Evidence map›Paper›PMID 38864874›Full record

ArticleRadiologie (Heidelberg, Germany)2024

[Accelerated musculoskeletal magnetic resonance imaging with deep learning-based image reconstruction at 0.55 T-3 T].

Jan Vosshenrich, Jan Fritz

Abstract readEnglish Abstract
In one paragraph

Article in Radiologie (Heidelberg, Germany), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

2 authors.

Jan VosshenrichDepartment of Radiology, Grossman School of Medicine, New York University, 660 First Avenue, 10016, New York, NY, USA. jan.vosshenrich@usb.ch.
Jan FritzDepartment of Radiology, Grossman School of Medicine, New York University, 660 First Avenue, 10016, New York, NY, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

clinical/methodical issueMagnetic resonance imaging (MRI) is a central component of musculoskeletal imaging. However, long image acquisition times can pose practical barriers in clinical practice. STANDARD RADIOLOGICAL

methodsMRI is the established modality of choice in the diagnostic workup of injuries and diseases of the musculoskeletal system due to its high spatial resolution, excellent signal-to-noise ratio (SNR), and unparalleled soft tissue contrast. METHODOLOGICAL INNOVATIONS: Continuous advances in hardware and software technology over the last few decades have enabled four-fold acceleration of 2D turbo-spin-echo (TSE) without compromising image quality or diagnostic performance. The recent clinical introduction of deep learning (DL)-based image reconstruction algorithms helps to minimize further the interdependency between SNR, spatial resolution and image acquisition time and allows the use of higher acceleration factors. PERFORMANCE: The combined use of advanced acceleration techniques and DL-based image reconstruction holds enormous potential to maximize efficiency, patient comfort, access, and value of musculoskeletal MRI while maintaining excellent diagnostic accuracy. ACHIEVEMENTS: Accelerated MRI with DL-based image reconstruction has rapidly found its way into clinical practice and proven to be of added value. Furthermore, recent investigations suggest that the potential of this technology does not yet appear to be fully harvested. PRACTICAL RECOMMENDATIONS: Deep learning-reconstructed fast musculoskeletal MRI examinations can be reliably used for diagnostic work-up and follow-up of musculoskeletal pathologies in clinical practice.

Indexed as

Deep LearningMagnetic Resonance ImagingMusculoskeletal DiseasesHumansImage Processing, Computer-AssistedMusculoskeletal SystemAccelerationArtificial intelligenceCompressed sensingImage reconstructionMusculoskeletal imaging

Identifiers

PMID38864874
PMCPMC11422270

What OpenQuestion holds

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Registered trials

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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.