Abstract
Objectives The aim of the study is to assess the role of ultrafast (UF) magnetic resonance (MR) sequences in stroke imaging. Material and Methods We prospectively studied 85 patients having clinical suspicion of stroke referred for MR imaging (MRI) during August 2016 to July 2018. These patients were subjected to both conventional and UF MRI sequences. The patients were divided into six categories based on the pathologies encountered. Further subclassification was done based on the size of the lesions as 10 mm and >10 mm as seen separately in both UF and conventional MR sequences. The number and visibility of these lesions on conventional and UF MRI were compared. The image quality of all the subjects was also compared based on a scale categorized into excellent, satisfactory, and poor. The findings on conventional and UF imaging sequences were correlated with the final clinical diagnosis arrived at the time of discharge. Results In our study comprising 85 patients, 57 showed pathologies. The patients showing pathologies were assigned into the six categories as acute infarct (34 cases), acute hemorrhagic infarct (six cases), chronic infarct (17 cases), chronic hemorrhagic infarct (four cases), subacute infarct (three cases), and chronic hemorrhage (one case). The number of lesions seen on conventional and UF sequences were the same although there was a slight decrease in the size of the lesions on UF sequences as compared with conventional counterparts. The image quality using UF sequences was better in motion prone patients while conventional imaging showed better image quality in cooperative patients. Conclusion In motion prone patients, UF sequences are a suitable alternative for conventional sequences as they help in arriving at the diagnosis in lesser time, with reasonably good image quality, and without motion artifacts. In cooperative stroke patients, it is better to use conventional MR sequences as the image quality is better.
Copyright
Association for Helping Neurosurgical Sick People. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-Non Derivative-Non Commercial License, permitting copying and reproduction so long as the original work is given appropriate credit.
Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)
This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License, which permits unrestricted reproduction and distribution, for non-commercial purposes only; and use and reproduction, but not distribution, of adapted material for non-commercial purposes only, provided the original work is properly cited.