Evidence map›Paper›PMID 41348234›Full record

ReviewStrahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]2026

Low-dose CBCT image reconstruction: a review.

Jiangyuan Shi, Ying Song, Guangjun Li, Sen Bai

Abstract readReview
PubMed Publisher
In one paragraph

Review in Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al], 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Jiangyuan ShiRadiophysical Technology Center, Cancer Center, West China Hospital, Sichuan University, No. 37 Guoxue Alley, 610065, Chengdu, China.
Ying SongRadiophysical Technology Center, Cancer Center, West China Hospital, Sichuan University, No. 37 Guoxue Alley, 610065, Chengdu, China. songying_Wch@163.com.
Guangjun LiRadiophysical Technology Center, Cancer Center, West China Hospital, Sichuan University, No. 37 Guoxue Alley, 610065, Chengdu, China.
Sen BaiRadiophysical Technology Center, Cancer Center, West China Hospital, Sichuan University, No. 37 Guoxue Alley, 610065, Chengdu, China.

Funding

National Natural Science Foundation of China 12475348Sichuan Province Science and Technology Support Program 2024NSFSC1789
6 · The paper itself

Abstract

Cone-beam computed tomography (CBCT) is a critical imaging modality in various medical fields, yet its repeated use poses radiation risks to patients. Low-dose CBCT image reconstruction aims to mitigate these risks while preserving image quality, which is crucial for clinical diagnosis and treatment. This review paper provides an in-depth analysis of the latest research progress in low-dose CBCT image reconstruction. We explore analytical reconstruction algorithms, iterative reconstruction algorithms, and deep learning approaches, each with distinct characteristics and applications. The paper comprehensively reviews the methods used for dose reduction in CBCT, the evolution of reconstruction algorithms, and their performance evaluations. We also identify challenges and limitations in current techniques, discussing potential future directions for low-dose CBCT reconstruction. Through a systematic literature search and analysis, this review offers a valuable reference for researchers and clinicians alike, aiming to advance the field of CBCT and enhance patient care through reduced radiation exposure and improved imaging outcomes.

Indexed as

Cone-Beam Computed TomographyImage Processing, Computer-AssistedRadiation DosageAlgorithmsDeep LearningHumansAnalytical reconstructionDeep learningImage processingImage quality enhancementIterative reconstruction

Identifiers

PMID41348234

What OpenQuestion holds

Textmetadata
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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.