ArticleRadiology2022
Deep Learning Reconstruction of Diffusion-weighted MRI Improves Image Quality for Prostatic Imaging.
Registry-linked trialAbstract read
In one paragraph
Article in Radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05820113 (Deep Learning Super Resolution Reconstruction for Fast and Motion Robust T2-weighted Prostate MRI), which is not on this map. Cited by 91 papers.
0numbers the graph read from it
0cells of the map it votes in
91citing papers in PubMed
19.0field-weighted citation impact, top 1% of its field
What it found
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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.
Deep Learning Super Resolution Reconstruction for Fast and Motion Robust T2-weighted Prostate MRI
Who cites it
91 citing papers in PubMed, 133 citations in OpenAlex.
- Highly accelerated knee magnetic resonance imaging using deep neural network (DNN)-based reconstruction: prospective, multi-reader, multi-vendor study.Scientific reports · 2023Trial
- Combined ADC and TMagnetic resonance in medicine · 2026Article
- Current status and future directions of AI in prostate cancer detection on MRI: a special report from the ESUR prostate MRI working group authors.European radiology · 2026Article
- Empirical noise mapping guides optimal region-of-interest selection for accurate noise estimation in prostate DWI.Journal of applied clinical medical physics · 2026Article
- Accelerating prostate MRI: a practical review of modern acquisition strategies.Abdominal radiology (New York) · 2026Review
- Impact of deep-learning image reconstruction on multiplexed sensitivity encoding diffusion-weighted imaging in the female pelvis.Quantitative imaging in medicine and surgery · 2026Article
- 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 · 2026Review
- 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
- Impact of deep learning-based MRI reconstruction on image quality and apparent diffusion coefficient measurements in patients with intracranial tumors: a prospective intra-individual study.Quantitative imaging in medicine and surgery · 2026Article
- Let distortion-guided restoration: a physics-informed learning framework to correct prostate diffusion MRI artifacts.Radiology advances · 2026Article
- Deep Learning Reconstruction of Diffusion-weighted MRI Enables Shorter Examination Times While Maintaining Image Quality in Head and Neck Imaging.Clinical neuroradiology · 2026Article
- Reverse Encoding Distortion Correction of Female Pelvic Diffusion-weighted Imaging on a 1.5-T MR System: Influence on Image Quality and Apparent Diffusion Coefficient Measurements for Both In Vitro and In Vivo Studies.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2026Article
- Enhancing resolution and image quality in musculoskeletal MRI using deep learning reconstruction.European radiology experimental · 2026Article
- Time-dependent Diffusion MRI for Predicting Response to Induction Chemotherapy in Nasopharyngeal Carcinoma.Radiology. Imaging cancer · 2026Article
- Conjugate gradient and deep learning reconstructions: reduced time without affecting image quality and nodule detection.European radiology · 2026Article
- Assessment of Robustness of MRI Radiomic Features in the Abdomen: Impact of Deep Learning Reconstruction and Accelerated Acquisition.Journal of imaging informatics in medicine · 2026Article
- Improved prostate diffusion imaging using deep learning denoising and phase correction with ultra-high-density coil array.Radiology advances · 2026Article
- Artificial Intelligence in Prostate MRI: Addressing Current Limitations Through Emerging Technologies.Journal of magnetic resonance imaging : JMRI · 2026Review
- Acquisition, image quality, and PI-RADS agreement of ultrahigh-gradient DWI in prostate 3-T MRI.European radiology experimental · 2026Article
- Deep learning reconstruction accelerated reduced field-of-view DWI in rectal cancer: mucosa-submucosa-muscularis visualization and T staging.European radiology experimental · 2026Article
31 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
11 authors at 1 institution in 1 country.
