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Welcome to the new AJNR, Updated Hall of Fame, and more. Read the full announcements.


AJNR is seeking candidates for the position of Associate Section Editor, AJNR Case Collection. Read the full announcement.

 

Index by author

March 01, 2019; Volume 40,Issue 3
  • A
  • B
  • C
  • D
  • E
  • F
  • G
  • H
  • I
  • J
  • K
  • L
  • M
  • N
  • O
  • P
  • Q
  • R
  • S
  • T
  • U
  • V
  • W
  • X
  • Y
  • Z

  1. Hernesniemi, J.

    1. Interventional
      Open Access
      Local Hemodynamic Conditions Associated with Focal Changes in the Intracranial Aneurysm Wall
      J.R. Cebral, F. Detmer, B.J. Chung, J. Choque-Velasquez, B. Rezai, H. Lehto, R. Tulamo, J. Hernesniemi, M. Niemela, A. Yu, R. Williamson, K. Aziz, S. Sakur, S. Amin-Hanjani, F. Charbel, Y. Tobe, A. Robertson and J. Frösen
      American Journal of Neuroradiology March 2019, 40 (3) 510-516; DOI: https://doi.org/10.3174/ajnr.A5970
  2. Hill, M.D.

    1. EDITOR'S CHOICE
      You have access
      Imaging of Patients with Suspected Large-Vessel Occlusion at Primary Stroke Centers: Available Modalities and a Suggested Approach
      M.A. Almekhlafi, W.G. Kunz, B.K. Menon, R.A. McTaggart, M.V. Jayaraman, B.W. Baxter, D. Heck, D. Frei, C.P. Derdeyn, T. Takagi, A.H. Aamodt, I.M.R. Fragata, M.D. Hill, A.M. Demchuk and M. Goyal
      American Journal of Neuroradiology March 2019, 40 (3) 396-400; DOI: https://doi.org/10.3174/ajnr.A5971

      Endovascular thrombectomy has proven efficacy for a wide range of patients with large-vessel occlusion stroke and in selected cases up to 24 hours from onset. While primary stroke centers have increased the proportion of patients withstroke receiving thrombolytic therapy, delays can be encountereduntil patients with LVO are identified and transferred from the primary stroke center to acomprehensive stroke center. Therefore, any extra steps need to be carefullyweighed. The use of CTA (especially multiphase) at the primary stroke center levelhas many advantages in expediting the transfer of appropriate patients to a comprehensive center.

  3. Hoang, A.

    1. Adult Brain
      Open Access
      Dynamic Contrast-Enhanced MRI Reveals Unique Blood-Brain Barrier Permeability Characteristics in the Hippocampus in the Normal Brain
      J. Ivanidze, M. Mackay, A. Hoang, J.M. Chi, K. Cheng, C. Aranow, B. Volpe, B. Diamond and P.C. Sanelli
      American Journal of Neuroradiology March 2019, 40 (3) 408-411; DOI: https://doi.org/10.3174/ajnr.A5962
    2. Adult Brain
      Open Access
      Alterations in Blood-Brain Barrier Permeability in Patients with Systemic Lupus Erythematosus
      J.M. Chi, M. Mackay, A. Hoang, K. Cheng, C. Aranow, J. Ivanidze, B. Volpe, B. Diamond and P.C. Sanelli
      American Journal of Neuroradiology March 2019, 40 (3) 470-477; DOI: https://doi.org/10.3174/ajnr.A5990
  4. Hoch, M.J.

    1. EDITOR'S CHOICEAdult Brain
      Open Access
      3T MRI Whole-Brain Microscopy Discrimination of Subcortical Anatomy, Part 1: Brain Stem
      M.J. Hoch, M.T. Bruno, A. Faustin, N. Cruz, L. Crandall, T. Wisniewski, O. Devinsky and T.M. Shepherd
      American Journal of Neuroradiology March 2019, 40 (3) 401-407; DOI: https://doi.org/10.3174/ajnr.A5956

      The authors applied an optimized TSE T2 sequence to washed postmortem brain samples to reveal exquisite and reproducible brain stem anatomic MR imaging contrast comparable with histologic atlases. Direct TSE MR imaging sequence discrimination of brain stem anatomy can help validate other MR imaging contrasts, such as diffusion tractography, or serve as a structural template for extracting quantitative MR imaging data in future postmortem investigations.

