ArticleRadiology. Artificial intelligence2021
Synthesizing High-
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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 14 papers.
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14citing papers in PubMed
2.1field-weighted citation impact, top 12% of its field
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Who cites it
14 citing papers in PubMed, 26 citations in OpenAlex.
- Accelerating prostate MRI: a practical review of modern acquisition strategies.Abdominal radiology (New York) · 2026Review
- 3D-StyleGAN2-ADA: Volumetric Synthesis of Realistic Prostate T2W MRI.Journal of imaging · 2026Article
- Artificial Intelligence in Prostate MRI: Addressing Current Limitations Through Emerging Technologies.Journal of magnetic resonance imaging : JMRI · 2026Review
- Artificial intelligence-generated apparent diffusion coefficient (AI-ADC) maps for prostate gland assessment: a multi-reader study.European radiology · 2026Article
- Deep learning-based diffusion-weighted imaging vs. conventionally obtained diffusion-weighted imaging in prostate cancer extracapsular extension detection: a multicenter retrospective study.BMC medical imaging · 2025Article
- Toward a Refined PI-RADS: The Feasibility and Limitations of More Informative Metrics in Reviewing MRI Scans.Journal of magnetic resonance imaging : JMRI · 2025Review
- Generative Artificial Intelligence in Prostate Cancer Imaging.Balkan medical journal · 2025Review
- Deep learning network enhances imaging quality of low-b-value diffusion-weighted imaging and improves lesion detection in prostate cancer.BMC cancer · 2025Article
- BEEx Is an Open-Source Tool That Evaluates Batch Effects in Medical Images to Enable Multicenter Studies.Cancer research · 2025Article
- AI-ADC: Channel and Spatial Attention-Based Contrastive Learning to Generate ADC Maps from T2W MRI for Prostate Cancer Detection.Journal of personalized medicine · 2024Article
- Deep Learning-based Unsupervised Domain Adaptation via a Unified Model for Prostate Lesion Detection Using Multisite Biparametric MRI Datasets.Radiology. Artificial intelligence · 2024Article
- Synthesizing High b-Value Diffusion-Weighted Imaging of Gastric Cancer Using an Improved Vision Transformer CycleGAN.Bioengineering (Basel, Switzerland) · 2024Article
- Development and Validation of a Deep Learning Model to Reduce the Interference of Rectal Artifacts in MRI-based Prostate Cancer Diagnosis.Radiology. Artificial intelligence · 2024Article
- Diffusion kurtosis imaging and standard diffusion imaging in the magnetic resonance imaging assessment of prostate cancer.Gland surgery · 2023Review
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Authors and funding
11 authors at 3 institutions in 1 country.
Lei HuDepartment of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 600 Yi Shan Road, Shanghai 200233, China (L.H., W.H.X., J.G.Z.); State Key Laboratory of Integrated Services Networks, School of Telecommunications Engineering, Xidian University, Xi'an, China (D.W.Z.); Department of Radiology, Renmin Hospital, Wuhan University, Wuhan, China (Y.F.Z., L.L., H. He, L.Q., Y.K.Z.); MR Application Development, Siemens Shenzhen MR, Shenzhen, China (C.X.F.); and Department of Radiology, The Affiliated Renmin Hospital of Jiangsu University, Zhenjiang, China (H. Hu).ORCID https://orcid.org/0000-0002-9124-5411
Da-Wei ZhouDepartment of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 600 Yi Shan Road, Shanghai 200233, China (L.H., W.H.X., J.G.Z.); State Key Laboratory of Integrated Services Networks, School of Telecommunications Engineering, Xidian University, Xi'an, China (D.W.Z.); Department of Radiology, Renmin Hospital, Wuhan University, Wuhan, China (Y.F.Z., L.L., H. He, L.Q., Y.K.Z.); MR Application Development, Siemens Shenzhen MR, Shenzhen, China (C.X.F.); and Department of Radiology, The Affiliated Renmin Hospital of Jiangsu University, Zhenjiang, China (H. Hu).ORCID https://orcid.org/0000-0002-0694-3603
Yun-Fei ZhaDepartment of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 600 Yi Shan Road, Shanghai 200233, China (L.H., W.H.X., J.G.Z.); State Key Laboratory of Integrated Services Networks, School of Telecommunications Engineering, Xidian University, Xi'an, China (D.W.Z.); Department of Radiology, Renmin Hospital, Wuhan University, Wuhan, China (Y.F.Z., L.L., H. He, L.Q., Y.K.Z.); MR Application Development, Siemens Shenzhen MR, Shenzhen, China (C.X.F.); and Department of Radiology, The Affiliated Renmin Hospital of Jiangsu University, Zhenjiang, China (H. Hu).ORCID https://orcid.org/0000-0002-8714-7472
Liang LiDepartment of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 600 Yi Shan Road, Shanghai 200233, China (L.H., W.H.X., J.G.Z.); State Key Laboratory of Integrated Services Networks, School of Telecommunications Engineering, Xidian University, Xi'an, China (D.W.Z.); Department of Radiology, Renmin Hospital, Wuhan University, Wuhan, China (Y.F.Z., L.L., H. He, L.Q., Y.K.Z.); MR Application Development, Siemens Shenzhen MR, Shenzhen, China (C.X.F.); and Department of Radiology, The Affiliated Renmin Hospital of Jiangsu University, Zhenjiang, China (H. Hu).ORCID https://orcid.org/0000-0001-5648-724X
