ArticleRadiology2023
Deep Learning Super-Resolution Reconstruction for Fast and Motion-Robust T2-weighted Prostate MRI.
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Article in Radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06244680 (Super-fast 3T Prostate MRI Using High Gradient Strength and Deep Learning), which is not on this map. Cited by 59 papers.
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59citing papers in PubMed
10.2field-weighted citation impact, top 1% of its field
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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.
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.
Super-fast 3T Prostate MRI Using High Gradient Strength and Deep Learning: Initial Experience
Who cites it
59 citing papers in PubMed, 85 citations in OpenAlex.
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- Image Quality and Diagnostic Performance of Accelerated T2-weighted Imaging of Prostate with Deep Learning Reconstruction: A Comparative Study.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2026Article
- Quantification of edge-enhancing effects using accelerated deep learning reconstructed orbital MRI sequences.European journal of radiology open · 2026Article
- The evolving PI-RADS paradigm.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Review
- Cardiac MR function analysis with DL-based super resolution reconstruction: application in the clinical setting.The international journal of cardiovascular imaging · 2026Article
- Improved visualization of perivascular spaces on T2-weighted imaging with deep learning-based denoising and super-resolution reconstruction.Scientific reports · 2026Article
- Acquisition, image quality, and PI-RADS agreement of ultrahigh-gradient DWI in prostate 3-T MRI.European radiology experimental · 2026Article
- Deep learning 3D super-resolution radiomics model based on Gd-enhanced MRI for improving preoperative prediction of HCC pathological grading.Abdominal radiology (New York) · 2026Article
- Deep learning reconstruction accelerated reduced field-of-view DWI in rectal cancer: mucosa-submucosa-muscularis visualization and T staging.European radiology experimental · 2026Article
- CHARMS: A CNN-Transformer Hybrid with Attention Regularization for MRI Super-Resolution.Sensors (Basel, Switzerland) · 2026Article
- Current state of the art of new prostate MRI technologies and potential future developments.BJR open · 2026Review
- Article
- The Efficacy of a Super-Resolution Reconstruction Radiomics Model Based on T2WI for Predicting Placenta Accreta Spectrum Disorders: A Multicenter Study.Current medical imaging · 2026Article
- Ultra-fast whole-brain T2-weighted imaging in 7 seconds using dual-type deep learning reconstruction with single-shot acquisition: clinical feasibility and comparison with conventional methods.Japanese journal of radiology · 2026Article
- Deep Learning HASTE for Upper Abdominal MRI: Improved Image Quality, Speed, and Energy Efficiency in a Prospective Study.NMR in biomedicine · 2026Article
- Deep learning-enhanced super-resolution diffusion-weighted liver MRI: improved image quality, diagnostic performance, and acceleration.Insights into imaging · 2025Article
- Comparative evaluation of four reconstruction techniques for prostate T2-weighted MRI: Sensitivity encoding, compressed sensing, deep learning, and super-resolution.European journal of radiology open · 2025Article
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Authors and funding
13 authors at 1 institution in 2 countries.
