ArticleJournal of biomedical optics2014
Fast spatiotemporal image reconstruction based on low-rank matrix estimation for dynamic photoacoustic computed tomography.
Article in Journal of biomedical optics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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.
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
9 citing papers in PubMed.
- Dynamic quantitative photoacoustic tomography under sparse sampling via residual-driven Kalman filter assisted reconstruction.Photoacoustics · 2026Article
- Spatiotemporal image reconstruction to enable high-frame-rate dynamic photoacoustic tomography with rotating-gantry volumetric imagers.Journal of biomedical optics · 2024Article
- ProxNF: Neural Field Proximal Training for High-Resolution 4D Dynamic Image Reconstruction.IEEE transactions on computational imaging · 2024Article
- Adaptive enhancement of acoustic resolution photoacoustic microscopy imagingPhotoacoustics · 2023Article
- Spatiotemporal singular value decomposition for denoising in photoacoustic imaging with a low-energy excitation light source.Biomedical optics express · 2022Article
- Enhancement of in vivo cardiac photoacoustic signal specificity using spatiotemporal singular value decomposition.Journal of biomedical optics · 2021Article
- Optoacoustic Tomography Using Accelerated Sparse Recovery and Coherence Factor Weighting.Tomography (Ann Arbor, Mich.) · 2016Article
- Enabling high-speed wide-field dynamic imaging in multifocal photoacoustic computed microscopy: a simulation study.Applied optics · 2016Article
- Photoacoustic imaging platforms for multimodal imaging.Ultrasonography (Seoul, Korea) · 2015Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
In order to monitor dynamic physiological events in near-real time, a variety of photoacoustic computed tomography (PACT) systems have been developed that can rapidly acquire data. Previously reported studies of dynamic PACT have employed conventional static methods to reconstruct a temporally ordered sequence of images on a frame-by-frame basis. Frame-by-frame image reconstruction (FBFIR) methods fail to exploit correlations between data frames and are known to be statistically and computationally suboptimal. In this study, a low-rank matrix estimation-based spatiotemporal image reconstruction (LRME-STIR) method is investigated for dynamic PACT applications. The LRME-STIR method is based on the observation that, in many PACT applications, the number of frames is much greater than the rank of the ideal noiseless data matrix. Using both computer-simulated and experimentally measured photoacoustic data, the performance of the LRME-STIR method is compared with that of conventional FBFIR method followed by image-domain filtering. The results demonstrate that the LRME-STIR method is not only computationally more efficient but also produces more accurate dynamic PACT images than a conventional FBFIR method followed by image-domain filtering.
Indexed as
Identifiers
What OpenQuestion holds
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.