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AJNR Awards, New Junior Editors, and more. Read the latest AJNR updates

Index by author

February 01, 2019; Volume 40,Issue 2
  • 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. Pfeifer, C.M.

    1. Letter
      You have access
      Maternal-Fetal Medicine Specialists Should Manage Patients Requiring Fetal MRI of the Central Nervous System
      C.M. Pfeifer
      American Journal of Neuroradiology February 2019, 40 (2) E6; DOI: https://doi.org/10.3174/ajnr.A5894
  2. Pilitsis, J.

    1. FELLOWS' JOURNAL CLUBAdult Brain
      You have access
      A Deep Learning–Based Approach to Reduce Rescan and Recall Rates in Clinical MRI Examinations
      A. Sreekumari, D. Shanbhag, D. Yeo, T. Foo, J. Pilitsis, J. Polzin, U. Patil, A. Coblentz, A. Kapadia, J. Khinda, A. Boutet, J. Port and I. Hancu
      American Journal of Neuroradiology February 2019, 40 (2) 217-223; DOI: https://doi.org/10.3174/ajnr.A5926

      The purpose of this study was to develop a fast, automated method for assessing rescan need in motion-corrupted brain series. A deep learning–based approach was developed, outputting a probability for a series to be clinically useful. Comparison of this per-series probability with a threshold, which can depend on scan indication and reading radiologist, determines whether a series needs to be rescanned. The deep learning classification performance was compared with that of 4 technologists and 5 radiologists in 49 test series with low and moderate motion artifacts. Fast, automated deep learning–based image-quality rating can decrease rescan and recall rates, while rendering them technologist-independent. It was estimated that decreasing rescans and recalls from the technologists' values to the values of deep learning could save hospitals $24,000/scanner/year.

  3. Pisani, L.

    1. Review Article
      Open Access
      A Review of Magnetic Particle Imaging and Perspectives on Neuroimaging
      L.C. Wu, Y. Zhang, G. Steinberg, H. Qu, S. Huang, M. Cheng, T. Bliss, F. Du, J. Rao, G. Song, L. Pisani, T. Doyle, S. Conolly, K. Krishnan, G. Grant and M. Wintermark
      American Journal of Neuroradiology February 2019, 40 (2) 206-212; DOI: https://doi.org/10.3174/ajnr.A5896
  4. Polzin, J.

    1. FELLOWS' JOURNAL CLUBAdult Brain
      You have access
      A Deep Learning–Based Approach to Reduce Rescan and Recall Rates in Clinical MRI Examinations
      A. Sreekumari, D. Shanbhag, D. Yeo, T. Foo, J. Pilitsis, J. Polzin, U. Patil, A. Coblentz, A. Kapadia, J. Khinda, A. Boutet, J. Port and I. Hancu
      American Journal of Neuroradiology February 2019, 40 (2) 217-223; DOI: https://doi.org/10.3174/ajnr.A5926

      The purpose of this study was to develop a fast, automated method for assessing rescan need in motion-corrupted brain series. A deep learning–based approach was developed, outputting a probability for a series to be clinically useful. Comparison of this per-series probability with a threshold, which can depend on scan indication and reading radiologist, determines whether a series needs to be rescanned. The deep learning classification performance was compared with that of 4 technologists and 5 radiologists in 49 test series with low and moderate motion artifacts. Fast, automated deep learning–based image-quality rating can decrease rescan and recall rates, while rendering them technologist-independent. It was estimated that decreasing rescans and recalls from the technologists' values to the values of deep learning could save hospitals $24,000/scanner/year.