  5. Hong, J.-S.

    1. Interventional
      Open Access
      Validating the Automatic Independent Component Analysis of DSA
      J.-S. Hong, Y.-H. Kao, F.-C. Chang and C.-J. Lin
      American Journal of Neuroradiology March 2019, 40 (3) 540-542; DOI: https://doi.org/10.3174/ajnr.A5963
  6. Horakova, D.

    1. Adult Brain
      You have access
      A Serial 10-Year Follow-Up Study of Atrophied Brain Lesion Volume and Disability Progression in Patients with Relapsing-Remitting MS
      R. Zivadinov, D. Horakova, N. Bergsland, J. Hagemeier, D.P. Ramasamy, T. Uher, M. Vaneckova, E. Havrdova and M.G. Dwyer
      American Journal of Neuroradiology March 2019, 40 (3) 446-452; DOI: https://doi.org/10.3174/ajnr.A5987
  7. Hoxworth, J.M.

    1. FELLOWS' JOURNAL CLUBAdult Brain
      Open Access
      Accurate Patient-Specific Machine Learning Models of Glioblastoma Invasion Using Transfer Learning
      L.S. Hu, H. Yoon, J.M. Eschbacher, L.C. Baxter, A.C. Dueck, A. Nespodzany, K.A. Smith, P. Nakaji, Y. Xu, L. Wang, J.P. Karis, A.J. Hawkins-Daarud, K.W. Singleton, P.R. Jackson, B.J. Anderies, B.R. Bendok, R.S. Zimmerman, C. Quarles, A.B. Porter-Umphrey, M.M. Mrugala, A. Sharma, J.M. Hoxworth, M.G. Sattur, N. Sanai, P.E. Koulemberis, C. Krishna, J.R. Mitchell, T. Wu, N.L. Tran, K.R. Swanson and J. Li
      American Journal of Neuroradiology March 2019, 40 (3) 418-425; DOI: https://doi.org/10.3174/ajnr.A5981

      The authors evaluated tumor cell density using a transfer learning method that generates individualized patient models, grounded in the wealth of population data, while also detecting and adjusting for interpatient variabilities based on each patient's own histologic data. They collected 82 image-recorded biopsy samples, from 18 patients with primary GBM. With multivariate modeling, transfer learning improved performance (r = 0.88) compared with one-model-fits-all (r = 0.39). They conclude that transfer learning significantly improves predictive modeling performance for quantifying tumor cell density in glioblastoma.

  8. Hu, L.S.

    1. FELLOWS' JOURNAL CLUBAdult Brain
      Open Access
      Accurate Patient-Specific Machine Learning Models of Glioblastoma Invasion Using Transfer Learning
      L.S. Hu, H. Yoon, J.M. Eschbacher, L.C. Baxter, A.C. Dueck, A. Nespodzany, K.A. Smith, P. Nakaji, Y. Xu, L. Wang, J.P. Karis, A.J. Hawkins-Daarud, K.W. Singleton, P.R. Jackson, B.J. Anderies, B.R. Bendok, R.S. Zimmerman, C. Quarles, A.B. Porter-Umphrey, M.M. Mrugala, A. Sharma, J.M. Hoxworth, M.G. Sattur, N. Sanai, P.E. Koulemberis, C. Krishna, J.R. Mitchell, T. Wu, N.L. Tran, K.R. Swanson and J. Li
      American Journal of Neuroradiology March 2019, 40 (3) 418-425; DOI: https://doi.org/10.3174/ajnr.A5981

      The authors evaluated tumor cell density using a transfer learning method that generates individualized patient models, grounded in the wealth of population data, while also detecting and adjusting for interpatient variabilities based on each patient's own histologic data. They collected 82 image-recorded biopsy samples, from 18 patients with primary GBM. With multivariate modeling, transfer learning improved performance (r = 0.88) compared with one-model-fits-all (r = 0.39). They conclude that transfer learning significantly improves predictive modeling performance for quantifying tumor cell density in glioblastoma.

  9. Hygino Da Cruz, L.C.

    1. LETTER
      You have access
      High-Resolution Vessel Wall MR Imaging as an Alternative to Brain Biopsy
      D.G. Corrêa and L.C. Hygino da Cruz
      American Journal of Neuroradiology March 2019, 40 (3) E17-E18; DOI: https://doi.org/10.3174/ajnr.A5950
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American Journal of Neuroradiology: 40 (3)
American Journal of Neuroradiology
Vol. 40, Issue 3
1 Mar 2019
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