Index by author
Pfeifer, C.M.
- LetterYou have accessMaternal-Fetal Medicine Specialists Should Manage Patients Requiring Fetal MRI of the Central Nervous SystemC.M. PfeiferAmerican Journal of Neuroradiology February 2019, 40 (2) E6; DOI: https://doi.org/10.3174/ajnr.A5894
Pilitsis, J.
- FELLOWS' JOURNAL CLUBAdult BrainYou have accessA Deep Learning–Based Approach to Reduce Rescan and Recall Rates in Clinical MRI ExaminationsA. 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. HancuAmerican 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.
Pisani, L.
- Review ArticleOpen AccessA Review of Magnetic Particle Imaging and Perspectives on NeuroimagingL.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. WintermarkAmerican Journal of Neuroradiology February 2019, 40 (2) 206-212; DOI: https://doi.org/10.3174/ajnr.A5896
Polzin, J.
- FELLOWS' JOURNAL CLUBAdult BrainYou have accessA Deep Learning–Based Approach to Reduce Rescan and Recall Rates in Clinical MRI ExaminationsA. 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. HancuAmerican 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.
Port, J.
- FELLOWS' JOURNAL CLUBAdult BrainYou have accessA Deep Learning–Based Approach to Reduce Rescan and Recall Rates in Clinical MRI ExaminationsA. 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. HancuAmerican 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.
Qiao, Z.
- EDITOR'S CHOICEAdult BrainOpen AccessUtility 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 GliomasZ. Qiao, X. Zhao, K. Wang, Y. Zhang, D. Fan, T. Yu, H. Shen, Q. Chen and L. AiAmerican 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.
Qu, H.
- Review ArticleOpen AccessA Review of Magnetic Particle Imaging and Perspectives on NeuroimagingL.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. WintermarkAmerican Journal of Neuroradiology February 2019, 40 (2) 206-212; DOI: https://doi.org/10.3174/ajnr.A5896
Rae-grant, A.
- Adult BrainYou have accessFDG-PET and MRI in the Evolution of New-Onset Refractory Status EpilepticusT. Strohm, C. Steriade, G. Wu, S. Hantus, A. Rae-Grant and M. LarvieAmerican Journal of Neuroradiology February 2019, 40 (2) 238-244; DOI: https://doi.org/10.3174/ajnr.A5929
Rajagopal, R.
- LetterYou have accessReply:R. Rajagopal and S. SharmaAmerican Journal of Neuroradiology February 2019, 40 (2) E9; DOI: https://doi.org/10.3174/ajnr.A5965
Rajeev-kumar, G.
- FunctionalOpen AccessResting-State Functional Connectivity of the Middle Frontal Gyrus Can Predict Language Lateralization in Patients with Brain TumorsS. Gohel, M.E. Laino, G. Rajeev-Kumar, M. Jenabi, K. Peck, V. Hatzoglou, V. Tabar, A.I. Holodny and B. VachhaAmerican Journal of Neuroradiology February 2019, 40 (2) 319-325; DOI: https://doi.org/10.3174/ajnr.A5932