Evidence map›Paper›PMID 40332600›Full record

ArticleNeuroradiology2025

CBCT-to-CT synthesis with a hybrid of CycleGAN and latent diffusion.

Feng Luo, Chaoyu Ma, Juntian Shi, Kunyuan Xu

Abstract read
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In one paragraph

Article in Neuroradiology, 2025. 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. Deep Learning for CT Synthesis in Radiotherapy.Bioengineering (Basel, Switzerland) · 2025
    Review
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.

Feng LuoSouth China Normal University, Guangzhou, China.
Chaoyu MaSouth China Normal University, Guangzhou, China.
Juntian ShiSun Yat-sen Memorial Hospital, Guangzhou, China. shijunt@mail.sysu.edu.com.cn.
Kunyuan XuSouth China Normal University, Guangzhou, China. xuky@scnu.edu.cn.

Funding

Guangdong Provincial Key Laboratory 2020B1212060066
6 · The paper itself

Abstract

introductionCone-beam computed tomography (CBCT) is instrumental in image-guided radiation therapy (IGRT), providing low radiation exposure while continuously monitoring anatomical structures for accurate dose estimation and treatment. Despite these advantages, CBCT inherently suffers from lower image quality and more frequent artifacts compared to computed tomography (CT), significantly undermining its effectiveness in IGRT. These drawbacks are especially pronounced in the pelvic region, where anatomical variability and dataset asymmetry challenge traditional image translation techniques like diffusion and CycleGAN networks.

methodsTo overcome these limitations, we propose CycleDiffSmoothGAN(CDSGAN), an innovative framework that enhances CBCT images by integrating CycleGAN with latent diffusion techniques and high-frequency detail preservation.This approach effectively blends features in the latent space, enabling smoother transitions between CBCT and synthetic CT (sCT) images.

resultsThe implementation of CDSGAN has shown superior performance, significantly outperforming existing technologies across crucial imaging metrics such as MAE (

conclusionThe research findings have substantiated the promising potential of CDSGAN in enhancing image quality for clinical applications.

Indexed as

Cone-Beam Computed TomographyRadiographic Image Interpretation, Computer-AssistedRadiotherapy, Image-GuidedTomography, X-Ray ComputedAlgorithmsArtifactsHumansCBCT-to-CT synthesisFeature blendingHigh-frequency preservationLatent diffusion

Identifiers

PMID40332600

What Socratic 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 Socratic graph.