Evidence map›Paper›PMID 42247107›Full record

Trial reportEuropean radiology experimental2026

Zero echo time MRI with deep learning reconstruction and chemical shift correction for detecting osteolytic myeloma lesions.

Darius Lepot, Caroline Chabot, Gaëtan Duchêne, Sagar Mandava, Maggie Fung, Julie Poujol, Marie-Christiane Vekemans, Perrine Triqueneaux, Nina Watté, Olivier Gheysens and 2 more

Registry-linked trialAbstract readClinical Trial
In one paragraph

Trial report in European radiology experimental, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05381077 (Evaluation of Whole Body Examination by MRI Integrating the "Zero Time Eco" Sequence), which is not on this 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.

NCT05381077 naunknown statusnot on this map

Evaluation of Whole Body Examination by MRI Integrating the "Zero Time Eco" Sequence (ZTE, Pseudo-CT) for the Detection of Bone Lesions in Multiple Myeloma: Comparison With Pet / CT and Whole Body Scanner

TypeinterventionalSponsorCliniques universitaires Saint-Luc- Université Catholique de LouvainRan2021 to 2023Enrolled30ConditionsMultiple Myeloma, Bone MetastasesArmsMRI
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

12 authors.

Darius Lepot *Department of Medical Imaging, Institut de Recherche Expérimentale et Clinique (IREC), Institut du Cancer Roi Albert II, Cliniques Universitaires Saint Luc, Université catholique de Louvain (UCLouvain), Brussels, Belgium.
Caroline Chabot *Department of Medical Imaging, Institut de Recherche Expérimentale et Clinique (IREC), Institut du Cancer Roi Albert II, Cliniques Universitaires Saint Luc, Université catholique de Louvain (UCLouvain), Brussels, Belgium.
Gaëtan DuchêneDepartment of Medical Imaging, Institut de Recherche Expérimentale et Clinique (IREC), Institut du Cancer Roi Albert II, Cliniques Universitaires Saint Luc, Université catholique de Louvain (UCLouvain), Brussels, Belgium.
Sagar MandavaGE HealthCare MR Clinical Solutions, Atlanta, GA, USA.
Maggie FungGE HealthCare MR Clinical Solutions, New York, NY, USA.
Julie PoujolGE HealthCare, Buc, France.
Marie-Christiane VekemansDepartment of Hematology, Institut de Recherche Expérimentale et Clinique (IREC), Institut du Cancer Roi Albert II, Cliniques Universitaires Saint Luc, Université catholique de Louvain (UCLouvain), Brussels, Belgium.
Perrine TriqueneauxDepartment of Medical Imaging, Institut de Recherche Expérimentale et Clinique (IREC), Institut du Cancer Roi Albert II, Cliniques Universitaires Saint Luc, Université catholique de Louvain (UCLouvain), Brussels, Belgium.
Nina WattéDepartment of Medical Imaging, Institut de Recherche Expérimentale et Clinique (IREC), Institut du Cancer Roi Albert II, Cliniques Universitaires Saint Luc, Université catholique de Louvain (UCLouvain), Brussels, Belgium.
Olivier GheysensDepartment of Nuclear Medicine, Institut de Recherche Expérimentale et Clinique (IREC), Institut du Cancer Roi Albert II, Cliniques Universitaires Saint Luc, Université catholique de Louvain (UCLouvain), Brussels, Belgium.
Nicolas MichouxDepartment of Medical Imaging, Institut de Recherche Expérimentale et Clinique (IREC), Institut du Cancer Roi Albert II, Cliniques Universitaires Saint Luc, Université catholique de Louvain (UCLouvain), Brussels, Belgium.
Frédéric E LecouvetDepartment of Medical Imaging, Institut de Recherche Expérimentale et Clinique (IREC), Institut du Cancer Roi Albert II, Cliniques Universitaires Saint Luc, Université catholique de Louvain (UCLouvain), Brussels, Belgium. frederic.lecouvet@saintluc.uclouvain.be.ORCID http://orcid.org/0000-0003-3568-3254

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveWe compared three magnetic resonance imaging (MRI) sequences-native zero echo time (ZTE), deep learning (DL)-chemical shift correction (CSC) reconstructed ZTE (ZTE-DLCSC), and gradient-echo black bone (BB)-in detecting osteolytic multiple myeloma (MM) lesions, using computed tomography (CT) as reference. MATERIALS AND

methodsPatients who had undergone whole-body

resultsTen patients, aged 67 ± 12 years (mean ± standard deviation), were enrolled. Considering all regions, repeatability was at least moderate for ZTE (AC1 ≥ 0.45), good for ZTE-DLCSC and BB (AC1 ≥ 0.60) and very good for CT (AC1 ≥ 0.80). Reproducibility was at least fair for ZTE and BB (AC2 ≥ 0.20), good for ZTE-DLCSC (AC2 ≥ 0.60) and very good for CT (AC2 ≥ 0.80). Accuracy of ZTE-DLCSC ranged 80‒93%; compared to ZTE, accuracy increased by 23% and 25% for junior readers (p = 0.010 and p = 0.002 respectively) and by 32% for the senior reader (p < 0.001). ZTE-DLCSC detected more lesions than ZTE/BB (+30%, p = 0.011; +25%, p = 0.024).

conclusionDL-driven DLCSC reconstruction improves the reliability and accuracy of the ZTE sequence for detecting MM lesions. RELEVANCE STATEMENT: The combination of deep learning reconstruction and chemical shift correction improves the accuracy of zero echo time MRI for detecting myeloma lesions.

trial registrationThis study was approved by the institutional ethics committee and registered at ClinicalTrials.gov (NCT05381077). KEY POINTS: The performance of ZTE-DLCSC for detecting osteolytic MM lesions was compared with that of native ZTE and BB sequences using CT as reference. ZTE-DLCSC showed better intra-reader agreement (repeatability) and inter-reader agreement (reproducibility) than ZTE and BB sequences. ZTE-DLCSC had fewer false positives and false negatives than ZTE and BB sequences. ZTE-DLCSC detected more lesions than ZTE and BB sequences.

Indexed as

Deep LearningImage Processing, Computer-AssistedMagnetic Resonance ImagingMultiple MyelomaOsteolysisAgedFemaleHumansMaleMiddle AgedPositron Emission Tomography Computed TomographyProspective StudiesReproducibility of ResultsWhole Body ImagingArtificial intelligenceBoneDeep learningMagnetic resonance imagingMultiple myeloma

Identifiers

PMID42247107
PMCPMC13241374

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Registered trials

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.