Index by author
Parekh, M.R.
- Head and Neck ImagingYou have accessA Simple Formula to Estimate Parathyroid Weight on 4D-CT, Predict Pathologic Weight, and Diagnose Parathyroid Adenoma in Patients with Primary HyperparathyroidismR. Yeh, Y.-K.D. Tay, L. Dercle, L. Bandeira, M.R. Parekh and J.P. BilezikianAmerican Journal of Neuroradiology September 2020, 41 (9) 1690-1697; DOI: https://doi.org/10.3174/ajnr.A6687
Patel, S.
- Adult BrainOpen AccessCOVID-19 and Involvement of the Corpus Callosum: Potential Effect of the Cytokine Storm?C. Rasmussen, I. Niculescu, S. Patel and A. KrishnanAmerican Journal of Neuroradiology September 2020, 41 (9) 1625-1628; DOI: https://doi.org/10.3174/ajnr.A6680
Patel, S.C.
- Extracranial VascularOpen AccessIntraluminal Carotid Artery Thrombus in COVID-19: Another Danger of Cytokine Storm?A.Y. Mohamud, B. Griffith, M. Rehman, D. Miller, A. Chebl, S.C. Patel, B. Howell, M. Kole and H. MarinAmerican Journal of Neuroradiology September 2020, 41 (9) 1677-1682; DOI: https://doi.org/10.3174/ajnr.A6674
Petracca, M.
- LetterYou have accessThe Development of Subcortical Gray Matter Atrophy in Multiple Sclerosis: One Size Does Not Fit AllG. Pontillo, M. Petracca, S. Cocozza and A. BrunettiAmerican Journal of Neuroradiology September 2020, 41 (9) E80-E81; DOI: https://doi.org/10.3174/ajnr.A6698
Peyre, H.
- PediatricsYou have accessAssessment of Maturational Changes in White Matter Anisotropy and Volume in Children: A DTI StudyG. Coll, E. de Schlichting, L. Sakka, J.-M. Garcier, H. Peyre and J.-J. LemaireAmerican Journal of Neuroradiology September 2020, 41 (9) 1726-1732; DOI: https://doi.org/10.3174/ajnr.A6709
Piotin, M.
- NeurointerventionYou have accessFusion Image Guidance for Supra-Aortic Vessel Catheterization in Neurointerventions: A Feasibility StudyA. Feddal, S. Escalard, F. Delvoye, R. Fahed, J.P. Desilles, K. Zuber, H. Redjem, J.S. Savatovsky, G. Ciccio, S. Smajda, M. Ben Maacha, M. Mazighi, M. Piotin and R. BlancAmerican Journal of Neuroradiology September 2020, 41 (9) 1663-1669; DOI: https://doi.org/10.3174/ajnr.A6707
Pontillo, G.
- LetterYou have accessThe Development of Subcortical Gray Matter Atrophy in Multiple Sclerosis: One Size Does Not Fit AllG. Pontillo, M. Petracca, S. Cocozza and A. BrunettiAmerican Journal of Neuroradiology September 2020, 41 (9) E80-E81; DOI: https://doi.org/10.3174/ajnr.A6698
Poussaint, T.Y.
- EDITOR'S CHOICEPediatricsYou have accessDeep Learning for Pediatric Posterior Fossa Tumor Detection and Classification: A Multi-Institutional StudyJ.L. Quon, W. Bala, L.C. Chen, J. Wright, L.H. Kim, M. Han, K. Shpanskaya, E.H. Lee, E. Tong, M. Iv, J. Seekins, M.P. Lungren, K.R.M. Braun, T.Y. Poussaint, S. Laughlin, M.D. Taylor, R.M. Lober, H. Vogel, P.G. Fisher, G.A. Grant, V. Ramaswamy, N.A. Vitanza, C.Y. Ho, M.S.B. Edwards, S.H. Cheshier and K.W. YeomAmerican Journal of Neuroradiology September 2020, 41 (9) 1718-1725; DOI: https://doi.org/10.3174/ajnr.A6704
This study cohort comprised 617 children (median age, 92 months; 56% males) from 5 pediatric institutions with posterior fossa tumors: diffuse midline glioma of the pons, medulloblastoma, pilocytic astrocytoma, and ependymoma. There were 199 controls. Tumor histology served as ground truth except for diffuse midline glioma of the pons, which was primarily diagnosed by MR imaging. A modified ResNeXt-50-32x4d architecture served as the backbone for a multitask classifier model, using T2-weighted MRI as input to detect the presence of tumor and predict tumor class. Model tumor detection accuracy exceeded an AUC of 0.99 and was similar to that of 4 radiologists. Model tumor classification accuracy was 92% with an F1 score of 0.80. The model was most accurate at predicting diffuse midline glioma of the pons, followed by pilocytic astrocytoma and medulloblastoma. Ependymoma prediction was the least accurate.
Purcell, Y.
- FELLOWS' JOURNAL CLUBPediatric NeuroimagingYou have accessFocal Areas of High Signal Intensity in Children with Neurofibromatosis Type 1: Expected Evolution on MRIS. Calvez, R. Levy, R. Calvez, C.-J. Roux, D. Grévent, Y. Purcell, K. Beccaria, T. Blauwblomme, J. Grill, C. Dufour, F. Bourdeaut, F. Doz, M.P. Robert, N. Boddaert and V. Dangouloff-RosAmerican Journal of Neuroradiology September 2020, 41 (9) 1733-1739; DOI: https://doi.org/10.3174/ajnr.A6740
The authors retrospectively examined the MRI of children diagnosed with neurofibromatosis type 1 using the National Institutes of Health Consensus Criteria (1987), with imaging follow-up of at least 4 years. They recorded the number, size, and surface area of focal areas of high signal intensity according to their anatomic distribution on T2WI/T2-FLAIR sequences. A generalized mixed model was used to analyze the evolution of focal areas of high signal intensity according to age, and separate analyses were performed for girls and boys. Thirty-nine patients with a median follow-up of 7 years were analyzed. Focal areas of high signal intensity were found in 100% of patients, preferentially in the infratentorial white matter (35% cerebellum, 30% brain stem) and in the capsular lenticular region (22%). They measured 15mm in 95% of cases. The areas appeared from the age of 1 year; increased in number, size, and surface area to a peak at the age of 7; and then spontaneously regressed by 17 years of age. The authors conclude that the study suggests that the evolution of focal areas of high signal intensity is not related to puberty and has a peak at the age of 7 years.
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