ArticleAnalytical chemistry2018
Dynamic Sparse Sampling for Confocal Raman Microscopy.
Article in Analytical chemistry, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 10 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
10 citing papers in PubMed.
- Integrating spatial and chemical information enhances differentiation of non-alcoholic steatohepatitis states in Raman imaging.Scientific reports · 2025Article
- Confocal Raman microspectroscopy for spatially resolved tissue characterisation of disease-linked spectra-pathological signatures.Clinical and translational medicine · 2025Review
- On-the-fly Raman microscopy guaranteeing the accuracy of discrimination.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Correction for Extrinsic Background in Raman Hyperspectral Images.Analytical chemistry · 2023Article
- Deep Learning Approach for Dynamic Sampling for Multichannel Mass Spectrometry Imaging.IEEE transactions on computational imaging · 2023Article
- High-Throughput Mass Spectrometry Imaging with Dynamic Sparse Sampling.ACS measurement science au · 2022Article
- A Review of Pharmaceutical Robot based on Hyperspectral Technology.Journal of intelligent & robotic systems · 2022Review
- Review
- Deep Learning Approach for Dynamic Sparse Sampling for High-Throughput Mass Spectrometry Imaging.IS&T International Symposium on Electronic Imaging · 2021Article
- Data mining in Raman imaging in a cellular biological system.Computational and structural biotechnology journal · 2020Review
Corrections and comments
- Erratum issued
Authors and funding
9 authors.
Funding
Abstract
The total number of data points required for image generation in Raman microscopy was greatly reduced using sparse sampling strategies, in which the preceding set of measurements informed the next most information-rich sampling location. Using this approach, chemical images of pharmaceutical materials were obtained with >99% accuracy from 15.8% sampling, representing an ∼6-fold reduction in measurement time relative to full field of view rastering with comparable image quality. This supervised learning approach to dynamic sampling (SLADS) has the distinct advantage of being directly compatible with standard confocal Raman instrumentation. Furthermore, SLADS is not limited to Raman imaging, potentially providing time-savings in image reconstruction whenever the single-pixel measurement time is the limiting factor in image generation.
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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.