Evidence map›Paper›PMID 24695846›Full record

ArticleJournal of biomedical optics2014

Numerical simulation of x-ray luminescence optical tomography for small-animal imaging.

Changqing Li, Arnulfo Martínez-Dávalos, Simon R Cherry

Abstract read
In one paragraph

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 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed.

  1. Superfast Scan of Focused X-Ray Luminescence Computed Tomography Imaging.IEEE access : practical innovations, open solutions · 2023
    Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. High-resolution x-ray luminescence computed tomography.Proceedings of SPIE--the International Society for Optical Engineering · 2020
    Article
  8. X-ray luminescence imaging for small animals.Proceedings of SPIE--the International Society for Optical Engineering · 2020
    Article
  9. Article
  10. Article
  11. Focused x-ray luminescence computed tomography: experimental studies.Proceedings of SPIE--the International Society for Optical Engineering · 2019
    Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Changqing LiUniversity of California, School of Engineering, Merced, Merced, California 95343.
Arnulfo Martínez-DávalosUniversidad Nacional Autónoma de México, Instituto de Física, A.P. 20-364, 01000 México D.F., Mexico.
Simon R CherryUniversity of California, Department of Biomedical Engineering, Davis, Davis, California 95616.

Funding

X-ray Luminescence Optical Tomography for Small Animal ImagingR21EB013828 · NIBIB · UNIVERSITY OF CALIFORNIA AT DAVIS · PI LI, CHANGQING · 2011 to 2012
$397k
NIBIB NIH HHS R21 EB013828
6 · The paper itself

Abstract

X-ray luminescence optical tomography (XLOT) is an emerging hybrid imaging modality in which x-ray excitable particles (phosphor particles) emit optical photons when stimulated with a collimated x-ray beam. XLOT can potentially combine the high sensitivity of optical imaging with the high spatial resolution of x-ray imaging. For reconstruction of XLOT data, we compared two reconstruction algorithms, conventional filtered backprojection (FBP) and a new algorithm, x-ray luminescence optical tomography with excitation priors (XLOT-EP), in which photon propagation is modeled with the diffusion equation and the x-ray beam positions are used as reconstruction priors. Numerical simulations based on dose calculations were used to validate the proposed XLOT imaging system and the reconstruction algorithms. Simulation results showed nanoparticle concentrations reconstructed with XLOT-EP are much less dependent on scan depth than those obtained with FBP. Measurements at just two orthogonal projections are sufficient for XLOT-EP to reconstruct an XLOT image for simple source distributions. The heterogeneity of x-ray energy deposition is included in the XLOT-EP reconstruction and improves the reconstruction accuracy, suggesting that there is a need to calculate the x-ray energy distribution for experimental XLOT imaging.

Indexed as

AlgorithmsComputer SimulationImage Processing, Computer-AssistedPhantoms, ImagingTomography, OpticalTomography, X-Ray Computed

Identifiers

PMID24695846
PMCPMC3973658

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.