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Index by author

July 01, 2020; Volume 41,Issue 7

  1. Zan, E.

    1. Adult Brain
      Open Access
      Brain Imaging Use and Findings in COVID-19: A Single Academic Center Experience in the Epicenter of Disease in the United States
      A. Radmanesh, E. Raz, E. Zan, A. Derman and M. Kaminetzky
      American Journal of Neuroradiology July 2020, 41 (7) 1179-1183; DOI: https://doi.org/10.3174/ajnr.A6610

  2. Zeng, F.Y.

    1. Neurointervention
      Open Access
      A Hemodynamic Mechanism Correlating with the Initiation of MCA Bifurcation Aneurysms
      Z. Huang, M. Zeng, W.G. Tao, F.Y. Zeng, C.Q. Chen, L.B. Zhang and F.H. Chen
      American Journal of Neuroradiology July 2020, 41 (7) 1217-1224; DOI: https://doi.org/10.3174/ajnr.A6615

  3. Zeng, M.

    1. Neurointervention
      Open Access
      A Hemodynamic Mechanism Correlating with the Initiation of MCA Bifurcation Aneurysms
      Z. Huang, M. Zeng, W.G. Tao, F.Y. Zeng, C.Q. Chen, L.B. Zhang and F.H. Chen
      American Journal of Neuroradiology July 2020, 41 (7) 1217-1224; DOI: https://doi.org/10.3174/ajnr.A6615

  4. Zhang, L.B.

    1. Neurointervention
      Open Access
      A Hemodynamic Mechanism Correlating with the Initiation of MCA Bifurcation Aneurysms
      Z. Huang, M. Zeng, W.G. Tao, F.Y. Zeng, C.Q. Chen, L.B. Zhang and F.H. Chen
      American Journal of Neuroradiology July 2020, 41 (7) 1217-1224; DOI: https://doi.org/10.3174/ajnr.A6615

  5. Zhou, H.

    1. EDITOR'S CHOICEPediatric Neuroimaging
      Open Access
      Automatic Machine Learning to Differentiate Pediatric Posterior Fossa Tumors on Routine MR Imaging
      H. Zhou, R. Hu, O. Tang, C. Hu, L. Tang, K. Chang, Q. Shen, J. Wu, B. Zou, B. Xiao, J. Boxerman, W. Chen, R.Y. Huang, L. Yang, H.X. Bai and C. Zhu
      American Journal of Neuroradiology July 2020, 41 (7) 1279-1285; DOI: https://doi.org/10.3174/ajnr.A6621

      This retrospective study included preoperative MR imaging of 288 pediatric patients with pediatric posterior fossa tumors, including medulloblastoma (n=111), ependymoma (n=70), and pilocytic astrocytoma (n=107). Radiomics features were extracted from T2-weighted images, contrast-enhanced T1-weighted images, and ADC maps. Models generated by standard manual optimization by a machine learning expert were compared with automatic machine learning via the Tree-Based Pipeline Optimization Tool for performance evaluation. The authors conclude that automatic machine learning based on routine MR imaging classified pediatric posterior fossa tumors with high accuracy compared with manual expert pipeline optimization and qualitative expert MR imaging review.

  6. Zhu, C.

    1. EDITOR'S CHOICEPediatric Neuroimaging
      Open Access
      Automatic Machine Learning to Differentiate Pediatric Posterior Fossa Tumors on Routine MR Imaging
      H. Zhou, R. Hu, O. Tang, C. Hu, L. Tang, K. Chang, Q. Shen, J. Wu, B. Zou, B. Xiao, J. Boxerman, W. Chen, R.Y. Huang, L. Yang, H.X. Bai and C. Zhu
      American Journal of Neuroradiology July 2020, 41 (7) 1279-1285; DOI: https://doi.org/10.3174/ajnr.A6621

      This retrospective study included preoperative MR imaging of 288 pediatric patients with pediatric posterior fossa tumors, including medulloblastoma (n=111), ependymoma (n=70), and pilocytic astrocytoma (n=107). Radiomics features were extracted from T2-weighted images, contrast-enhanced T1-weighted images, and ADC maps. Models generated by standard manual optimization by a machine learning expert were compared with automatic machine learning via the Tree-Based Pipeline Optimization Tool for performance evaluation. The authors conclude that automatic machine learning based on routine MR imaging classified pediatric posterior fossa tumors with high accuracy compared with manual expert pipeline optimization and qualitative expert MR imaging review.

  7. Ziegelitz, D.

    1. Adult Brain
      Ventricular Volume Is More Strongly Associated with Clinical Improvement Than the Evans Index after Shunting in Idiopathic Normal Pressure Hydrocephalus
      J. Neikter, S. Agerskov, P. Hellström, M. Tullberg, G. Starck, D. Ziegelitz and D. Farahmand
      American Journal of Neuroradiology July 2020, 41 (7) 1187-1192; DOI: https://doi.org/10.3174/ajnr.A6620

  8. Zou, B.

    1. EDITOR'S CHOICEPediatric Neuroimaging
      Open Access
      Automatic Machine Learning to Differentiate Pediatric Posterior Fossa Tumors on Routine MR Imaging
      H. Zhou, R. Hu, O. Tang, C. Hu, L. Tang, K. Chang, Q. Shen, J. Wu, B. Zou, B. Xiao, J. Boxerman, W. Chen, R.Y. Huang, L. Yang, H.X. Bai and C. Zhu
      American Journal of Neuroradiology July 2020, 41 (7) 1279-1285; DOI: https://doi.org/10.3174/ajnr.A6621

      This retrospective study included preoperative MR imaging of 288 pediatric patients with pediatric posterior fossa tumors, including medulloblastoma (n=111), ependymoma (n=70), and pilocytic astrocytoma (n=107). Radiomics features were extracted from T2-weighted images, contrast-enhanced T1-weighted images, and ADC maps. Models generated by standard manual optimization by a machine learning expert were compared with automatic machine learning via the Tree-Based Pipeline Optimization Tool for performance evaluation. The authors conclude that automatic machine learning based on routine MR imaging classified pediatric posterior fossa tumors with high accuracy compared with manual expert pipeline optimization and qualitative expert MR imaging review.

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