Evidence map›Paper›PMID 41152657›Full record

ArticleJournal of imaging informatics in medicine2026

Impact of Super-Resolution Deep Learning Reconstruction on Low-Dose CT in Patients with Central Venous Catheter or Central Venous Port.

Koichiro Yasaka, Miyaka Wada, Kishin Tokuyama, Taku Takaishi, Osamu Abe

Abstract read
In one paragraph

Article in Journal of imaging informatics in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

5 authors.

Koichiro YasakaDepartment of Radiology, The University of Tokyo, Tokyo, Japan. koyasaka@gmail.com.ORCID http://orcid.org/0000-0002-0324-6562
Miyaka WadaDepartment of Radiology, The University of Tokyo, Tokyo, Japan.
Kishin TokuyamaDepartment of Radiology, The University of Tokyo, Tokyo, Japan.
Taku TakaishiDepartment of Radiology, The University of Tokyo, Tokyo, Japan.
Osamu AbeDepartment of Radiology, The University of Tokyo, Tokyo, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

CT imaging is useful for evaluating complications related to central venous catheter insertion; however, it involves exposure to ionizing radiation. This study aimed to evaluate the impact of super-resolution deep learning reconstruction (SR-DLR) on the quality of low-dose CT imaging compared to hybrid iterative reconstruction (HIR) in patients with central venous catheter or central venous port. In this retrospective study, chest CT images were reconstructed using SR-DLR and HIR from source data acquired with a three-dimensional landmark scan during scout imaging. Three readers independently assessed the quality of images in terms of noise, artifacts, depiction of the brachiocephalic vein and superior vena cava, and ease of evaluating hematoma and catheter location. The standard deviation of CT attenuation within a region of interest placed in the right atrium was recorded as quantitative image noise. All readers rated noise, artifacts, and depiction of the brachiocephalic vein as significantly improved in SR-DLR compared to HIR (p ≤ 0.042). Two out of three readers rated depiction of the superior vena cava, ease of evaluating hematoma, and catheter location as significantly improved in SR-DLR compared to HIR (p ≤ 0.016). Quantitative image noise in SR-DLR was 10.2 Hounsfield unit, which was significantly reduced compared to HIR (24.0 Hounsfield unit) (p < 0.001). When sufficient information can be obtained from these images, the main CT scans may be omitted. In conclusion, SR-DLR enhanced the quality of low-dose CT imaging compared to HIR in patients with central venous catheter or central venous port.

Indexed as

Catheterization, Central VenousCentral Venous CathetersDeep LearningRadiographic Image Interpretation, Computer-AssistedTomography, X-Ray ComputedAdultAgedArtifactsBrachiocephalic VeinsFemaleHumansMaleMiddle AgedRadiation DosageRetrospective StudiesCentral venous catheterCentral venous portCTRadiation doseSuper-resolution deep learning reconstruction

Identifiers

PMID41152657
PMCPMC13481595

What Socratic holds

Textmetadata
Read underepoch 390

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.