Objective: To compare the two-point Dixon T 2 weighted imaging (T 2WI) with conventional fat-sat T 2WI in fat suppression (FS) quality and staging performance for patients with TAO. Methods: We enrolled 37 thyroid-associated ophthalmopathy (TAO) patients and 15 healthy controls who underwent both coronal two-point Dixon and fat-sat T 2WI. Qualitative (overall imaging quality, FS uniformity) and quantitative [signal intensity ratio of extraocular muscle (EOM-SIR)] parameters were assessed between the two-point Dixon T 2WI and fat-sat T 2WI. Additionally, water fraction of intraorbital fat (IF-WF) was measured on Dixon image. Dixon-EOM-SIR, Fat-sat-EOM-SIR and Dixon-IF-WF values were compared between active and inactive TAO groups, and the diagnostic efficiency for the active phase were evaluated. Results: Two-point Dixon T 2WI showed significantly higher overall image quality score, FS uniformity score as well as EOM-SIR value than fat-sat T 2WI in both TAO and control groups (all p < 0.05). Active TAOs had significantly higher Dixon-EOM-SIR (p < 0.001), Fat-sat-EOM-SIR (p < 0.001) and Dixon-IF-WF (p = 0.001) than inactive TAOs. ROC curves analyses indicated that Dixon-EOM-SIR ≥3.32 alone demonstrated the highest staging sensitivity (75.0%). When integrating Dixon-EOM-SIR ≥3.32 and Dixon-IF-WF ≥0.09, improved staging efficiency and specificity could be achieved (area under the curve, 0.872; specificity, 97.1%). Conclusion: Compared with conventional fat-sat technique, two-point Dixon T 2WI offers better image quality, as well as improved staging sensitivity and specificity for TAO. Dixon T 2WI is suggested to be used to evaluate the patients with TAO in clinical practice. Advances in knowledge: Two-point Dixon T 2WI offers better image quality than fat-sat T 2WI. Dixon-EOM-SIR alone demonstrated the highest staging sensitivity. Combining with Dixon-IF-WF showed improved staging efficiency and specificity. Dixon T 2WI is suggested to be used to evaluate TAO patients in clinical practice.
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Chen et al. (2020) studied this question.
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