Evidence map›Paper›PMID 39470733›Full record

ArticleEpilepsia2024

Proof of concept: Portable ultra-low-field magnetic resonance imaging for the diagnosis of epileptogenic brain pathologies.

Tobias Bauer, Simon Olbrich, Anne Groteklaes, Nils Christian Lehnen, Mousa Zidan, Annalena Lange, Justus Bisten, Lennart Walger, Jennifer Faber, Walter Bruchhausen and 6 more

Abstract read
In one paragraph

Article in Epilepsia, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
–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

11 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  5. Article
  6. AI in epilepsy neuroimaging.Current opinion in neurology · 2026
    Review
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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

16 authors.

Tobias BauerDepartment of Neuroradiology, University Hospital Bonn, Bonn, Germany.ORCID https://orcid.org/0000-0002-0555-6214
Simon OlbrichDepartment of Neuroradiology, University Hospital Bonn, Bonn, Germany.
Anne GroteklaesDepartment of Neonatology and Pediatric Intensive Care, University Hospital Bonn, Bonn, Germany.
Nils Christian LehnenDepartment of Neuroradiology, University Hospital Bonn, Bonn, Germany.
Mousa ZidanDepartment of Neuroradiology, University Hospital Bonn, Bonn, Germany.
Annalena LangeDepartment of Neuroradiology, University Hospital Bonn, Bonn, Germany.
Justus BistenDepartment of Neuroradiology, University Hospital Bonn, Bonn, Germany.
Lennart WalgerDepartment of Neuroradiology, University Hospital Bonn, Bonn, Germany.ORCID https://orcid.org/0000-0002-3300-6877
Jennifer FaberGerman Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.
Walter BruchhausenSection for Global Health, Institute for Hygiene and Public Health, University Hospital Bonn, Bonn, Germany.
Philipp VollmuthDepartment of Neuroradiology, University Hospital Bonn, Bonn, Germany.
Ulrich HerrlingerDepartment of Neurology, University Hospital Bonn, Bonn, Germany.
Alexander RadbruchDepartment of Neuroradiology, University Hospital Bonn, Bonn, Germany.
Rainer SurgesDepartment of Epileptology, University Hospital Bonn, Bonn, Germany.
Hemmen SabirGerman Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.
Theodor RüberDepartment of Neuroradiology, University Hospital Bonn, Bonn, Germany.ORCID https://orcid.org/0000-0002-6180-7671

Funding

Neuro-aCSis Bonn Neuroscience Clinician Scientist Program 2024-12-07
6 · The paper itself

Abstract

objectiveHigh-field magnetic resonance imaging (MRI) is a standard in the diagnosis of epilepsy. However, high costs and technical barriers have limited adoption in low- and middle-income countries. Even in high-income nations, many individuals with epilepsy face delays in undergoing MRI. Recent advancements in ultra-low-field (ULF) MRI technology, particularly the development of portable scanners, offer a promising solution to the limited accessibility of MRI. In this study, we present and evaluate the imaging capability of ULF MRI in detecting structural abnormalities typically associated with epilepsy and compare it to high-field MRI at 3 T.

methodsData collection was conducted within 3 consecutive weeks at the University Hospital Bonn. Inclusion criteria were a minimum age of 18 years, diagnosed epilepsy, and clinical high-field MRI with abnormalities. We used a .064 T Swoop portable MR Imaging System. Both high-field MRI and ULF MRI scans were evaluated independently by two experienced neuroradiologists as part of their clinical routine, comparing pathology detection and diagnosis completeness.

resultsTwenty-three individuals with epilepsy were recruited. One subject presented with a dual pathology. Across the entire cohort, in 17 of 24 (71%) pathologies, an anomaly colocalizing with the actual lesion was observed on ULF MRI. For 11 of 24 (46%) pathologies, the full diagnosis could be made based on ULF MRI. Tumors and posttraumatic lesions could be diagnosed best on ULF MRI, whereas cortical dysplasia and other focal pathologies were the least well diagnosed. SIGNIFICANCE: This single-center series of individuals with epilepsy demonstrates the feasibility and utility of ULF MRI for the field of epileptology. Its integration into epilepsy care offers transformative potential, particularly in resource-limited settings. Further research is needed to position ULF MRI within imaging modalities in the diagnosis of epilepsy.

Indexed as

EpilepsyMagnetic Resonance ImagingAdolescentAdultBrainFemaleHumansMaleMiddle AgedProof of Concept StudyYoung AdultEpilepsyGlobal Epileptologylimited ressourcesNeuroimagingSustainability

Identifiers

PMID39470733
PMCPMC11647431

What OpenQuestion holds

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LicenceCC BY-NC
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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.