Index by author
Lang, Min
- EDITOR'S CHOICEEmergency NeuroradiologyYou have accessClinical Evaluation of a 2-Minute Ultrafast Brain MR Protocol for Evaluation of Acute Pathology in the Emergency and Inpatient SettingsMin Lang, Bryan Clifford, Wei-Ching Lo, Brooks P. Applewhite, Azadeh Tabari, Augusto Lio M. Goncalves Filho, Zahra Hosseini, Maria Gabriela Figueiro Longo, Stephen F. Cauley, Kawin Setsompop, Berkin Bilgic, Thorsten Feiweier, Michael H. Lev, Pamela W. Schaefer, Otto Rapalino, Susie Y. Huang and John ConklinAmerican Journal of Neuroradiology April 2024, 45 (4) 379-385; DOI: https://doi.org/10.3174/ajnr.A8143
Ultrafast MR imaging (2.1 minutes) reconstructed with machine-learning assisted framework demonstrated significant reduction in motion artifacts and 98.5% agreement with the reference MR protocol (10 minutes) on the main clinical diagnosis, though there were greater image noise and geometric distortion on the ultrafast protocol.
Lansberg, Maarten G.
- EDITOR'S CHOICENeurovascular/Stroke ImagingYou have accessA Clinical and Imaging Fused Deep Learning Model Matches Expert Clinician Prediction of 90-Day Stroke OutcomesYongkai Liu, Preya Shah, Yannan Yu, Jai Horsey, Jiahong Ouyang, Bin Jiang, Guang Yang, Jeremy J. Heit, Margy E. McCullough-Hicks, Stephen M. Hugdal, Max Wintermark, Patrik Michel, David S. Liebeskind, Maarten G. Lansberg, Gregory W. Albers and Greg ZaharchukAmerican Journal of Neuroradiology April 2024, 45 (4) 406-411; DOI: https://doi.org/10.3174/ajnr.A8140
The authors in this study used a deep learning-based predictive model (DLPD) that incorporated DWI and clinical data from the acute period to predict 90-day mRS outcomes and compared its predictions with those made by physicians. The results showed that the clinical and imaging fused deep learning model is noninferior to expert physicians in predicting specific mRS outcomes and unfavorable prognoses.
Lanterna, Andrea Luigi
- FELLOWS' JOURNAL CLUBNeurovascular/Stroke ImagingYou have accessHemorrhage Volume Drives Early Brain Injury and Outcome in Poor-Grade Aneurysmal SAHPietro Panni, Franco Simionato, Roberta Cao, Alessandro Pedicelli, Enrico Marchese, Anselmo Caricato, Andrea Alexandre, Alberto Feletti, Mattia Testa, Paolo Zanatta, Nicola Gitti, Simone Piva, Dikran Mardighian, Vittorio Semeraro, Giordano Nardin, Emilio Lozupone, Giafranco Paiano, Edoardo Picetti, Vito Montanaro, Massimo Petranca, Carlo Bortolotti, Antonino Scibilia, Luigi Cirillo, Raffaele Aspide, Andrea Luigi Lanterna, Alessandro Ambrosi, Pietro Mortini, Maria Luisa Azzolini, Maria Rosa Calvi, Andrea Falini and on behalf of the POGASH InvestigatorsAmerican Journal of Neuroradiology April 2024, 45 (4) 393-399; DOI: https://doi.org/10.3174/ajnr.A8135
Early brain injury (radiologically defined by global cerebral edema) is a major determinant of clinical outcome in poor-grade aneurysmal SAH. In this retrospective study of 400 patients with poor-grade aneurysmal SAH, it was shown that intracerebral hemorrhage volume independently predicted global cerebral edema and long-term outcome, intraventricular hemorrhage volume predicted mortality and long-term outcome, and SAH volume predicted long-term clinical outcome.
Lanzman, Bryan A.
- Brain Tumor ImagingOpen AccessArterial Spin-Labeling and DSC Perfusion Metrics Improve Agreement in Neuroradiologists’ Clinical Interpretations of Posttreatment High-Grade Glioma Surveillance MR Imaging—An Institutional ExperienceGhiam Yamin, Eric Tranvinh, Bryan A. Lanzman, Elizabeth Tong, Syed S. Hashmi, Chirag B. Patel and Michael IvAmerican Journal of Neuroradiology April 2024, 45 (4) 453-460; DOI: https://doi.org/10.3174/ajnr.A8190
Lev, Michael H.
