Evidence map›Paper›PMID 42704461›Full record

ArticleClinical neuroradiology2026

AI-Assisted Compressed Sensing for Optimized Inner Ear Imaging: a Multi-Rater MRI Evaluation of CSAI T2-DRIVE.

Enrike Rosenkranz, Jennifer Köhler, Nora M Weiss, Martin Renz, Laura Leukert, Kilian Weiss, Barbara Wollenberg, Dennis M Hedderich, Jan S Kirschke, Wilhelm Wimmer and 1 more

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Article in Clinical neuroradiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

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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

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No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Enrike RosenkranzInstitute of Diagnostic and Interventional Neuroradiology, TUM University Hospital, Rechts der Isar Hospital, Munich, Germany. enrike.rosenkranz@tum.de.
Jennifer KöhlerDepartment of Otorhinolaryngology, Head and Neck Surgery, TUM University Hospital, Rechts der Isar Hospital, Munich, Germany.
Nora M WeissDepartment of Otorhinolaryngology, Head and Neck Surgery, TUM University Hospital, Rechts der Isar Hospital, Munich, Germany.
Martin RenzInstitute of Diagnostic and Interventional Neuroradiology, TUM University Hospital, Rechts der Isar Hospital, Munich, Germany.ORCID https://orcid.org/0000-0001-6644-5342
Laura LeukertInstitute of Diagnostic and Interventional Neuroradiology, TUM University Hospital, Rechts der Isar Hospital, Munich, Germany.
Kilian WeissPhilips (Germany), Hamburg, Germany.
Barbara WollenbergDepartment of Otorhinolaryngology, Head and Neck Surgery, TUM University Hospital, Rechts der Isar Hospital, Munich, Germany.
Dennis M HedderichInstitute of Diagnostic and Interventional Neuroradiology, TUM University Hospital, Rechts der Isar Hospital, Munich, Germany.
Jan S KirschkeInstitute of Diagnostic and Interventional Neuroradiology, TUM University Hospital, Rechts der Isar Hospital, Munich, Germany.
Wilhelm WimmerDepartment of Otorhinolaryngology, Head and Neck Surgery, TUM University Hospital, Rechts der Isar Hospital, Munich, Germany.ORCID https://orcid.org/0000-0001-5392-2074
Jannis BoddenInstitute of Diagnostic and Interventional Neuroradiology, TUM University Hospital, Rechts der Isar Hospital, Munich, Germany.ORCID https://orcid.org/0000-0001-5997-203X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeHigh-resolution T2-weighted imaging is essential for preoperative assessment before cochlear implantation. Compressed sensing (CS) with AI-based reconstruction (CSAI) reduces acquisition times whilst preserving image quality. Although CSAI has been established in various clinical applications, its performance in inner ear imaging remains unclear. This study assesses CSAI-optimized T2-DRIVE sequences at different resolutions and acquisition times for visualizing inner ear structures.

methodsIn 30 healthy participants, CS T2-DRIVE was acquired at isotropic resolutions of 0.65, 0.5, and 0.4 mm. 0.5 and 0.4 mm datasets were also reconstructed using a commercially available AI-based reconstruction algorithm. Three raters independently assessed the imaging quality of anatomical landmarks (cochlea, semicircular canals, vestibulocochlear nerve), artifacts, and signal-to-noise ratio (SNR) using a 5-point Likert scale. Each rater re-rated a subset of images after ≥ 4 weeks. Inter- and intra-rater reliability were calculated using quadratically weighted Cohen's kappa, and differences between sequences were analyzed using cumulative link mixed models (CLMM).

results0.4 mm isotropic imaging exhibited lower SNR compared to CSAI 0.5 mm, regardless of reconstruction algorithm (p < 0.001). Across all raters, CSAI T2 at 0.5 mm resolution significantly improved delineation of the cochlea and vestibulocochlear nerve compared to 0.65 mm imaging (p < 0.001), while assessability of semicircular canals was reduced (p = 0.082). Acquisition times increased with higher resolutions (0.65/0.5/0.4 mm: 4:02/4:25/4:34 min).

conclusionAI-driven reconstruction algorithms enable statistically significant improvements in imaging of key inner-ear structures with minimal increases in scan time at 0.5 mm resolution.

Indexed as

CochleaDeep learning reconstructionImage quality assessmentMagnetic resonance imagingT2-weighted imaging

Identifiers

PMID42704461

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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.