Leon M BischoffFrom the Department of Diagnostic and Interventional Radiology (L.M.B., A.I., D.K., U.A., C.C.P., A.M.S., J.A.L.), Quantitative Imaging Laboratory Bonn (QILaB) (L.M.B., A.I., D.K., A.M.S., J.A.L.), Institute for Medical Biometry, Informatics and Epidemiology (L.W.), and Department of Urology (P.K., J.E.), University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany; Philips MR Clinical Science, Best, the Netherlands (J.M.P.); and Philips Market DACH, Hamburg, Germany (C.K., O.M.W.).ORCID 0000-0001-5193-5226
Johannes M PeetersFrom the Department of Diagnostic and Interventional Radiology (L.M.B., A.I., D.K., U.A., C.C.P., A.M.S., J.A.L.), Quantitative Imaging Laboratory Bonn (QILaB) (L.M.B., A.I., D.K., A.M.S., J.A.L.), Institute for Medical Biometry, Informatics and Epidemiology (L.W.), and Department of Urology (P.K., J.E.), University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany; Philips MR Clinical Science, Best, the Netherlands (J.M.P.); and Philips Market DACH, Hamburg, Germany (C.K., O.M.W.).ORCID 0000-0001-6639-0863
Leonie WeinholdFrom the Department of Diagnostic and Interventional Radiology (L.M.B., A.I., D.K., U.A., C.C.P., A.M.S., J.A.L.), Quantitative Imaging Laboratory Bonn (QILaB) (L.M.B., A.I., D.K., A.M.S., J.A.L.), Institute for Medical Biometry, Informatics and Epidemiology (L.W.), and Department of Urology (P.K., J.E.), University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany; Philips MR Clinical Science, Best, the Netherlands (J.M.P.); and Philips Market DACH, Hamburg, Germany (C.K., O.M.W.).ORCID 0000-0002-9194-9427
Philipp KrausewitzFrom the Department of Diagnostic and Interventional Radiology (L.M.B., A.I., D.K., U.A., C.C.P., A.M.S., J.A.L.), Quantitative Imaging Laboratory Bonn (QILaB) (L.M.B., A.I., D.K., A.M.S., J.A.L.), Institute for Medical Biometry, Informatics and Epidemiology (L.W.), and Department of Urology (P.K., J.E.), University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany; Philips MR Clinical Science, Best, the Netherlands (J.M.P.); and Philips Market DACH, Hamburg, Germany (C.K., O.M.W.).ORCID 0000-0002-8213-9975
Jörg EllingerFrom the Department of Diagnostic and Interventional Radiology (L.M.B., A.I., D.K., U.A., C.C.P., A.M.S., J.A.L.), Quantitative Imaging Laboratory Bonn (QILaB) (L.M.B., A.I., D.K., A.M.S., J.A.L.), Institute for Medical Biometry, Informatics and Epidemiology (L.W.), and Department of Urology (P.K., J.E.), University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany; Philips MR Clinical Science, Best, the Netherlands (J.M.P.); and Philips Market DACH, Hamburg, Germany (C.K., O.M.W.).ORCID 0000-0002-7526-0857
Christoph KatemannFrom the Department of Diagnostic and Interventional Radiology (L.M.B., A.I., D.K., U.A., C.C.P., A.M.S., J.A.L.), Quantitative Imaging Laboratory Bonn (QILaB) (L.M.B., A.I., D.K., A.M.S., J.A.L.), Institute for Medical Biometry, Informatics and Epidemiology (L.W.), and Department of Urology (P.K., J.E.), University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany; Philips MR Clinical Science, Best, the Netherlands (J.M.P.); and Philips Market DACH, Hamburg, Germany (C.K., O.M.W.).ORCID 0000-0002-5199-0644
Alexander IsaakFrom the Department of Diagnostic and Interventional Radiology (L.M.B., A.I., D.K., U.A., C.C.P., A.M.S., J.A.L.), Quantitative Imaging Laboratory Bonn (QILaB) (L.M.B., A.I., D.K., A.M.S., J.A.L.), Institute for Medical Biometry, Informatics and Epidemiology (L.W.), and Department of Urology (P.K., J.E.), University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany; Philips MR Clinical Science, Best, the Netherlands (J.M.P.); and Philips Market DACH, Hamburg, Germany (C.K., O.M.W.).ORCID 0000-0003-2016-6166
