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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. Paliwal, N.

    1. Neurointervention
      Open Access
      Ostium Ratio and Neck Ratio Could Predict the Outcome of Sidewall Intracranial Aneurysms Treated with Flow Diverters
      N. Paliwal, V.M. Tutino, H. Shallwani, J.S. Beecher, R.J. Damiano, H.J. Shakir, G.S. Atwal, V.S. Fennell, S.K. Natarajan, E.I. Levy, A.H. Siddiqui, J.M. Davies and H. Meng
      American Journal of Neuroradiology February 2019, 40 (2) 288-294; DOI: https://doi.org/10.3174/ajnr.A5953
  2. Patil, U.

    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. Peck, K.

    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
  4. Pedersen, R.C.

    1. Letter
      You have access
      Reply:
      V.J. Rooks, L. Ruess, G.W. Peterman and R.C. Pedersen
      American Journal of Neuroradiology February 2019, 40 (2) E11; DOI: https://doi.org/10.3174/ajnr.A5969
  5. Peterman, G.W.

    1. Letter
      You have access
      Reply:
      V.J. Rooks, L. Ruess, G.W. Peterman and R.C. Pedersen
      American Journal of Neuroradiology February 2019, 40 (2) E11; DOI: https://doi.org/10.3174/ajnr.A5969
  6. Pfaff, J.

    1. Neurointervention
      You have access
      Clinical Outcome after Thrombectomy in Patients with Stroke with Premorbid Modified Rankin Scale Scores of 3 and 4: A Cohort Study with 136 Patients
      F. Seker, J. Pfaff, S. Schönenberger, C. Herweh, S. Nagel, P.A. Ringleb, M. Bendszus and M.A. Möhlenbruch
      American Journal of Neuroradiology February 2019, 40 (2) 283-286; DOI: https://doi.org/10.3174/ajnr.A5920
  7. 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
  8. 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.

  9. 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
  10. 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.

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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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