ArticleTomography (Ann Arbor, Mich.)2016
Optoacoustic Tomography Using Accelerated Sparse Recovery and Coherence Factor Weighting.
Article in Tomography (Ann Arbor, Mich.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
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.
Who cites it
1 citing paper in PubMed.
- Single-pixel camera photoacoustic tomography.Journal of biomedical optics · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sparse recovery algorithms have shown great potential to accurately reconstruct images using limited-view optoacoustic (photoacoustic) tomography data sets, but these are computationally expensive. In this paper, we propose an improvement of the fast converging Split Augmented Lagrangian Shrinkage Algorithm method based on least square QR inversion for improving the reconstruction speed. We further show image fidelity improvement when using a coherence factor to weight the reconstruction result. Phantom and in vivo measurements show that the accelerated Split Augmented Lagrangian Shrinkage Algorithm method with coherence factor weighting offers images with reduced artifacts and provides faster convergence compared with existing sparse recovery algorithms.
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Registered trials
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.