Evidence map›Paper›PMID 34733793›Full record

ArticleFrontiers in oncology2021

Bioluminescence Tomography Based on One-Dimensional Convolutional Neural Networks.

Jingjing Yu, Chenyang Dai, Xuelei He, Hongbo Guo, Siyu Sun, Ying Liu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in oncology, 2021. 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. Article
  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

6 authors.

Jingjing YuSchool of Physics and Information Technology, Shaanxi Normal University, Xi'an, China.
Chenyang DaiSchool of Physics and Information Technology, Shaanxi Normal University, Xi'an, China.
Xuelei HeSchool of Information Sciences and Technology, Northwest University, Xi'an, China.
Hongbo GuoSchool of Information Sciences and Technology, Northwest University, Xi'an, China.
Siyu SunSchool of Physics and Information Technology, Shaanxi Normal University, Xi'an, China.
Ying LiuSchool of Physics and Information Technology, Shaanxi Normal University, Xi'an, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Bioluminescent tomography (BLT) has increasingly important applications in preclinical studies. However, the simplified photon propagation model and the inherent ill-posedness of the inverse problem limit the quality of BLT reconstruction. In order to improve the reconstruction accuracy of positioning and reconstruction efficiency, this paper presents a deep-learning optical reconstruction method based on one-dimensional convolutional neural networks (1DCNN). The nonlinear mapping relationship between the surface photon flux density and the distribution of the internal bioluminescence sources is directly established, which fundamentally avoids solving the ill-posed inverse problem iteratively. Compared with the previous reconstruction method based on multilayer perceptron, the training parameters in the 1DCNN are greatly reduced and the learning efficiency of the model is improved. Simulations verify the superiority and stability of the 1DCNN method, and the

Indexed as

bioluminescent tomography (BLT)convolutional neural networksdeep learninginverse problemoptical reconstruction

Identifiers

PMID34733793
PMCPMC8558399

What OpenQuestion holds

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Registered trials

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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.