Evidence map›Paper›PMID 41080365›Full record

ArticleCureus2025

Magnetic Resonance Imaging of Endolymphatic Hydrops Using Compressed Sensing Acceleration Technique.

Yukiyoshi Kimura, Ricardo Ceballos-Lizarraga, Dafne Soto-Trujillo, Cesar N Cristancho-Rojas

Abstract read
In one paragraph

Article in Cureus, 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
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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.

Yukiyoshi KimuraRadiology, Instituto Nacional De Ciencias Medicas y Nutricion Salvador Zubiran, Mexico City, MEX.
Ricardo Ceballos-LizarragaNeurology, Clinica de Vertigo y Mareo Santa Fe, Mexico City, MEX.
Dafne Soto-TrujilloRadiology, Centro Medico ABC, Mexico City, MEX.
Cesar N Cristancho-RojasRadiology, CT Scanner Mexico, Mexico City, MEX.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Endolymphatic hydrops (EH) is a key underlying pathologic alteration of Ménière's disease (MD). MD is characterized by recurrent episodes of spontaneous vertigo, fluctuating sensorineural hearing loss, tinnitus, and aural fullness. Magnetic resonance imaging (MRI) plays a crucial role in visualizing EH, with gadolinium-based contrast agents facilitating the identification of endolymph and perilymph. Specific MRI techniques, such as the hybrid of reversed image of positive endolymph signal and native image of positive perilymph signal (HYDROPS) imaging method, offer high-quality results but are limited by long acquisition times. This study explores the use of compressed sensing to accelerate image acquisition and improve contrast-to-noise ratio (CNR) in EH imaging in a 3 Tesla magnet using a 16-channel array head coil, comparing a new proposed cs-HYDROPS sequence with the conventional HYDROPS technique. A total of 24 ears from 12 patients with suspected MD were analyzed using both methods. The cs-HYDROPS sequence demonstrated a substantial reduction in scan time with a significant reduction in acquisition time of 57.3% (from 30.2 minutes to 12.9 minutes) while achieving a 143.5% increase in CNR (mean CNR for cs-HYDROPS: 103.8; SD: 37.3). Diagnostic agreement between the two methods had a Cohen's kappa value of 0.903. These findings demonstrate that cs-HYDROPS offers a reliable and efficient alternative for evaluating EH in MD patients, reducing scan time while enhancing image quality and maintaining diagnostic accuracy. This technique may facilitate wider clinical adoption of EH imaging in the routine workup of MD.

Indexed as

endolymphatic hydropshydropsmagnetic resonance (mr)meniere’s diseaseperipheral vertigo

Identifiers

PMID41080365
PMCPMC12511649

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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.