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Index by author

February 01, 2019; Volume 40,Issue 2

  1. Du, F.

    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

  2. El Methni, J.

    1. Spine
      A 3T Phase-Sensitive Inversion Recovery MRI Sequence Improves Detection of Cervical Spinal Cord Lesions and Shows Active Lesions in Patients with Multiple Sclerosis
      A. Fechner, J. Savatovsky, J. El Methni, J.C. Sadik, O. Gout, R. Deschamps, A. Gueguen and A. Lecler
      American Journal of Neuroradiology February 2019, 40 (2) 370-375; DOI: https://doi.org/10.3174/ajnr.A5941

  3. Eriksson, A.

    1. LETTER
      Is Delayed Speech Development a Long-Term Sequela of Birth-Related Subdural Hematoma?
      N. Lynøe, D. Olsson and A. Eriksson
      American Journal of Neuroradiology February 2019, 40 (2) E10; DOI: https://doi.org/10.3174/ajnr.A5890

  4. Fan, D.

    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.

  5. Fechner, A.

    1. Spine
      A 3T Phase-Sensitive Inversion Recovery MRI Sequence Improves Detection of Cervical Spinal Cord Lesions and Shows Active Lesions in Patients with Multiple Sclerosis
      A. Fechner, J. Savatovsky, J. El Methni, J.C. Sadik, O. Gout, R. Deschamps, A. Gueguen and A. Lecler
      American Journal of Neuroradiology February 2019, 40 (2) 370-375; DOI: https://doi.org/10.3174/ajnr.A5941

  6. Fennel, V.

    1. Interventional
      Open Access
      High-Definition Zoom Mode, a High-Resolution X-Ray Microscope for Neurointerventional Treatment Procedures: A Blinded-Rater Clinical-Utility Study
      S.V. Setlur Nagesh, V. Fennel, J. Krebs, C. Ionita, J. Davies, D.R. Bednarek, M. Mokin, A.H. Siddiqui and S. Rudin
      American Journal of Neuroradiology February 2019, 40 (2) 302-308; DOI: https://doi.org/10.3174/ajnr.A5922

  7. Fennell, V.S.

    1. Interventional
      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

  8. Foo, T.

    1. FELLOWS' JOURNAL CLUBAdult Brain
      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. Frassanito, P.

    1. Pediatrics
      Brain DSC MR Perfusion in Children: A Clinical Feasibility Study Using Different Technical Standards of Contrast Administration
      S. Gaudino, M. Martucci, A. Botto, E. Ruberto, E. Leone, A. Infante, A. Ramaglia, M. Caldarelli, P. Frassanito, F.M. Triulzi and C. Colosimo
      American Journal of Neuroradiology February 2019, 40 (2) 359-365; DOI: https://doi.org/10.3174/ajnr.A5954

  10. Fujita, S.

    1. EDITOR'S CHOICEAdult Brain
      Open Access
      Improving the Quality of Synthetic FLAIR Images with Deep Learning Using a Conditional Generative Adversarial Network for Pixel-by-Pixel Image Translation
      A. Hagiwara, Y. Otsuka, M. Hori, Y. Tachibana, K. Yokoyama, S. Fujita, C. Andica, K. Kamagata, R. Irie, S. Koshino, T. Maekawa, L. Chougar, A. Wada, M.Y. Takemura, N. Hattori and S. Aoki
      American Journal of Neuroradiology February 2019, 40 (2) 224-230; DOI: https://doi.org/10.3174/ajnr.A5927

      Forty patients with MS were prospectively included and scanned (3T) to acquire synthetic MR imaging and conventional FLAIR images. Synthetic FLAIR images were created with the SyMRI software. Acquired data were divided into 30 training and 10 test datasets. A conditional generative adversarial network was trained to generate improved FLAIR images from raw synthetic MR imaging data using conventional FLAIR images as targets. The peak signal-to-noise ratio, normalized root mean square error, and the Dice index of MS lesion maps were calculated for synthetic and deep learning FLAIR images against conventional FLAIR images, respectively. Lesion conspicuity and the existence of artifacts were visually assessed. The peak signal-to-noise ratio and normalized root mean square error were significantly higher and lower, respectively, in generated-versus-synthetic FLAIR images in aggregate intracranial tissues and all tissue segments. The Dice index of lesion maps and visual lesion conspicuity were comparable between generated and synthetic FLAIR images. Using deep learning, the authors conclude that they improved the synthetic FLAIR image quality by generating FLAIR images that have contrast closer to that of conventional FLAIR images and fewer granular and swelling artifacts, while preserving the lesion contrast.

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