Evidence map›Paper›PMID 41557752›Full record

ArticlePloS one2026

Multi-resolution tone mapping for high dynamic range medical ultrasound images.

Thi Lan Nhi Vu, Vimal Chandran, Christina Haberl, Otmar Scherzer, Julia Binder

Abstract read
In one paragraph

Article in PloS one, 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

5 authors.

Thi Lan Nhi VuFaculty of Mathematics, University of Vienna, Vienna, Austria.ORCID https://orcid.org/0009-0004-3457-0201
Vimal ChandranChristian Doppler Laboratory for Mathematical Modeling and Simulation of Next Generations of Ultrasound Devices (MaMSi), Vienna, Austria.ORCID https://orcid.org/0000-0002-2331-776X
Christina HaberlChristian Doppler Laboratory for Mathematical Modeling and Simulation of Next Generations of Ultrasound Devices (MaMSi), Vienna, Austria.ORCID https://orcid.org/0000-0003-3557-5050
Otmar ScherzerFaculty of Mathematics, University of Vienna, Vienna, Austria.
Julia BinderChristian Doppler Laboratory for Mathematical Modeling and Simulation of Next Generations of Ultrasound Devices (MaMSi), Vienna, Austria.ORCID https://orcid.org/0000-0002-5725-9270

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ultrasound diagnostics is a key tool in obstetrics for detecting fetal anomalies and monitoring pregnancy, but image quality often declines in obese patients due to reduced contrast resolution. This pilot study develops and preliminarily validates a novel tone-mapping algorithm for enhancing contrast resolution in high dynamic range (HDR) ultrasound images. The method employs multi-resolution fusion with depth-adaptive weighting and depth compensation to improve contrast, enhance tissue differentiation, reduce noise, and preserve fine details. The algorithm was tested on 20 fetal ultrasound images focused on fetal kidney visualization. Quantitative evaluation showed a 5.4% mean increase in entropy and mean generalized contrast-to-noise ratio (gCNR) improvements of 15.79%, 8.93%, and 17.39% between fetal kidneys and amniotic fluid, far-field objects and fluid, and fetal kidneys and adjacent tissues, respectively, compared with an existing method. These results demonstrate improved anatomical visualization, particularly of fetal kidneys, with potential clinical relevance.

Indexed as

Image Processing, Computer-AssistedKidneyUltrasonography, PrenatalAlgorithmsFemaleFetusHumansPilot ProjectsPregnancySignal-To-Noise Ratio

Identifiers

PMID41557752
PMCPMC12818761

What OpenQuestion holds

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LicenceCC BY
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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.