Evidence map›Paper›PMID 32445254›Full record

ArticleJournal of biophotonics2020

High-speed X-ray-induced luminescence computed tomography.

Xianjin Dai, Kai Cheng, Wei Zhao, Lei Xing

Abstract read
In one paragraph

Article in Journal of biophotonics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

4 authors.

Xianjin DaiDepartment of Radiation Oncology, Stanford University, Stanford, California, USA.
Kai ChengDepartment of Radiation Oncology, Stanford University, Stanford, California, USA.
Wei ZhaoDepartment of Radiation Oncology, Stanford University, Stanford, California, USA.
Lei XingDepartment of Radiation Oncology, Stanford University, Stanford, California, USA.

Funding

Dual Modality X-ray Luminescence CT for in vivo Cancer ImagingR01CA227713 · NCI · STANFORD UNIVERSITY · PI XING, LEI · 2019 to 2023
$2.9M
Radioluminescence dosimetry solution for precision radiation therapyR01CA223667 · NCI · STANFORD UNIVERSITY · PI XING, LEI · 2018 to 2022
$2.4M
NCI NIH HHS 1R01CA223667; 1R01CA227713NCI NIH HHS R01 CA223667NCI NIH HHS R01 CA227713
6 · The paper itself

Abstract

X-ray-induced luminescence computed tomography (XLCT) is an emerging molecular imaging. Challenges in improving spatial resolution and reducing the scan time in a whole-body field of view (FOV) still remain for practical in vivo applications. In this study, we present a novel XLCT technique capable of obtaining three-dimensional (3D) images from a single snapshot. Specifically, a customed two-planar-mirror component is integrated into a cone beam XLCT imaging system to obtain multiple optical views of an object simultaneously. Furthermore, a compressive sensing based algorithm is adopted to improve the efficiency of 3D XLCT image reconstruction. Numerical simulations and experiments were conducted to validate the single snapshot X-ray-induced luminescence computed tomography (SS-XLCT). The results show that the 3D distribution of the nanophosphor targets can be visualized much faster than conventional cone beam XLCT imaging method that was used in our comparisons while maintaining comparable spatial resolution as in conventional XLCT imaging. SS-XLCT has the potential to harness the power of XLCT for rapid whole-body in vivo molecular imaging of small animals.

Indexed as

Image Processing, Computer-AssistedLuminescenceAlgorithmsAnimalsPhantoms, ImagingTomography, X-Ray ComputedX-Rayscomputed tomographyluminescenceXLCTX-ray

Identifiers

PMID32445254
PMCPMC7598839

What OpenQuestion holds

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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.