Huan HeDepartment of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 600 Yi Shan Road, Shanghai 200233, China (L.H., W.H.X., J.G.Z.); State Key Laboratory of Integrated Services Networks, School of Telecommunications Engineering, Xidian University, Xi'an, China (D.W.Z.); Department of Radiology, Renmin Hospital, Wuhan University, Wuhan, China (Y.F.Z., L.L., H. He, L.Q., Y.K.Z.); MR Application Development, Siemens Shenzhen MR, Shenzhen, China (C.X.F.); and Department of Radiology, The Affiliated Renmin Hospital of Jiangsu University, Zhenjiang, China (H. Hu).ORCID https://orcid.org/0000-0002-3081-7236
Wen-Hao XuDepartment of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 600 Yi Shan Road, Shanghai 200233, China (L.H., W.H.X., J.G.Z.); State Key Laboratory of Integrated Services Networks, School of Telecommunications Engineering, Xidian University, Xi'an, China (D.W.Z.); Department of Radiology, Renmin Hospital, Wuhan University, Wuhan, China (Y.F.Z., L.L., H. He, L.Q., Y.K.Z.); MR Application Development, Siemens Shenzhen MR, Shenzhen, China (C.X.F.); and Department of Radiology, The Affiliated Renmin Hospital of Jiangsu University, Zhenjiang, China (H. Hu).ORCID https://orcid.org/0000-0003-1449-8287
Li QianDepartment of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 600 Yi Shan Road, Shanghai 200233, China (L.H., W.H.X., J.G.Z.); State Key Laboratory of Integrated Services Networks, School of Telecommunications Engineering, Xidian University, Xi'an, China (D.W.Z.); Department of Radiology, Renmin Hospital, Wuhan University, Wuhan, China (Y.F.Z., L.L., H. He, L.Q., Y.K.Z.); MR Application Development, Siemens Shenzhen MR, Shenzhen, China (C.X.F.); and Department of Radiology, The Affiliated Renmin Hospital of Jiangsu University, Zhenjiang, China (H. Hu).ORCID https://orcid.org/0000-0002-5432-6589
Yi-Kun ZhangDepartment of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 600 Yi Shan Road, Shanghai 200233, China (L.H., W.H.X., J.G.Z.); State Key Laboratory of Integrated Services Networks, School of Telecommunications Engineering, Xidian University, Xi'an, China (D.W.Z.); Department of Radiology, Renmin Hospital, Wuhan University, Wuhan, China (Y.F.Z., L.L., H. He, L.Q., Y.K.Z.); MR Application Development, Siemens Shenzhen MR, Shenzhen, China (C.X.F.); and Department of Radiology, The Affiliated Renmin Hospital of Jiangsu University, Zhenjiang, China (H. Hu).ORCID https://orcid.org/0000-0002-9588-0578
Cai-Xia FuDepartment of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 600 Yi Shan Road, Shanghai 200233, China (L.H., W.H.X., J.G.Z.); State Key Laboratory of Integrated Services Networks, School of Telecommunications Engineering, Xidian University, Xi'an, China (D.W.Z.); Department of Radiology, Renmin Hospital, Wuhan University, Wuhan, China (Y.F.Z., L.L., H. He, L.Q., Y.K.Z.); MR Application Development, Siemens Shenzhen MR, Shenzhen, China (C.X.F.); and Department of Radiology, The Affiliated Renmin Hospital of Jiangsu University, Zhenjiang, China (H. Hu).
Hui HuDepartment of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 600 Yi Shan Road, Shanghai 200233, China (L.H., W.H.X., J.G.Z.); State Key Laboratory of Integrated Services Networks, School of Telecommunications Engineering, Xidian University, Xi'an, China (D.W.Z.); Department of Radiology, Renmin Hospital, Wuhan University, Wuhan, China (Y.F.Z., L.L., H. He, L.Q., Y.K.Z.); MR Application Development, Siemens Shenzhen MR, Shenzhen, China (C.X.F.); and Department of Radiology, The Affiliated Renmin Hospital of Jiangsu University, Zhenjiang, China (H. Hu).
Jun-Gong ZhaoDepartment of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 600 Yi Shan Road, Shanghai 200233, China (L.H., W.H.X., J.G.Z.); State Key Laboratory of Integrated Services Networks, School of Telecommunications Engineering, Xidian University, Xi'an, China (D.W.Z.); Department of Radiology, Renmin Hospital, Wuhan University, Wuhan, China (Y.F.Z., L.L., H. He, L.Q., Y.K.Z.); MR Application Development, Siemens Shenzhen MR, Shenzhen, China (C.X.F.); and Department of Radiology, The Affiliated Renmin Hospital of Jiangsu University, Zhenjiang, China (H. Hu).ORCID https://orcid.org/0000-0001-5823-2179
Jiangsu University · CNWuhan University · CNShanghai Jiao Tong University · CN
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo develop and evaluate a diffusion-weighted imaging (DWI) deep learning framework based on the generative adversarial network (GAN) to generate synthetic high- MATERIALS AND
methodsThis retrospective multicenter study included 395 patients who underwent prostate multiparametric MRI. This cohort was split into internal training (96 patients) and external testing (299 patients) datasets. To create SYN
resultsWhen compared with the M
conclusionA deep learning framework based on GAN is a promising method to synthesize realistic high-
Indexed as
Abdomen/GIDeep Learning FrameworkDiffusion-weighted ImagingGenerative Adversarial NetworksHigh b ValueProstate Cancer
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