ArticleScientific data2025
A large open access dataset of transillumination imaging the toward realization of optical computed tomography.
Article in Scientific data, 2025. 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
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
1 citing paper in PubMed.
- A large open access dataset of transillumination imaging the toward realization of optical computed tomography.Scientific data · 2025Article
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
No grant is acknowledged in the PubMed record.
Abstract
Transillumination imaging is commonly used in medicine and biometrics to provide non-invasive insights into internal structures. However, the prevalent image blurring resulting from scattering effects poses a significant challenge to the effective application of transillumination. Artificial intelligence algorithms have gained prominence for enhancing transillumination images and addressing challenges such as scattering suppression, depth estimation, and three-dimensional structure reconstruction. These advances require high-quality input images to optimize model performance. Acquiring a large-scale transillumination image dataset for practical AI applications is difficult due to subjective imaging conditions. This study aimed to overcome this obstacle by introducing a comprehensive dataset of transillumination images. Methods and algorithms for generating depth-dependent point-spread function and transillumination images were presented. The dataset comprised 12,000 pairs of images of clear and scattered media, each associated with the corresponding depth information. This study is valuable for advancing AI-based solutions in transillumination imaging and provides a foundation for further research on image deblurring, depth perception, and 3D reconstruction.
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.