Evidence map›Paper›PMID 35574417›Full record

ArticleFrontiers in oncology2022

Sparse Angle CBCT Reconstruction Based on Guided Image Filtering.

Siyuan Xu, Bo Yang, Congcong Xu, Jiawei Tian, Yan Liu, Lirong Yin, Shan Liu, Wenfeng Zheng, Chao Liu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in oncology, 2022. 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. Low-dose CBCT image reconstruction: a review.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2026
    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

9 authors.

Siyuan XuSchool of Automation, University of Electronic Science and Technology of China, Chengdu, China.
Bo YangSchool of Automation, University of Electronic Science and Technology of China, Chengdu, China.
Congcong XuSchool of Automation, University of Electronic Science and Technology of China, Chengdu, China.
Jiawei TianSchool of Automation, University of Electronic Science and Technology of China, Chengdu, China.
Yan LiuSchool of Automation, University of Electronic Science and Technology of China, Chengdu, China.
Lirong YinDepartment of Geography and Anthropology, Louisiana State University, Baton Rouge, LA, United States.
Shan LiuSchool of Automation, University of Electronic Science and Technology of China, Chengdu, China.
Wenfeng ZhengSchool of Automation, University of Electronic Science and Technology of China, Chengdu, China.
Chao LiuLaboratoire d'Informatique, de Robotique et de Microélectronique de Montpellier (LIRMM), Unité Mixte de Recherche (UMR) 5506, French National Center for Scientific Research (CNRS) - University of Montpellier (UM), Montpellier, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cone-beam Computerized Tomography (CBCT) has the advantages of high ray utilization and detection efficiency, short scan time, high spatial and isotropic resolution. However, the X-rays emitted by CBCT examination are harmful to the human body, so reducing the radiation dose without damaging the reconstruction quality is the key to the reconstruction of CBCT. In this paper, we propose a sparse angle CBCT reconstruction algorithm based on Guided Image FilteringGIF, which combines the classic Simultaneous Algebra Reconstruction Technique(SART) and the Total p-Variation (TpV) minimization. Due to the good edge-preserving ability of SART and noise suppression ability of TpV minimization, the proposed method can suppress noise and artifacts while preserving edge and texture information in reconstructed images. Experimental results based on simulated and real-measured CBCT datasets show the advantages of the proposed method.

Indexed as

CBCT reconstructionguided image filteringradiation therapysimultaneous algebraic reconstruction techniquethe total p-Variation minimization

Identifiers

PMID35574417
PMCPMC9093219

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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.