Evidence map›Paper›PMID 40970959›Full record

ArticleNeuroradiology2025

Deep learning-based acceleration and denoising of 0.55T MRI for enhanced conspicuity of vestibular Schwannoma post contrast administration.

Maximilian Hinsen, Armin Nagel, Rafael Heiss, Matthias May, Marco Wiesmueller, Claudius Mathy, Martin Zeilinger, Joachim Hornung, Sarina Mueller, Michael Uder and 1 more

Abstract read
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. 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

11 authors.

Maximilian HinsenDepartment of Radiology, Universitätsklinikum Erlangen, Germany. maximilian.hinsen@uk-erlangen.de.
Armin NagelDepartment of Radiology, Universitätsklinikum Erlangen, Germany.
Rafael HeissDepartment of Radiology, Universitätsklinikum Erlangen, Germany.
Matthias MayDepartment of Radiology, Universitätsklinikum Erlangen, Germany.
Marco WiesmuellerDepartment of Radiology, Universitätsklinikum Erlangen, Germany.
Claudius MathyDepartment of Radiology, Universitätsklinikum Erlangen, Germany.
Martin ZeilingerDepartment of Radiology, Universitätsklinikum Erlangen, Germany.
Joachim HornungDepartment of Otorhinolaryngology, Head and Neck Surgery, Universitätsklinikum Erlangen, Germany.
Sarina MuellerDepartment of Otorhinolaryngology, Head and Neck Surgery, Universitätsklinikum Erlangen, Germany.
Michael UderDepartment of Radiology, Universitätsklinikum Erlangen, Germany.
Markus KoppDepartment of Radiology, Universitätsklinikum Erlangen, Germany. markus.kopp@uk-erlangen.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

background and purposeDeep-learning (DL) based MRI denoising techniques promise improved image quality and shorter examination times. This advancement is particularly beneficial for 0.55T MRI, where the inherently lower signal-to-noise (SNR) ratio can compromise image quality. Sufficient SNR is crucial for the reliable detection of vestibular schwannoma (VS). The objective of this study is to evaluate the VS conspicuity and acquisition time (TA) of 0.55T MRI examinations with contrast agents using a DL-denoising algorithm. MATERIALS AND

methodsFrom January 2024 to October 2024, we retrospectively included 30 patients with VS (9 women). We acquired a clinical reference protocol of the cerebellopontine angle containing a T1w fat-saturated (fs) axial (number of signal averages [NSA] 4) and a T1w Spectral Attenuated Inversion Recovery (SPAIR) coronal (NSA 2) sequence after contrast agent (CA) application without advanced DL-based denoising (w/o DL). We reconstructed the T1w fs CA sequence axial and the T1w SPAIR CA coronal with full DL-denoising mode without change of NSA, and secondly with 1 NSA for T1w fs CA axial and T1w SPAIR coronal (DL&1NSA). Each sequence was rated on a 5-point Likert scale (1: insufficient, 3: moderate, clinically sufficient; 5: perfect) for: overall image quality; VS conspicuity, and artifacts. Secondly, we analyzed the reliability of the size measurements. Two radiologists specializing in head and neck imaging performed the reading and measurements. The Wilcoxon Signed-Rank Test was used for non-parametric statistical comparison.

resultsThe DL&4NSA axial/coronal study sequence achieved the highest overall IQ (median 4.9). The image quality (IQ) for DL&1NSA was higher (M: 4.0) than for the reference sequence (w/o DL; median 4.0 versus 3.5, each p < 0.01). Similarly, the VS conspicuity was best for DL&4NSA (M: 4.9), decreased for DL&1NSA (M: 4.1), and was lower but still sufficient for w/o DL (M: 3.7, each p < 0.01). The TA for the axial and coronal post-contrast sequences was 8:59 minutes for DL&4NSA and w/o DL and decreased to 3:24 minutes with DL&1NSA.

conclusionsThis study underlines that advanced DL-based denoising techniques can reduce the examination time by more than half while simultaneously improving image quality.

Indexed as

Contrast MediaDeep LearningImage Interpretation, Computer-AssistedMagnetic Resonance ImagingNeuroma, AcousticAdultAgedFemaleHumansMaleMiddle AgedRetrospective StudiesSignal-To-Noise RatioContrast Media0.55T MRIDeep learning denoising of MRIHead and neck imagingLow field MRIMagnetic resonance imagingVestibular schwannoma

Identifiers

PMID40970959
PMCPMC12847088

What OpenQuestion holds

Textmetadata
LicenceCC BY
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 OpenQuestion graph.