Evidence map›Paper›PMID 32713168›Full record

ArticleJournal of biomedical optics2020

GPU implementation of photoacoustic short-lag spatial coherence imaging for improved image-guided interventions.

Eduardo A Gonzalez, Muyinatu A Lediju Bell

Abstract read
In one paragraph

Article in Journal of biomedical optics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
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

15 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Deep learningJournal of biomedical optics · 2024
    Article
  7. Article
  8. Spatial Coherence in Medical Ultrasound: A Review.Ultrasound in medicine & biology · 2022
    Review
  9. Improving Minimum Variance Beamforming with Sub-Aperture Processing for Photoacoustic Imaging.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2021
    Article
  10. Article
  11. Subaperture Processing-Based Adaptive Beamforming for Photoacoustic Imaging.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2021
    Article
  12. Article
  13. Article
  14. Review
  15. Article
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

2 authors.

Eduardo A GonzalezJohns Hopkins University, School of Medicine, Department of Biomedical Engineering, Baltimore, Maryl, United States.
Muyinatu A Lediju BellJohns Hopkins University, School of Medicine, Department of Biomedical Engineering, Baltimore, Maryl, United States.

Funding

Coherence-Based Photoacoustic Image Guidance of Transsphenoidal SurgeriesR00EB018994 · NIBIB · JOHNS HOPKINS UNIVERSITY · PI BELL, MUYINATU A. LEDIJU · 2017 to 2019
$726k
NIBIB NIH HHS R00 EB018994
6 · The paper itself

Abstract

significancePhotoacoustic-based visual servoing is a promising technique for surgical tool tip tracking and automated visualization of photoacoustic targets during interventional procedures. However, one outstanding challenge has been the reliability of obtaining segmentations using low-energy light sources that operate within existing laser safety limits.

aimWe developed the first known graphical processing unit (GPU)-based real-time implementation of short-lag spatial coherence (SLSC) beamforming for photoacoustic imaging and applied this real-time algorithm to improve signal segmentation during photoacoustic-based visual servoing with low-energy lasers. APPROACH: A 1-mm-core-diameter optical fiber was inserted into ex vivo bovine tissue. Photoacoustic-based visual servoing was implemented as the fiber was manually displaced by a translation stage, which provided ground truth measurements of the fiber displacement. GPU-SLSC results were compared with a central processing unit (CPU)-SLSC approach and an amplitude-based delay-and-sum (DAS) beamforming approach. Performance was additionally evaluated with in vivo cardiac data.

resultsThe GPU-SLSC implementation achieved frame rates up to 41.2 Hz, representing a factor of 348 speedup when compared with offline CPU-SLSC. In addition, GPU-SLSC successfully recovered low-energy signals (i.e., ≤268  μJ) with mean ± standard deviation of signal-to-noise ratios of 11.2  ±  2.4 (compared with 3.5  ±  0.8 with conventional DAS beamforming). When energies were lower than the safety limit for skin (i.e., 394.6  μJ for 900-nm wavelength laser light), the median and interquartile range (IQR) of visual servoing tracking errors obtained with GPU-SLSC were 0.64 and 0.52 mm, respectively (which were lower than the median and IQR obtained with DAS by 1.39 and 8.45 mm, respectively). GPU-SLSC additionally reduced the percentage of failed segmentations when applied to in vivo cardiac data.

conclusionsResults are promising for the use of low-energy, miniaturized lasers to perform GPU-SLSC photoacoustic-based visual servoing in the operating room with laser pulse repetition frequencies as high as 41.2 Hz.

Indexed as

AlgorithmsPhotoacoustic TechniquesAnimalsCattleDiagnostic ImagingPhantoms, ImagingReproducibility of ResultsSignal-To-Noise RatioUltrasonographycoherence-based beamforminggraphical processing unitphotoacousticreal-timevisual servoing

Identifiers

PMID32713168
PMCPMC7381831

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LicenceCC BY
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Registered trials

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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.