BACKGROUND: Conventional magnetic resonance imaging (MRI) has relatively long scan times for routine examinations, and the signal intensity of the images is related to the specific MR scanner settings. Due to scanner imperfections and automatic optimizations, it is impossible to compare images in terms of absolute image intensity. Synthetic MRI, a method to generate conventional images based on MR quantification, potentially both decreases examination time and enables quantitative measurements. PURPOSE: To evaluate synthetic MRI of the brain in a clinical setting by assessment of the contrast, the contrast-to-noise ratio (CNR), and the diagnostic quality compared with conventional MR images. MATERIAL AND METHODS: Twenty-two patients had synthetic imaging added to their clinical MR examination. In each patient, 12 regions of interest were placed in the brain images to measure contrast and CNR. Furthermore, general image quality, probable diagnosis, and lesion conspicuity were investigated. RESULTS: Synthetic T1-weighted turbo spin echo and T2-weighted turbo spin echo images had higher contrast but also a higher level of noise, resulting in a similar CNR compared with conventional images. Synthetic T2-weighted FLAIR images had lower contrast and a higher level of noise, which led to a lower CNR. Synthetic images were generally assessed to be of inferior image quality, but agreed with the clinical diagnosis to the same extent as the conventional images. Lesion conspicuity was higher in the synthetic T1-weighted images, which also had a better agreement with the clinical diagnoses than the conventional T1-weighted images. CONCLUSION: Synthetic MR can potentially shorten the MR examination time. Even though the image quality is perceived to be inferior, synthetic images agreed with the clinical diagnosis to the same extent as the conventional images in this study.
BACKGROUND: Conventional magnetic resonance imaging (MRI) has relatively long scan times for routine examinations, and the signal intensity of the images is related to the specific MR scanner settings. Due to scanner imperfections and automatic optimizations, it is impossible to compare images in terms of absolute image intensity. Synthetic MRI, a method to generate conventional images based on MR quantification, potentially both decreases examination time and enables quantitative measurements. PURPOSE: To evaluate synthetic MRI of the brain in a clinical setting by assessment of the contrast, the contrast-to-noise ratio (CNR), and the diagnostic quality compared with conventional MR images. MATERIAL AND METHODS: Twenty-two patients had synthetic imaging added to their clinical MR examination. In each patient, 12 regions of interest were placed in the brain images to measure contrast and CNR. Furthermore, general image quality, probable diagnosis, and lesion conspicuity were investigated. RESULTS: Synthetic T1-weighted turbo spin echo and T2-weighted turbo spin echo images had higher contrast but also a higher level of noise, resulting in a similar CNR compared with conventional images. Synthetic T2-weighted FLAIR images had lower contrast and a higher level of noise, which led to a lower CNR. Synthetic images were generally assessed to be of inferior image quality, but agreed with the clinical diagnosis to the same extent as the conventional images. Lesion conspicuity was higher in the synthetic T1-weighted images, which also had a better agreement with the clinical diagnoses than the conventional T1-weighted images. CONCLUSION: Synthetic MR can potentially shorten the MR examination time. Even though the image quality is perceived to be inferior, synthetic images agreed with the clinical diagnosis to the same extent as the conventional images in this study.
Authors: L N Tanenbaum; A J Tsiouris; A N Johnson; T P Naidich; M C DeLano; E R Melhem; P Quarterman; S X Parameswaran; A Shankaranarayanan; M Goyen; A S Field Journal: AJNR Am J Neuroradiol Date: 2017-04-27 Impact factor: 3.825
Authors: Hollie West; James L Leach; Blaise V Jones; Marguerite Care; Rupa Radhakrishnan; Arnold C Merrow; Enrique Alvarado; Suraj D Serai Journal: Neuroradiology Date: 2016-11-26 Impact factor: 2.804
Authors: T Granberg; M Uppman; F Hashim; C Cananau; L E Nordin; S Shams; J Berglund; Y Forslin; P Aspelin; S Fredrikson; M Kristoffersen-Wiberg Journal: AJNR Am J Neuroradiol Date: 2016-01-21 Impact factor: 3.825