Quantification of traumatic meningeal injury using dynamic contrast enhanced (DCE) fluid-attenuated inversion recovery (FLAIR) imaging

2016 ◽  
Author(s):  
Marcelo A. Castro ◽  
Joshua P. Williford ◽  
Martin R. Cota ◽  
Judy M. MacLaren ◽  
Bernard J. Dardzinski ◽  
...  
Keyword(s):  
Inversion Recovery ◽  
Flair Imaging ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced ◽  
Fluid Attenuated Inversion Recovery

Contrast-enhanced Fast Fluid-attenuated Inversion Recovery MR Imaging in Patients with Brain Tumors

2000 ◽  
Vol 43 (3) ◽  
pp. 257
Author(s):  
Chan Kyo Kim ◽  
Dong Gyu Na ◽  
Wook Jae Ryoo ◽  
Hong Sik Byun ◽  
Hye Kyung Yoon ◽  
...  
Keyword(s):  
Brain Tumors ◽  
Mr Imaging ◽  
Inversion Recovery ◽  
Contrast Enhanced ◽  
Fluid Attenuated Inversion Recovery

Optimization of Contrast Agent Dosage on Contrast-Enhanced T2 Fluid-Attenuated Inversion Recovery

2020 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Wenting Rui ◽  
Teng Jin ◽  
Hua Zhang ◽  
Jing Wang ◽  
Yan Ren ◽  
...  
Keyword(s):  
Contrast Agent ◽  
Inversion Recovery ◽  
Contrast Enhanced ◽  
Fluid Attenuated Inversion Recovery

Signal intensity pattern of the normal oculomotor nerve on contrast-enhanced 3D FLAIR at 3.0 T MRI

2020 ◽  
pp. 197140092097091
Author(s):  
Thiparom Sananmuang ◽  
Chanonporn Boonsiriwattanakul ◽  
Theeraphol Panyaping
Keyword(s):  
White Matter ◽  
Signal Intensity ◽  
Intensity Ratio ◽  
Grey Matter ◽  
Three Dimensional ◽  
Oculomotor Nerve ◽  
Inversion Recovery ◽  
Signal Intensity Ratio ◽  
Contrast Enhanced ◽  
Fluid Attenuated Inversion Recovery

Purpose The aim of this study was to depict the signal intensity pattern of the normal oculomotor nerve demonstrated on contrast-enhanced three-dimensional fluid-attenuated inversion recovery images. Materials and methods Eighty-one patients were included in the study. Contrast-enhanced three-dimensional fluid-attenuated inversion recovery images with magnetisation-prepared rapid acquisition were reconstructed and evaluated in the coronal plane. The signal intensity of the cisternal segment of the oculomotor nerve was graded into a visual scale of 1 to 5 as compared to the white matter, grey matter and the pituitary stalk. The signal intensity ratio of the oculomotor nerve was consequently measured. Results By using the visual scale, more than half of the oculomotor nerves showed higher signal intensity than the grey matter signal on contrast-enhanced three-dimensional fluid-attenuated inversion recovery images (59.3–80.2%). It can demonstrate a signal intensity similar to the pituitary stalk (14.8%) by visualisation. None of them showed signal intensity equal to the normal white matter signal. By signal intensity measurement, the mean signal intensity ratio of oculomotor nerves to white matter equals 1.54±0.20 (95% confidence interval (CI) 1.51–1.57); mean signal intensity ratio to grey matter equals 1.16±0.15 (95% CI 1.14–1.18); mean signal intensity ratio to the pituitary stalk equals 0.68±0.10 (95% CI 0.64–0.70). Conclusions The normal oculomotor nerve visualised on contrast-enhanced three-dimensional fluid-attenuated inversion recovery images has a higher signal intensity than the white matter and may have a signal intensity similar to the grey matter or the pituitary stalk. The high signal intensity of the oculomotor nerve in contrast-enhanced three-dimensional fluid-attenuated inversion recovery should not be misinterpreted as a pathology.

scholarly journals Better  efficacy in differentiating WHO grade II from III oligodendrogliomas with machine-learning than radiologist’s reading from conventional T1 contrast-enhanced and fluid attenuated inversion recovery images

2020 ◽  
Author(s):  
Sha-Sha Zhao ◽  
Xiu-Long Feng ◽  
Yu-Chuan Hu ◽  
Yu Han ◽  
Qiang Tian ◽  
...  
Keyword(s):  
Machine Learning ◽  
Inversion Recovery ◽  
World Health ◽  
Diagnostic Efficacy ◽  
Who Grade ◽  
Superior Efficacy ◽  
Contrast Enhanced ◽  
Fluid Attenuated Inversion Recovery ◽  
T1 Contrast ◽  
Grade Ii

Abstract Background: The medical imaging to differentiate World Health Organization (WHO) grade II (ODG2) from III (ODG3) oligodendrogliomas still remains a challenge. We investigated whether combination of machine leaning with radiomics from conventional T1 contrast-enhanced (T1CE) and fluid attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) offered superior efficacy. Methods: Thirty-six patients with histologically confirmed ODGs underwent T1CE and 33 of them underwent FLAIR MR examination before any intervention from January 2015 to July 2017 were retrospectively recruited in the current study. The volume of interest (VOI) covering the whole tumor enhancement were manually drawn on the T1CE and FLAIR slice by slice using ITK-SNAP and a total of 1072 features were extracted from the VOI using 3-D slicer software. Random forest (RF) algorithm was applied to differentiate ODG2 from ODG3 and the efficacy was tested with 5-fold cross validation. The diagnostic efficacy of radiomics-based machine learning and radiologist’s assessment were also compared. Results: Nineteen ODG2 and 17 ODG3 were included in this study and ODG3 tended to present with prominent necrosis and nodular/ring-like enhancement (P < 0.05). The AUC, ACC, sensitivity, and specificity of radiomics were 0.798, 0.735, 0.672, 0.789 for T1CE, 0.774, 0.689, 0.700, 0.683 for FLAIR, as well as 0.861, 0.781, 0.778, 0.783 for the combination, respectively. The AUCs of radiologists 1, 2 and 3 were 0.700, 0.687, and 0.714, respectively. The efficacy of machine learning based on radiomics was superior to the radiologists' assessment. Conclusions: Machine-learning based on radiomics of T1CE and FLAIR offered superior efficacy to that of radiologists in differentiating ODG2 from ODG3.

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