Takahiro UedaFrom the Department of Radiology (T.U., Y. Ohno, S.H., Y.T., Y. Obama, H.I., H.T.) and Joint Research Laboratory of Advanced Medical Imaging (Y. Ohno, K.M.), Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Japan; and Canon Medical Systems Corporation, Otawara, Japan (K.Y., M.I., M.Y.).ORCID 0000-0003-2577-3619
Yoshiharu OhnoFrom the Department of Radiology (T.U., Y. Ohno, S.H., Y.T., Y. Obama, H.I., H.T.) and Joint Research Laboratory of Advanced Medical Imaging (Y. Ohno, K.M.), Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Japan; and Canon Medical Systems Corporation, Otawara, Japan (K.Y., M.I., M.Y.).ORCID 0000-0002-4431-1084
Kaori YamamotoFrom the Department of Radiology (T.U., Y. Ohno, S.H., Y.T., Y. Obama, H.I., H.T.) and Joint Research Laboratory of Advanced Medical Imaging (Y. Ohno, K.M.), Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Japan; and Canon Medical Systems Corporation, Otawara, Japan (K.Y., M.I., M.Y.).ORCID 0000-0003-0884-3419
Kazuhiro MurayamaFrom the Department of Radiology (T.U., Y. Ohno, S.H., Y.T., Y. Obama, H.I., H.T.) and Joint Research Laboratory of Advanced Medical Imaging (Y. Ohno, K.M.), Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Japan; and Canon Medical Systems Corporation, Otawara, Japan (K.Y., M.I., M.Y.).ORCID 0000-0002-4167-1930
Masato IkedoFrom the Department of Radiology (T.U., Y. Ohno, S.H., Y.T., Y. Obama, H.I., H.T.) and Joint Research Laboratory of Advanced Medical Imaging (Y. Ohno, K.M.), Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Japan; and Canon Medical Systems Corporation, Otawara, Japan (K.Y., M.I., M.Y.).ORCID 0000-0002-2701-1899
Masao YuiFrom the Department of Radiology (T.U., Y. Ohno, S.H., Y.T., Y. Obama, H.I., H.T.) and Joint Research Laboratory of Advanced Medical Imaging (Y. Ohno, K.M.), Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Japan; and Canon Medical Systems Corporation, Otawara, Japan (K.Y., M.I., M.Y.).ORCID 0000-0002-2562-0623
Satomu HanamatsuFrom the Department of Radiology (T.U., Y. Ohno, S.H., Y.T., Y. Obama, H.I., H.T.) and Joint Research Laboratory of Advanced Medical Imaging (Y. Ohno, K.M.), Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Japan; and Canon Medical Systems Corporation, Otawara, Japan (K.Y., M.I., M.Y.).ORCID 0000-0002-3911-4107
Yumi TanakaFrom the Department of Radiology (T.U., Y. Ohno, S.H., Y.T., Y. Obama, H.I., H.T.) and Joint Research Laboratory of Advanced Medical Imaging (Y. Ohno, K.M.), Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Japan; and Canon Medical Systems Corporation, Otawara, Japan (K.Y., M.I., M.Y.).ORCID 0000-0002-8936-5246
Yuki ObamaFrom the Department of Radiology (T.U., Y. Ohno, S.H., Y.T., Y. Obama, H.I., H.T.) and Joint Research Laboratory of Advanced Medical Imaging (Y. Ohno, K.M.), Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Japan; and Canon Medical Systems Corporation, Otawara, Japan (K.Y., M.I., M.Y.).
Hirotaka IkedaFrom the Department of Radiology (T.U., Y. Ohno, S.H., Y.T., Y. Obama, H.I., H.T.) and Joint Research Laboratory of Advanced Medical Imaging (Y. Ohno, K.M.), Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Japan; and Canon Medical Systems Corporation, Otawara, Japan (K.Y., M.I., M.Y.).ORCID 0000-0002-6945-8816
Hiroshi ToyamaFrom the Department of Radiology (T.U., Y. Ohno, S.H., Y.T., Y. Obama, H.I., H.T.) and Joint Research Laboratory of Advanced Medical Imaging (Y. Ohno, K.M.), Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Japan; and Canon Medical Systems Corporation, Otawara, Japan (K.Y., M.I., M.Y.).ORCID 0000-0002-5341-1693
Fujita Health University Hospital · JP
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background Deep learning reconstruction (DLR) may improve image quality. However, its impact on diffusion-weighted imaging (DWI) of the prostate has yet to be assessed. Purpose To determine whether DLR can improve image quality of diffusion-weighted MRI at
Indexed as
Deep LearningProstatic NeoplasmsAgedDiffusion Magnetic Resonance ImagingHumansMaleMiddle AgedProstateRetrospective Studies
Identifiers
What OpenQuestion holds
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Read underepoch 390
Registered trials
NCT05820113ClinicalTrials.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.