ArticleBiomedical optics express2021
3-D compressed sensing optical coherence tomography using predictive coding.
Article in Biomedical optics express, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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.
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Who cites it
6 citing papers in PubMed.
- Predictive coding compressive sensing optical coherence tomography hardware implementation.Biomedical optics express · 2024Article
- High-speed forward-viewing optical coherence tomography probe based on Lissajous sampling and sparse reconstruction.Optics letters · 2024Article
- High-speed, long-range and wide-field OCT for in vivo 3D imaging of the oral cavity achieved by a 600 kHz swept source laser.Biomedical optics express · 2024Article
- Compressed sensing on displacement signals measured with optical coherence tomography.Biomedical optics express · 2023Article
- Compressed sensing of human breast optical coherence 3-D image volume data using predictive coding.Biomedical optics express · 2023Article
- Effect of optical coherence tomography and angiography sampling rate towards diabetic retinopathy severity classification.Biomedical optics express · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
We present a compressed sensing (CS) algorithm and sampling strategy for reconstructing 3-D Optical Coherence Tomography (OCT) image volumes from as little as 10% of the original data. Reconstruction using the proposed method, Denoising Predictive Coding (DN-PC), is demonstrated for five clinically relevant tissue types including human heart, retina, uterus, breast, and bovine ligament. DN-PC reconstructs the difference between adjacent b-scans in a volume and iteratively applies Gaussian filtering to improve image sparsity. An a-line sampling strategy was developed that can be easily implemented in existing Spectral-Domain OCT systems and reduce scan time by up to 90%.
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.