Oliver M WeberFrom the Department of Diagnostic and Interventional Radiology (L.M.B., A.I., D.K., U.A., C.C.P., A.M.S., J.A.L.), Quantitative Imaging Laboratory Bonn (QILaB) (L.M.B., A.I., D.K., A.M.S., J.A.L.), Institute for Medical Biometry, Informatics and Epidemiology (L.W.), and Department of Urology (P.K., J.E.), University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany; Philips MR Clinical Science, Best, the Netherlands (J.M.P.); and Philips Market DACH, Hamburg, Germany (C.K., O.M.W.).ORCID 0000-0002-2569-0875
Daniel KuettingFrom the Department of Diagnostic and Interventional Radiology (L.M.B., A.I., D.K., U.A., C.C.P., A.M.S., J.A.L.), Quantitative Imaging Laboratory Bonn (QILaB) (L.M.B., A.I., D.K., A.M.S., J.A.L.), Institute for Medical Biometry, Informatics and Epidemiology (L.W.), and Department of Urology (P.K., J.E.), University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany; Philips MR Clinical Science, Best, the Netherlands (J.M.P.); and Philips Market DACH, Hamburg, Germany (C.K., O.M.W.).ORCID 0000-0001-6520-1020
Ulrike AttenbergerFrom the Department of Diagnostic and Interventional Radiology (L.M.B., A.I., D.K., U.A., C.C.P., A.M.S., J.A.L.), Quantitative Imaging Laboratory Bonn (QILaB) (L.M.B., A.I., D.K., A.M.S., J.A.L.), Institute for Medical Biometry, Informatics and Epidemiology (L.W.), and Department of Urology (P.K., J.E.), University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany; Philips MR Clinical Science, Best, the Netherlands (J.M.P.); and Philips Market DACH, Hamburg, Germany (C.K., O.M.W.).ORCID 0000-0002-8123-5447
Claus C PieperFrom the Department of Diagnostic and Interventional Radiology (L.M.B., A.I., D.K., U.A., C.C.P., A.M.S., J.A.L.), Quantitative Imaging Laboratory Bonn (QILaB) (L.M.B., A.I., D.K., A.M.S., J.A.L.), Institute for Medical Biometry, Informatics and Epidemiology (L.W.), and Department of Urology (P.K., J.E.), University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany; Philips MR Clinical Science, Best, the Netherlands (J.M.P.); and Philips Market DACH, Hamburg, Germany (C.K., O.M.W.).ORCID 0000-0002-0893-1200
Alois M SprinkartFrom the Department of Diagnostic and Interventional Radiology (L.M.B., A.I., D.K., U.A., C.C.P., A.M.S., J.A.L.), Quantitative Imaging Laboratory Bonn (QILaB) (L.M.B., A.I., D.K., A.M.S., J.A.L.), Institute for Medical Biometry, Informatics and Epidemiology (L.W.), and Department of Urology (P.K., J.E.), University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany; Philips MR Clinical Science, Best, the Netherlands (J.M.P.); and Philips Market DACH, Hamburg, Germany (C.K., O.M.W.).ORCID 0000-0002-1435-9562
Julian A LuetkensFrom the Department of Diagnostic and Interventional Radiology (L.M.B., A.I., D.K., U.A., C.C.P., A.M.S., J.A.L.), Quantitative Imaging Laboratory Bonn (QILaB) (L.M.B., A.I., D.K., A.M.S., J.A.L.), Institute for Medical Biometry, Informatics and Epidemiology (L.W.), and Department of Urology (P.K., J.E.), University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany; Philips MR Clinical Science, Best, the Netherlands (J.M.P.); and Philips Market DACH, Hamburg, Germany (C.K., O.M.W.).ORCID 0000-0002-7715-4636
Zimmer Biomet (Netherlands) · NL
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background Deep learning (DL) reconstructions can enhance image quality while decreasing MRI acquisition time. However, DL reconstruction methods combined with compressed sensing for prostate MRI have not been well studied. Purpose To use an industry-developed DL algorithm to reconstruct low-resolution T2-weighted turbo spin-echo (TSE) prostate MRI scans and compare these with standard sequences. Materials and Methods In this prospective study, participants with suspected prostate cancer underwent prostate MRI with a Cartesian standard-resolution T2-weighted TSE sequence (T2
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Deep LearningProstatic NeoplasmsAgedHumansMagnetic Resonance ImagingMaleProspective StudiesProstate
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NCT06244680ClinicalTrials.gov
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.