrasound localization microscopy.

YiRang Shin, Matthew R Lowerison, Yike Wang, Xi Chen, Qi You, Zhijie Dong, Mark A Anastasio, Pengfei Song

Open access · goldAbstract read
In one paragraph

Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed
12.0field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

32 citing papers in PubMed, 66 citations in OpenAlex.

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  10. Assessing cerebral capillary function and stalling using single capillary reporters in ultrasound localization microscopy.Proceedings of the National Academy of Sciences of the United States of America · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 1 institution in 1 country.

YiRang ShinBeckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA.
Matthew R LowerisonBeckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA.
Yike WangBeckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA.ORCID http://orcid.org/0009-0003-0113-6014
Xi ChenBeckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA.
Qi YouBeckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA.
Zhijie DongBeckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA.ORCID http://orcid.org/0000-0001-8098-7789
Mark A AnastasioBeckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA.ORCID http://orcid.org/0000-0002-3192-4172
Pengfei SongBeckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA. songp@illinois.edu.ORCID http://orcid.org/0000-0002-9103-6345
University of Illinois Urbana-Champaign · US

Funding

Next-Generation Ultrasound Localization MicroscopyR21EB030072 · NIBIB · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI SONG, PENGFEI · 2020 to 2022
$927k
High-resolution cerebral microvascular imaging for characterizing vascular dysfunction in Alzheimer's disease mouse modelR56NS131516 · NINDS · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI LLANO, DANIEL A · 2023 to 2023
$575k
Super-resolution imaging of brain microvascular changes in a model of Alzheimer DiseaseR21AG077173 · NIA · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI LLANO, DANIEL A · 2022 to 2022
$422k
NIA NIH HHS R21 AG077173NIBIB NIH HHS R21 EB030072NINDS NIH HHS R56 NS131516
6 · The paper itself

Abstract

Ultrasound localization microscopy (ULM) enables deep tissue microvascular imaging by localizing and tracking intravenously injected microbubbles circulating in the bloodstream. However, conventional localization techniques require spatially isolated microbubbles, resulting in prolonged imaging time to obtain detailed microvascular maps. Here, we introduce LOcalization with Context Awareness (LOCA)-ULM, a deep learning-based microbubble simulation and localization pipeline designed to enhance localization performance in high microbubble concentrations. In silico, LOCA-ULM enhanced microbubble detection accuracy to 97.8% and reduced the missing rate to 23.8%, outperforming conventional and deep learning-based localization methods up to 17.4% in accuracy and 37.6% in missing rate reduction. In in vivo rat brain imaging, LOCA-ULM revealed dense cerebrovascular networks and spatially adjacent microvessels undetected by conventional ULM. We further demonstrate the superior localization performance of LOCA-ULM in functional ULM (fULM) where LOCA-ULM significantly increased the functional imaging sensitivity of fULM to hemodynamic responses invoked by whisker stimulations in the rat brain.

Indexed as

Deep LearningMicroscopyAnimalsIntravital MicroscopyMicrobubblesMicrovesselsRatsUltrasonography

Identifiers

PMID38575577
PMCPMC10995206
OpenAlexW4393928472

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.