Evidence map›Paper›PMID 34617028›Full record

ArticleRadiology. Artificial intelligence2021

Accelerating Whole-Body Diffusion-weighted MRI with Deep Learning-based Denoising Image Filters.

Konstantinos Zormpas-Petridis, Nina Tunariu, Andra Curcean, Christina Messiou, Sebastian Curcean, David J Collins, Julie C Hughes, Yann Jamin, Dow-Mu Koh, Matthew D Blackledge

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Radiology. Artificial intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
3.1field-weighted citation impact, top 8% of its field
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

15 citing papers in PubMed, 39 citations in OpenAlex.

  1. Article
  2. Article
  3. Detecting Distant Metastases in Prostate Cancer Using Whole-body MR Imaging Together with DWIBS (Diffusion-weighted Imaging with Background Body Signal Suppression).Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2026
    Review
  4. Precision medicine in myeloma demands precision imaging: is whole body MRI the solution?Cancer imaging : the official publication of the International Cancer Imaging Society · 2026
    Article
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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

10 authors at 1 institution in 1 country.

Konstantinos Zormpas-PetridisDivision of Radiation Therapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Rd, London SW7 3RP, England (K.Z.P., N.T., A.C., C.M., S.C., D.J.C., J.C.H., Y.J., D.M.K., M.D.B.); and Department of Radiology, The Royal Marsden National Health Service Foundation Trust, Surrey, England (N.T., A.C., C.M., S.C., J.C.H., D.M.K.).ORCID https://orcid.org/0000-0002-3371-2915
Nina TunariuDivision of Radiation Therapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Rd, London SW7 3RP, England (K.Z.P., N.T., A.C., C.M., S.C., D.J.C., J.C.H., Y.J., D.M.K., M.D.B.); and Department of Radiology, The Royal Marsden National Health Service Foundation Trust, Surrey, England (N.T., A.C., C.M., S.C., J.C.H., D.M.K.).ORCID https://orcid.org/0000-0001-6656-3699
Andra CurceanDivision of Radiation Therapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Rd, London SW7 3RP, England (K.Z.P., N.T., A.C., C.M., S.C., D.J.C., J.C.H., Y.J., D.M.K., M.D.B.); and Department of Radiology, The Royal Marsden National Health Service Foundation Trust, Surrey, England (N.T., A.C., C.M., S.C., J.C.H., D.M.K.).ORCID https://orcid.org/0000-0003-2385-2954
Christina MessiouDivision of Radiation Therapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Rd, London SW7 3RP, England (K.Z.P., N.T., A.C., C.M., S.C., D.J.C., J.C.H., Y.J., D.M.K., M.D.B.); and Department of Radiology, The Royal Marsden National Health Service Foundation Trust, Surrey, England (N.T., A.C., C.M., S.C., J.C.H., D.M.K.).ORCID https://orcid.org/0000-0002-0557-9379
Sebastian CurceanDivision of Radiation Therapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Rd, London SW7 3RP, England (K.Z.P., N.T., A.C., C.M., S.C., D.J.C., J.C.H., Y.J., D.M.K., M.D.B.); and Department of Radiology, The Royal Marsden National Health Service Foundation Trust, Surrey, England (N.T., A.C., C.M., S.C., J.C.H., D.M.K.).ORCID https://orcid.org/0000-0002-7256-461X
David J CollinsDivision of Radiation Therapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Rd, London SW7 3RP, England (K.Z.P., N.T., A.C., C.M., S.C., D.J.C., J.C.H., Y.J., D.M.K., M.D.B.); and Department of Radiology, The Royal Marsden National Health Service Foundation Trust, Surrey, England (N.T., A.C., C.M., S.C., J.C.H., D.M.K.).
Julie C HughesDivision of Radiation Therapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Rd, London SW7 3RP, England (K.Z.P., N.T., A.C., C.M., S.C., D.J.C., J.C.H., Y.J., D.M.K., M.D.B.); and Department of Radiology, The Royal Marsden National Health Service Foundation Trust, Surrey, England (N.T., A.C., C.M., S.C., J.C.H., D.M.K.).ORCID https://orcid.org/0000-0003-3380-696X
Yann JaminDivision of Radiation Therapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Rd, London SW7 3RP, England (K.Z.P., N.T., A.C., C.M., S.C., D.J.C., J.C.H., Y.J., D.M.K., M.D.B.); and Department of Radiology, The Royal Marsden National Health Service Foundation Trust, Surrey, England (N.T., A.C., C.M., S.C., J.C.H., D.M.K.).ORCID https://orcid.org/0000-0003-0350-3757
Dow-Mu KohDivision of Radiation Therapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Rd, London SW7 3RP, England (K.Z.P., N.T., A.C., C.M., S.C., D.J.C., J.C.H., Y.J., D.M.K., M.D.B.); and Department of Radiology, The Royal Marsden National Health Service Foundation Trust, Surrey, England (N.T., A.C., C.M., S.C., J.C.H., D.M.K.).
Matthew D BlackledgeDivision of Radiation Therapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Rd, London SW7 3RP, England (K.Z.P., N.T., A.C., C.M., S.C., D.J.C., J.C.H., Y.J., D.M.K., M.D.B.); and Department of Radiology, The Royal Marsden National Health Service Foundation Trust, Surrey, England (N.T., A.C., C.M., S.C., J.C.H., D.M.K.).ORCID https://orcid.org/0000-0001-8117-9003
Engineering and Physical Sciences Research Council · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo use deep learning to improve the image quality of subsampled images (number of acquisitions = 1 [NOA MATERIALS AND

methodsBoth retrospective and prospective patient groups were used to develop a deep learning-based denoising image filter (DNIF) model. For initial model training and validation, 17 patients with metastatic prostate cancer with acquired WBDWI NOA

resultsThe model visually improved the quality of NOA

conclusionClinical-standard images were generated from subsampled images by using a DNIF.

Indexed as

Image PostprocessingLungMetastasesMR-Diffusion-weighted ImagingMR-Functional ImagingNeural NetworksOncologyProstateSupervised LearningWhole-Body Imaging

Identifiers

PMID34617028
PMCPMC8489468
OpenAlexW3178065392

What OpenQuestion holds

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LicenceCC BY
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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.