Evidence map›Paper›PMID 34298806›Full record

ArticleCancers2021

Accelerated T2-Weighted TSE Imaging of the Prostate Using Deep Learning Image Reconstruction: A Prospective Comparison with Standard T2-Weighted TSE Imaging.

Sebastian Gassenmaier, Saif Afat, Marcel Dominik Nickel, Mahmoud Mostapha, Judith Herrmann, Haidara Almansour, Konstantin Nikolaou, Ahmed E Othman

Open access · goldAbstract read
In one paragraph

Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
47citing papers in PubMed, 1 pooled it
9.4field-weighted citation impact, top 1% 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

47 citing papers in PubMed, 1 synthesis or guideline pooled it, 82 citations in OpenAlex.

  1. AI-accelerated prostate MRI: a systematic review.The British journal of radiology · 2024
    Pooled it
  2. Review
  3. Review
  4. Integrating Artificial Intelligence into Prostate MR Imaging: Technical Foundations, Clinical Applications, and Workflow Implications.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2026
    Review
  5. 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 · 2026
    Article
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  16. Deep learning-based reconstruction and superresolution for MR-guided thermal ablation of malignant liver lesions.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025
    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

8 authors at 4 institutions in 2 countries.

Sebastian GassenmaierDepartment of Diagnostic and Interventional Radiology, Eberhard-Karls-University Tuebingen, Hoppe-Seyler-Straße 3, 72076 Tuebingen, Germany.ORCID 0000-0003-3701-2227
Saif AfatDepartment of Diagnostic and Interventional Radiology, Eberhard-Karls-University Tuebingen, Hoppe-Seyler-Straße 3, 72076 Tuebingen, Germany.ORCID 0000-0002-6861-6245
Marcel Dominik NickelMR Applications Predevelopment, Siemens Healthcare GmbH, 91052 Erlangen, Germany.
Mahmoud MostaphaDigital Technology & Innovation, Siemens Medical Solutions USA, Inc., Princeton, NJ 08540, USA.
Judith HerrmannDepartment of Diagnostic and Interventional Radiology, Eberhard-Karls-University Tuebingen, Hoppe-Seyler-Straße 3, 72076 Tuebingen, Germany.ORCID 0000-0003-1535-8980
Haidara AlmansourDepartment of Diagnostic and Interventional Radiology, Eberhard-Karls-University Tuebingen, Hoppe-Seyler-Straße 3, 72076 Tuebingen, Germany.
Konstantin NikolaouDepartment of Diagnostic and Interventional Radiology, Eberhard-Karls-University Tuebingen, Hoppe-Seyler-Straße 3, 72076 Tuebingen, Germany.
Ahmed E OthmanDepartment of Diagnostic and Interventional Radiology, Eberhard-Karls-University Tuebingen, Hoppe-Seyler-Straße 3, 72076 Tuebingen, Germany.
University of Tübingen · DEJohannes Gutenberg University Mainz · DESiemens Healthineers (Germany) · DESiemens (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Multiparametric MRI (mpMRI) of the prostate has become the standard of care in prostate cancer evaluation. Recently, deep learning image reconstruction (DLR) methods have been introduced with promising results regarding scan acceleration. Therefore, the aim of this study was to investigate the impact of deep learning image reconstruction (DLR) in a shortened acquisition process of T2-weighted TSE imaging, regarding the image quality and diagnostic confidence, as well as PI-RADS and T2 scoring, as compared to standard T2 TSE imaging. Sixty patients undergoing 3T mpMRI for the evaluation of prostate cancer were prospectively enrolled in this institutional review board-approved study between October 2020 and March 2021. After the acquisition of standard T2 TSE imaging (T2

Indexed as

deep learningdiagnostic imagingmultiparametric magnetic resonance imagingprostatic neoplasms

Identifiers

PMID34298806
PMCPMC8303682
OpenAlexW3186750081

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.