Related Experiment Video For T2* mapping
Updated: Apr 3, 2026

Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis
Published on: February 9, 2024
T2* mapping at 3.0T MRI for differentiation of papillary thyroid carcinoma from benign thyroid nodules
Ruoyang Shi1, Qiuying Yao1, Lianming Wu1
1Department of Radiology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Purpose:
To study the quantitative T2* mapping for thyroid nodules and to explore the use of T2* values to differentiate papillary thyroid carcinoma (PTC) from benign thyroid nodules, with histopathological examination as a reference standard.
Materials And Methods:
Twenty-eight consecutive patients with thyroid nodules were subjected to a 3.0T magnetic resonance imaging (MRI) examination. T2 * mapping was acquired using six echo times with a multiecho fast field echo (mFFE) sequence and constructed by exponentially fitting the multiecho T2* images pixel-by-pixel. The quality of the native T2* image was evaluated. An independent sample t-test was used to evaluate the statistical difference of the mean T2* value and the mean ratio of lesion to contralateral normal tissue between PTC and benign thyroid nodules. A receiver operating characteristic (ROC) curve was used to calculate the sensitivity and specificity.
Results:
The T2* value (mean: 21.73 ± 2.09 msec) and the ratio (mean: 1.61 ± 0.11) of PTC group were both significantly lower (P < 0.001) than those of the benign group (mean T2* value: 28.78 ± 5.02 msec, mean ratio: 2.18 ± 0.43). Applying a threshold value of 25.00 msec for T2* values and 1.795 for the ratio of lesion regions to normal tissue regions to identify PTC yielded a sensitivity of 84.2% and 89.5%, respectively, and a specificity of 100% for both.
Conclusion:
T2* mapping can potentially provide quantitative information to separate PTC from benign thyroid nodules.
More Related Videos
Related Concept Videos
Magnetic Resonance Imaging
Imaging Studies III: Computed Tomography