- EDITOR'S CHOICEEmergency NeuroradiologyYou have accessClinical Evaluation of a 2-Minute Ultrafast Brain MR Protocol for Evaluation of Acute Pathology in the Emergency and Inpatient SettingsMin Lang, Bryan Clifford, Wei-Ching Lo, Brooks P. Applewhite, Azadeh Tabari, Augusto Lio M. Goncalves Filho, Zahra Hosseini, Maria Gabriela Figueiro Longo, Stephen F. Cauley, Kawin Setsompop, Berkin Bilgic, Thorsten Feiweier, Michael H. Lev, Pamela W. Schaefer, Otto Rapalino, Susie Y. Huang and John ConklinAmerican Journal of Neuroradiology April 2024, 45 (4) 379-385; DOI: https://doi.org/10.3174/ajnr.A8143
Ultrafast MR imaging (2.1 minutes) reconstructed with machine-learning assisted framework demonstrated significant reduction in motion artifacts and 98.5% agreement with the reference MR protocol (10 minutes) on the main clinical diagnosis, though there were greater image noise and geometric distortion on the ultrafast protocol.
Li, Huan
- Brain Tumor ImagingYou have accessImproved Detection of Target Metabolites in Brain Tumors with Intermediate TE, High SNR, and High Bandwidth Spin-Echo Sequence at 5TWenbo Sun, Dan Xu, YanXing Yang, Linfei Wen, Hanjiang Yu, Yaowen Xing, Xiaopeng Song, Huan Li and Haibo XuAmerican Journal of Neuroradiology April 2024, 45 (4) 461-467; DOI: https://doi.org/10.3174/ajnr.A8150
Li, Linna
- Artificial IntelligenceOpen AccessCompressed Sensitivity Encoding Artificial Intelligence Accelerates Brain Metastasis Imaging by Optimizing Image Quality and Reducing Scan TimeMengmeng Wang, Yue Ma, Linna Li, Xingchen Pan, Yafei Wen, Ying Qiu, Dandan Guo, Yi Zhu, Jianxiu Lian and Dan TongAmerican Journal of Neuroradiology April 2024, 45 (4) 444-452; DOI: https://doi.org/10.3174/ajnr.A8161
Lian, Jianxiu
- Artificial IntelligenceOpen AccessCompressed Sensitivity Encoding Artificial Intelligence Accelerates Brain Metastasis Imaging by Optimizing Image Quality and Reducing Scan TimeMengmeng Wang, Yue Ma, Linna Li, Xingchen Pan, Yafei Wen, Ying Qiu, Dandan Guo, Yi Zhu, Jianxiu Lian and Dan TongAmerican Journal of Neuroradiology April 2024, 45 (4) 444-452; DOI: https://doi.org/10.3174/ajnr.A8161
Liebeskind, David S.
- EDITOR'S CHOICENeurovascular/Stroke ImagingYou have accessA Clinical and Imaging Fused Deep Learning Model Matches Expert Clinician Prediction of 90-Day Stroke OutcomesYongkai Liu, Preya Shah, Yannan Yu, Jai Horsey, Jiahong Ouyang, Bin Jiang, Guang Yang, Jeremy J. Heit, Margy E. McCullough-Hicks, Stephen M. Hugdal, Max Wintermark, Patrik Michel, David S. Liebeskind, Maarten G. Lansberg, Gregory W. Albers and Greg ZaharchukAmerican Journal of Neuroradiology April 2024, 45 (4) 406-411; DOI: https://doi.org/10.3174/ajnr.A8140
The authors in this study used a deep learning-based predictive model (DLPD) that incorporated DWI and clinical data from the acute period to predict 90-day mRS outcomes and compared its predictions with those made by physicians. The results showed that the clinical and imaging fused deep learning model is noninferior to expert physicians in predicting specific mRS outcomes and unfavorable prognoses.
Lin, MingDe
- Pediatric NeuroimagingYou have accessComparison of Volumetric and 2D Measurements and Longitudinal Trajectories in the Response Assessment of BRAF V600E-Mutant Pediatric Gliomas in the Pacific Pediatric Neuro-Oncology Consortium Clinical TrialDivya Ramakrishnan, Sarah C. Brüningk, Marc von Reppert, Fatima Memon, Nazanin Maleki, Sanjay Aneja, Anahita Fathi Kazerooni, Ali Nabavizadeh, MingDe Lin, Khaled Bousabarah, Annette Molinaro, Theodore Nicolaides, Michael Prados, Sabine Mueller and Mariam S. AboianAmerican Journal of Neuroradiology April 2024, 45 (4) 475-482; DOI: https://doi.org/10.3174/ajnr.A8189
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