Evidence map›Paper›PMID 41109866›Full record

ArticleSkeletal radiology2026

Real-world diagnostic performance of knee MRI protocols accelerated using simultaneous multi-slice acquisition and deep learning reconstruction.

Patricia M Johnson, Siddhant Dogra, Malte Westerhoff, Jan Fritz, Dana J Lin, Michael P Recht

Abstract read
In one paragraph

Article in Skeletal radiology, 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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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Patricia M JohnsonBernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA. patricia.johnson3@nyulangone.org.ORCID http://orcid.org/0000-0003-1547-9969
Siddhant DograBernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA.
Malte WesterhoffVisage Imaging, GmbH, Berlin, Germany.
Jan FritzBernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA.
Dana J LinBernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA.
Michael P RechtBernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA.

Funding

TR&D 4: Revealing Microstructure: Biophysical modeling and validation for discovery and clinical careP41EB017183 · NIBIB · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI Hersh Chandarana · 2014 to 2026
$19.3M
NIBIB NIH HHS EB017183NIBIB NIH HHS P41 EB017183
6 · The paper itself

Abstract

objectiveTo assess whether accelerated knee MRI protocols using simultaneous multi-slice (SMS) and deep learning reconstruction (DLR) are non-inferior to a conventional parallel imaging protocol for detecting internal derangement injuries.

methodsThis retrospective cohort study included 1055 patients who underwent knee MRI followed by arthroscopy within 180 days. Patients were scanned using either a conventional protocol (n = 226), an accelerated SMS protocol (n = 406), or a SMS with DLR protocol (n = 423). Each group included consecutive exams. Imaging was performed on 3 T MRI using five standardized two-dimensional turbo spin echo sequences. Radiology interpretations were compared with arthroscopy (reference standard) for anterior cruciate ligament (ACL), medial meniscus (MM), and lateral meniscus (LM) tears. Sensitivity and specificity were calculated with 95% confidence intervals using non-parametric bootstrapping. Non-inferiority was concluded if the upper bound of the 95% confidence interval for the difference in sensitivity and specificity was ≤ 0.05.

resultsAmong all patients, 666 had MM tears, 417 had LM tears, and 220 had ACL tears. Sensitivity for ACL tears was higher with accelerated protocols (0.96 and 0.98) than the conventional (0.85), with non-inferiority confirmed. Specificity was ≥ 0.98 across all protocols. MM sensitivity (0.94-0.95) met non-inferiority criteria. MM specificity (0.88-0.91) and LM sensitivity (0.63-0.68) were not statistically different across protocols but did not meet the non-inferiority margin. LM specificity (0.94) met non-inferiority criteria.

conclusionAccelerated MRI protocols using SMS and DLR demonstrated comparable diagnostic performance to the reference protocol. Although not all metrics met the strict non-inferiority margin, none showed statistically significant reductions in sensitivity or specificity. These findings support the clinical adoption of accelerated protocols for faster, high-throughput knee imaging.

Indexed as

Anterior Cruciate Ligament InjuriesDeep LearningImage Interpretation, Computer-AssistedKnee InjuriesMagnetic Resonance ImagingTibial Meniscus InjuriesAdolescentAdultArthroscopyFemaleHumansMaleMiddle AgedRetrospective StudiesSensitivity and SpecificityDeep learning reconstructionKnee MRISimultaneous multi-slice

Identifiers

PMID41109866
PMCPMC13015831

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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.