  5. Port, J.

    1. FELLOWS' JOURNAL CLUBAdult Brain
      You have access
      A Deep Learning–Based Approach to Reduce Rescan and Recall Rates in Clinical MRI Examinations
      A. Sreekumari, D. Shanbhag, D. Yeo, T. Foo, J. Pilitsis, J. Polzin, U. Patil, A. Coblentz, A. Kapadia, J. Khinda, A. Boutet, J. Port and I. Hancu
      American Journal of Neuroradiology February 2019, 40 (2) 217-223; DOI: https://doi.org/10.3174/ajnr.A5926

      The purpose of this study was to develop a fast, automated method for assessing rescan need in motion-corrupted brain series. A deep learning–based approach was developed, outputting a probability for a series to be clinically useful. Comparison of this per-series probability with a threshold, which can depend on scan indication and reading radiologist, determines whether a series needs to be rescanned. The deep learning classification performance was compared with that of 4 technologists and 5 radiologists in 49 test series with low and moderate motion artifacts. Fast, automated deep learning–based image-quality rating can decrease rescan and recall rates, while rendering them technologist-independent. It was estimated that decreasing rescans and recalls from the technologists' values to the values of deep learning could save hospitals $24,000/scanner/year.

  6. Qiao, Z.

    1. EDITOR'S CHOICEAdult Brain
      Open Access
      Utility of Dynamic Susceptibility Contrast Perfusion-Weighted MR Imaging and 11C-Methionine PET/CT for Differentiation of Tumor Recurrence from Radiation Injury in Patients with High-Grade Gliomas
      Z. Qiao, X. Zhao, K. Wang, Y. Zhang, D. Fan, T. Yu, H. Shen, Q. Chen and L. Ai
      American Journal of Neuroradiology February 2019, 40 (2) 253-259; DOI: https://doi.org/10.3174/ajnr.A5952

      Forty-two patients with high-grade gliomas were enrolled in this study. The final diagnosis was determined by histopathologic analysis or clinical follow-up. PWI and PET parameters were recorded and compared between patients with recurrence and those with radiation injury using Student t tests. Receiver operating characteristic and logistic regression analyses were used to determine the diagnostic performance of each parameter. The final diagnosis was recurrence in 33 patients and radiation injury in 9. PET/CT showed a patient-based sensitivity and specificity of 0.909 and 0.556, respectively, while PWI showed values of 0.667 and 0.778, respectively. The maximum standardized uptake value, mean standardized uptake value, tumor-to-background maximum standardized uptake value, and mean relative CBV were significantly higher for patients with recurrence than for patients with radiation injury. All these parameters showed a significant discriminative power in receiver operating characteristic analysis. Both 11C-methionine PET/CT and PWI are equally accurate in the differentiation of recurrence from radiation injury in patients with high-grade gliomas, and a combination of the 2 modalities could result in increased diagnostic accuracy.

  7. Qu, H.

    1. Review Article
      Open Access
      A Review of Magnetic Particle Imaging and Perspectives on Neuroimaging
      L.C. Wu, Y. Zhang, G. Steinberg, H. Qu, S. Huang, M. Cheng, T. Bliss, F. Du, J. Rao, G. Song, L. Pisani, T. Doyle, S. Conolly, K. Krishnan, G. Grant and M. Wintermark
      American Journal of Neuroradiology February 2019, 40 (2) 206-212; DOI: https://doi.org/10.3174/ajnr.A5896
  8. Rae-grant, A.

    1. Adult Brain
      You have access
      FDG-PET and MRI in the Evolution of New-Onset Refractory Status Epilepticus
      T. Strohm, C. Steriade, G. Wu, S. Hantus, A. Rae-Grant and M. Larvie
      American Journal of Neuroradiology February 2019, 40 (2) 238-244; DOI: https://doi.org/10.3174/ajnr.A5929
  9. Rajagopal, R.

    1. Letter
      You have access
      Reply:
      R. Rajagopal and S. Sharma
      American Journal of Neuroradiology February 2019, 40 (2) E9; DOI: https://doi.org/10.3174/ajnr.A5965
  10. Rajeev-kumar, G.

    1. Functional
      Open Access
      Resting-State Functional Connectivity of the Middle Frontal Gyrus Can Predict Language Lateralization in Patients with Brain Tumors
      S. Gohel, M.E. Laino, G. Rajeev-Kumar, M. Jenabi, K. Peck, V. Hatzoglou, V. Tabar, A.I. Holodny and B. Vachha
      American Journal of Neuroradiology February 2019, 40 (2) 319-325; DOI: https://doi.org/10.3174/ajnr.A5932
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American Journal of Neuroradiology: 40 (2)
American Journal of Neuroradiology
Vol. 40, Issue 2
1 Feb 2019
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