Evidence map›Paper›PMID 41299300›Full record

Observational studyBMC medical imaging2025

Qualitative and quantitative assessment of accelerated liver diffusion-weighted imaging using deep-learning reconstruction in oncologic patients.

Mihaela Rata, Francesca Castagnoli, Joshua Shur, Emily Evans, Georgina Hopkinson, Thomas Benkert, Elisabeth Weiland, Dow-Mu Koh, Jessica M Winfield

Registry-linked trialAbstract readObservational Study
In one paragraph

Observational study in BMC medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05118555 (Evaluation of New Magnetic Resonance Techniques), 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.

NCT05118555 recruitingnot on this map

Evaluation of New Magnetic Resonance Techniques

TypeobservationalSponsorRoyal Marsden NHS Foundation TrustRan2021 to 2031Enrolled2,850ConditionsMagnetic Resonance Imaging
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

9 authors.

Mihaela Rata *Department of Radiology, MRI Unit, The Royal Marsden NHS Foundation Trust, London, UK. Mihaela.Rata@icr.ac.uk.ORCID 0000-0003-4999-8038
Francesca Castagnoli *Department of Radiology, MRI Unit, The Royal Marsden NHS Foundation Trust, London, UK.
Joshua ShurDepartment of Radiology, MRI Unit, The Royal Marsden NHS Foundation Trust, London, UK.
Emily EvansDepartment of Radiology, MRI Unit, The Royal Marsden NHS Foundation Trust, London, UK.
Georgina HopkinsonDepartment of Radiology, MRI Unit, The Royal Marsden NHS Foundation Trust, London, UK.
Thomas BenkertResearch and Clinical Translation, Magnetic Resonance, Siemens Healthineers AG, Erlangen, Germany.
Elisabeth WeilandResearch and Clinical Translation, Magnetic Resonance, Siemens Healthineers AG, Erlangen, Germany.
Dow-Mu KohDepartment of Radiology, MRI Unit, The Royal Marsden NHS Foundation Trust, London, UK.
Jessica M WinfieldDepartment of Radiology, MRI Unit, The Royal Marsden NHS Foundation Trust, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDeep-learning (DL) reconstructions could improve image quality and reduce acquisition time in diffusion-weighted imaging (DWI). This study assessed, qualitatively and quantitatively, DL-DWI in liver metastasis of colorectal cancer patients.

methodsThis prospective study enrolled 50 participants from June to November 2022. Phantom and participant data were acquired on a 1.5T MR scanner using a free-breathing DL-DWI research application sequence. Three DWIs were compared: a moderately-accelerated DL-DWI (DL-1), a corresponding standard reconstruction (Standard-1) and a highly-accelerated DL-DWI (DL-2). Image quality (four features on b750 images and one feature on ADC map) was assessed by two radiologists. Region of interest (ROI) based ADC measurements were performed at three locations: liver, spleen, liver metastasis. Across the three series, median scores and ADC values were assessed using a Friedman non-parametric test and post-hoc analysis (pairwise Wilcoxon tests with Bonferroni correction). A p-value < 0.05 was considered statistically significant.

resultsFifty participants with metastatic colorectal cancer (mean age 62 years, range 36-88 years, 26 males) were evaluated. ROIs were delineated in liver (N = 50), spleen (N = 48), and liver metastasis (N = 11). Qualitatively, across both readers, DL-1 method received the highest scores for 5/8 features on the b750 images; all methods scored similarly on ADC maps for both readers. Quantitatively, ADCs were significantly different between DL-1 and Standard-1 series across all three organs, with DL-1-based ADC always higher (p < 0.01). This ADC increase was small: 8.9% (liver), 3.4% (spleen), 4.5% (liver metastasis).

conclusionsThis study suggests that a DL-based reconstruction is a promising technique to enable acceleration of liver DWI considering both qualitative and quantitative results.

trial registrationNCT05118555 (Evaluation of New Magnetic Resonance Techniques); study date of registration (first submitted: 2021-10-18).

Indexed as

Colorectal NeoplasmsDeep LearningDiffusion Magnetic Resonance ImagingImage Interpretation, Computer-AssistedImage Processing, Computer-AssistedLiver NeoplasmsAdultAgedAged, 80 and overFemaleHumansLiverMaleMiddle AgedProspective StudiesDeep learning reconstructionDiffusion magnetic resonance imagingLiverMetastasis

Identifiers

PMID41299300
PMCPMC12659593

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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.