Evidence map›Paper›PMID 40469203›Full record

ArticleJournal of medical imaging (Bellingham, Wash.)2025

Deep learning-based temporal MR image reconstruction for accelerated interventional imaging during in-bore biopsies.

Constant R Noordman, Steffan J W Borgers, Martijn F Boomsma, Thomas C Kwee, Marloes M G van der Lees, Christiaan G Overduin, Maarten de Rooij, Derya Yakar, Jurgen J Fütterer, Henkjan J Huisman

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In one paragraph

Article in Journal of medical imaging (Bellingham, Wash.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

10 authors.

Constant R NoordmanRadboud University Medical Center, Department of Medical Imaging, Nijmegen, The Netherlands.ORCID https://orcid.org/0000-0003-4358-0087
Steffan J W BorgersRadboud University Medical Center, Department of Medical Imaging, Nijmegen, The Netherlands.ORCID https://orcid.org/0009-0007-6703-5047
Martijn F BoomsmaIsala, Department of Radiology, Zwolle, The Netherlands.ORCID https://orcid.org/0000-0002-7878-0970
Thomas C KweeUniversity Medical Center Groningen, Department of Radiology, Groningen, The Netherlands.
Marloes M G van der LeesRadboud University Medical Center, Department of Medical Imaging, Nijmegen, The Netherlands.
Christiaan G OverduinRadboud University Medical Center, Department of Medical Imaging, Nijmegen, The Netherlands.ORCID https://orcid.org/0000-0002-4589-4357
Maarten de RooijRadboud University Medical Center, Department of Medical Imaging, Nijmegen, The Netherlands.
Derya YakarUniversity Medical Center Groningen, Department of Radiology, Groningen, The Netherlands.ORCID https://orcid.org/0000-0002-4920-9441
Jurgen J FüttererRadboud University Medical Center, Department of Medical Imaging, Nijmegen, The Netherlands.
Henkjan J HuismanRadboud University Medical Center, Department of Medical Imaging, Nijmegen, The Netherlands.ORCID https://orcid.org/0000-0001-6753-3221

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Interventional MR imaging struggles with speed and efficiency. We aim to accelerate transrectal in-bore MR-guided biopsies for prostate cancer through undersampled image reconstruction and instrument localization by image segmentation. Approach: In this single-center retrospective study, we used 8464 MR 2D multislice scans from 1289 patients undergoing a prostate biopsy to train and test a deep learning-based spatiotemporal MR image reconstruction model and a nnU-Net segmentation model. The dataset was synthetically undersampled using various undersampling rates ( Results: The maximum noninferior undersampling rate is 16-times for the temporal model (ITP error: 2.28 mm, 95% CI: 1.68 to 3.31, mean difference from reference standard: 0.63 mm, Conclusion: Deep learning-based spatiotemporal MR image reconstruction can improve time-critical intervention tasks such as instrument tracking. We found 16 times undersampling as the maximum noninferior acceleration where image quality is preserved, ITP error is minimized, and the instrument prediction success rate is maximized.

Indexed as

artificial intelligencedeep learningimage processing (computer-assisted)interventional radiologymagnetic resonance imagingneural networks (computer)

Identifiers

PMID40469203
PMCPMC12131189

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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.