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Index by author

May 01, 2025; Volume 46,Issue 5

  1. Youssef, Michael

    1. Brain Tumor Imaging
      Repeatability and Reproducibility of Pseudocontinuous Arterial Spin-Labeling–Measured Brain Perfusion in Healthy Volunteers and Patients with Glioblastoma
      Limin Zhou, Durga Udayakumar, Yiming Wang, Marco C. Pinho, Benjamin C. Wagner, Michael Youssef, Joseph A. Maldjian and Ananth J. Madhuranthakam
      American Journal of Neuroradiology May 2025, 46 (5) 973-982; DOI: https://doi.org/10.3174/ajnr.A8551

  2. Zapaishchykova, Anna

    1. Artificial Intelligence
      Empowering Data Sharing in Neuroscience: A Deep Learning Deidentification Method for Pediatric Brain MRIs
      Ariana M. Familiar, Neda Khalili, Nastaran Khalili, Cassidy Schuman, Evan Grove, Karthik Viswanathan, Jakob Seidlitz, Aaron Alexander-Bloch, Anna Zapaishchykova, Benjamin H. Kann, Arastoo Vossough, Phillip B. Storm, Adam C. Resnick, Anahita Fathi Kazerooni and Ali Nabavizadeh
      American Journal of Neuroradiology May 2025, 46 (5) 964-972; DOI: https://doi.org/10.3174/ajnr.A8581

  3. Zarrintan, Armin

    1. Neurointervention
      Stent Angioplasty for the Treatment of Cerebral Vasospasm: A Systematic Review and Meta-Analysis
      Jonathan Cortese, Esref Alperen Bayraktar, Sherief Ghozy, Armin Zarrintan, Cem Bilgin, Waleed Brinjikji, Ramanathan Kadirvel, Pervinder Bhogal and David F. Kallmes
      American Journal of Neuroradiology May 2025, 46 (5) 943-949; DOI: https://doi.org/10.3174/ajnr.A8617

  4. Zhang, Dylan

    1. EDITOR'S CHOICEBrain Tumor Imaging
      Development and Evaluation of Automated Artificial Intelligence–Based Brain Tumor Response Assessment in Patients with Glioblastoma
      Jikai Zhang, Dominic LaBella, Dylan Zhang, Jessica L. Houk, Jeffrey D. Rudie, Haotian Zou, Pranav Warman, Maciej A. Mazurowski and Evan Calabrese
      American Journal of Neuroradiology May 2025, 46 (5) 990-998; DOI: https://doi.org/10.3174/ajnr.A8580

      The goal of this study was to compare AI-based volumetric GBM MRI response assessment with standardized radiologist response assessments. The AI-based volumetric response assessment yielded overall moderate performance for recapitulating most human response assessment categories (BT-RADS 1, 2, and 4) but demonstrated the lowest performance for predicting BT-RADS 3, which is likely related to the high variability of this assessment. In comparison to radiologist assessment, the AI-based volumetric GBM MRI response assessment showed comparable performance for overall survival.

  5. Zhang, Jikai

    1. EDITOR'S CHOICEBrain Tumor Imaging
      Development and Evaluation of Automated Artificial Intelligence–Based Brain Tumor Response Assessment in Patients with Glioblastoma
      Jikai Zhang, Dominic LaBella, Dylan Zhang, Jessica L. Houk, Jeffrey D. Rudie, Haotian Zou, Pranav Warman, Maciej A. Mazurowski and Evan Calabrese
      American Journal of Neuroradiology May 2025, 46 (5) 990-998; DOI: https://doi.org/10.3174/ajnr.A8580

      The goal of this study was to compare AI-based volumetric GBM MRI response assessment with standardized radiologist response assessments. The AI-based volumetric response assessment yielded overall moderate performance for recapitulating most human response assessment categories (BT-RADS 1, 2, and 4) but demonstrated the lowest performance for predicting BT-RADS 3, which is likely related to the high variability of this assessment. In comparison to radiologist assessment, the AI-based volumetric GBM MRI response assessment showed comparable performance for overall survival.

  6. Zhang, Zhonghe

    1. Pediatric Neuroimaging
      Mapping Fetal Brain Development of 10 Weeks’ Gestational Age with 9.4T Postmortem MRI and Histologic Sections
      Zhonghe Zhang, Yue Gao, Xiangtao Lin, Xue He, Ximing Wang and Shuwei Liu
      American Journal of Neuroradiology May 2025, 46 (5) 1029-1035; DOI: https://doi.org/10.3174/ajnr.A8595

  7. Zhou, Limin

    1. Brain Tumor Imaging
      Repeatability and Reproducibility of Pseudocontinuous Arterial Spin-Labeling–Measured Brain Perfusion in Healthy Volunteers and Patients with Glioblastoma
      Limin Zhou, Durga Udayakumar, Yiming Wang, Marco C. Pinho, Benjamin C. Wagner, Michael Youssef, Joseph A. Maldjian and Ananth J. Madhuranthakam
      American Journal of Neuroradiology May 2025, 46 (5) 973-982; DOI: https://doi.org/10.3174/ajnr.A8551

  8. Zou, Haotian

    1. EDITOR'S CHOICEBrain Tumor Imaging
      Development and Evaluation of Automated Artificial Intelligence–Based Brain Tumor Response Assessment in Patients with Glioblastoma
      Jikai Zhang, Dominic LaBella, Dylan Zhang, Jessica L. Houk, Jeffrey D. Rudie, Haotian Zou, Pranav Warman, Maciej A. Mazurowski and Evan Calabrese
      American Journal of Neuroradiology May 2025, 46 (5) 990-998; DOI: https://doi.org/10.3174/ajnr.A8580

      The goal of this study was to compare AI-based volumetric GBM MRI response assessment with standardized radiologist response assessments. The AI-based volumetric response assessment yielded overall moderate performance for recapitulating most human response assessment categories (BT-RADS 1, 2, and 4) but demonstrated the lowest performance for predicting BT-RADS 3, which is likely related to the high variability of this assessment. In comparison to radiologist assessment, the AI-based volumetric GBM MRI response assessment showed comparable performance for overall survival.

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