ArticleJournal of synchrotron radiation2020
Limited angle tomography for transmission X-ray microscopy using deep learning.
Article in Journal of synchrotron radiation, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Hybrid Reconstruction Approach for Polychromatic Computed Tomography in Highly Limited-Data Scenarios.Sensors (Basel, Switzerland) · 2024Article
- Towards full-stack deep learning-empowered data processing pipeline for synchrotron tomography experiments.Innovation (Cambridge (Mass.)) · 2024Review
- Machine learning denoising of high-resolution X-ray nanotomography data.Journal of synchrotron radiation · 2022Article
- Article
- A Survey of Soft Computing Approaches in Biomedical Imaging.Journal of healthcare engineering · 2021Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
In transmission X-ray microscopy (TXM) systems, the rotation of a scanned sample might be restricted to a limited angular range to avoid collision with other system parts or high attenuation at certain tilting angles. Image reconstruction from such limited angle data suffers from artifacts because of missing data. In this work, deep learning is applied to limited angle reconstruction in TXMs for the first time. With the challenge to obtain sufficient real data for training, training a deep neural network from synthetic data is investigated. In particular, U-Net, the state-of-the-art neural network in biomedical imaging, is trained from synthetic ellipsoid data and multi-category data to reduce artifacts in filtered back-projection (FBP) reconstruction images. The proposed method is evaluated on synthetic data and real scanned chlorella data in 100° limited angle tomography. For synthetic test data, U-Net significantly reduces the root-mean-square error (RMSE) from 2.55 × 10
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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.