Evidence map›Paper›PMID 41874638›Full record

ReviewSkeletal radiology2026

Best fast MRI protocols for the knee: advantages and limitations.

Yannik Leonhardt, Jan Vosshenrich, Jan Fritz

Abstract readReview
PubMed Publisher
In one paragraph

Review 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.

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

3 authors.

Yannik LeonhardtDepartment of Diagnostic and Interventional Radiology, School of Medicine & Klinikum Rechts Der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany.
Jan VosshenrichDepartment of Radiology, University Hospital Basel, Basel, Switzerland.
Jan FritzDepartment of Radiology, New York University Grossman School of Medicine, New York, NY, USA. janfritz777@gmail.com.ORCID http://orcid.org/0000-0003-4456-3043

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Knee MRI plays a central role in musculoskeletal diagnostics but has traditionally been associated with relatively long acquisition times. Recent technological advances have fundamentally changed this paradigm. Parallel imaging (PI), simultaneous multi-slice acquisition (SMS), compressed sensing (CS), and combinations thereof have substantially reduced scan times without compromising diagnostic image quality. The introduction of deep learning (DL)-based reconstruction further elevates this transformative breakthrough, as it can reconstruct high-quality diagnostic MR images at higher acceleration factors, where conventional image reconstruction methods have traditionally struggled to succeed. Sixfold PIxSMS-accelerated DL protocols have demonstrated excellent diagnostic performance and image quality, allowing comprehensive knee MRI examinations to be completed in under five minutes. Accelerated three-dimensional (3D) TSE techniques, such as CAIPIRINHA-accelerated SPACE sequences, further expand the potential of knee MRI by enabling high-resolution isotropic 3D imaging at acquisition times that are increasingly practical for routine clinical use. Ongoing improvements in DL-based reconstruction and denoising may soon bridge the remaining gap, promising to enable the acquisition of isotropic 3D datasets with multiple contrasts within minutes. Beyond technical acceleration, the successful implementation of fast MRI requires careful workflow optimization and consideration of architectural and economic factors. This review outlines the technical principles underlying modern acceleration strategies, summarizes evidence from validation studies, discusses practical aspects of clinical implementation and protocol optimization, and highlights future opportunities and challenges.

Indexed as

Joint DiseasesKnee JointMagnetic Resonance ImagingDeep LearningHumansImage Interpretation, Computer-AssistedImaging, Three-DimensionalDeep learningDiagnostic performanceKnee MRIParallel imagingSimultaneous multislice

Identifiers

What OpenQuestion holds

Textmetadata
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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.