Impact of software modeling on the accuracy of perfusion MRI in glioma

Autor: Timothy J. Kaufmann, Christian Elrod, Bradley J. Erickson, Deanna H. Pafundi, Benjamin M. Ellingson, Leslie C. Baxter, Peter Nakaji, Debra Brinkman, Jennifer M. Eschbacher, Leland S. Hu, Kris A. Smith, Zachary S. Kelm, John P. Karis, Panagiotis Korfiatis, Amylou C. Dueck

Publikováno v: AJNR. American journal of neuroradiology, vol 36, iss 12
Hu, LS; Kelm, Z; Korfiatis, P; Dueck, AC; Elrod, C; Ellingson, BM; et al.(2015). Impact of software modeling on the accuracy of perfusion MRI in glioma. American Journal of Neuroradiology, 36(12), 2242-2249. doi: 10.3174/ajnr.A4451. UCLA: Retrieved from: http://www.escholarship.org/uc/item/216339bt

Background and purposeRelative cerebral blood volume, as measured by T2*-weighted dynamic susceptibility-weighted contrast-enhanced MRI, represents the most robust and widely used perfusion MR imaging metric in neuro-oncology. Our aim was to determin

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f24eafd48ef8e01266b71e4313ab0823
https://europepmc.org/articles/PMC4681640/