ArticleAbdominal radiology (New York)2021
Novel deep learning-based noise reduction technique for prostate magnetic resonance imaging.
Article in Abdominal radiology (New York), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed, 72 citations in OpenAlex.
- 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
- Radon-Guided Wavelet-Domain Attention U-Net for Periodic Artifact Suppression in Brain MRI.Journal of imaging · 2026Article
- Deep learning reconstruction for temporomandibular joint MRI: diagnostic interchangeability, image quality, and scan time reduction.European radiology · 2026Article
- Artificial Intelligence in Prostate MRI: Addressing Current Limitations Through Emerging Technologies.Journal of magnetic resonance imaging : JMRI · 2026Review
- Application of 3T contrast-enhanced Dixon water-fat separation coronary magnetic resonance angiography with artificial intelligence-assisted compressed sensing on coronary artery aneurysm in children with Kawasaki disease.Translational pediatrics · 2026Article
- Impact of AIR™ Recon DL on magnetic resonance imaging-based quantitative brain structure measurements.Psychoradiology · 2026Article
- Current state of the art of new prostate MRI technologies and potential future developments.BJR open · 2026Review
- Pix2Pix generative-adversarial network in improving the quality of T2-weighted prostate magnetic resonance imaging: a multi-reader study.Diagnostic and interventional radiology (Ankara, Turkey) · 2025Article
- Effect of age on substantia nigra subregions using neuromelanin-sensitive magnetic resonance imaging overlapping on quantitative susceptibility mapping.Quantitative imaging in medicine and surgery · 2025Article
- Deep learning reconstruction of diffusion-weighted imaging with single-shot echo-planar imaging in endometrial cancer: a comparison with multi-shot echo-planar imaging.Abdominal radiology (New York) · 2025Article
- Deep learning reconstruction for accelerating synthetic magnetic resonance imaging of the breast: a comparative analysis of accelerated and standard protocols.Quantitative imaging in medicine and surgery · 2025Article
- Prostate MRI Using Deep Learning Reconstruction in Response to Cancer Screening Demands-A Systematic Review and Meta-Analysis.Journal of personalized medicine · 2025Review
- Preoperative MRI-based deep learning reconstruction and classification model for assessing rectal cancer.BMC medical imaging · 2025Article
- Deep learning reconstruction in biparametric prostate MRI: Impact on qualitative and radiomics analyses.Research in diagnostic and interventional imaging · 2025Article
- A Narrative Review of Artificial Intelligence in MRI-Guided Prostate Cancer Diagnosis: Addressing Key Challenges.Diagnostics (Basel, Switzerland) · 2025Review
- Improved image quality and reduced acquisition time in prostate T2-weighted spin-echo MRI using a modified PI-RADS-adherent sequence.European radiology experimental · 2025Article
- Deep learning for quality assessment of axial T2-weighted prostate MRI: a tool to reduce unnecessary rescanning.European radiology experimental · 2025Article
- Interpreting Prostate Multiparametric MRI: Beyond Adenocarcinoma - Anatomical Variations, Mimickers, and Post-Intervention Changes.Seminars in ultrasound, CT, and MR · 2025Review
- Deep-Learning-Based Reconstruction of Single-Breath-Hold 3 mm HASTE Improves Abdominal Image Quality and Reduces Acquisition Time: A Quantitative Analysis.Current oncology (Toronto, Ont.) · 2025Article
- High-resolution deep learning-reconstructed T2-weighted imaging for the improvement of image quality and extraprostatic extension assessment in prostate MRI.Frontiers in radiology · 2025Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
introductionMagnetic resonance imaging (MRI) has played an increasingly major role in the evaluation of patients with prostate cancer, although prostate MRI presents several technical challenges. Newer techniques, such as deep learning (DL), have been applied to medical imaging, leading to improvements in image quality. Our goal is to evaluate the performance of a new deep learning-based reconstruction method, "DLR" in improving image quality and mitigating artifacts, which is now commercially available as AIR
methodsThis study included 31 patients with a history of prostate cancer that had a multiparametric MRI of the prostate with an endorectal coil (ERC) at 1.5 T or 3.0 T. Four series of T2-weighted images were generated in total: one set with the ERC signal turned on (ERC) and another set with the ERC signal turned off (Non-ERC). Each of these sets then reconstructed using two different reconstruction methods: conventional reconstruction (Conv) and DL Recon (DLR): ERC
resultsThe Non-ERC
conclusionProstate imaging without the use of an endorectal coil could benefit from deep learning reconstruction as demonstrated with T2-weighted imaging MRI evaluations of the prostate.
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Registered trials
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 Socratic graph.