Purpose To validate an automatic algorithm for offline T2∗ measurements, providing robust, vendor-independent T2∗, and uncertainty estimates for iron load quantification in the heart and liver using clinically available imaging sequences. Methods A T2∗ region of interest (ROI)-based algorithm was developed for robustness in an offline setting. Phantom imaging was performed on a 1.5 Tesla system, with clinically available multiecho gradient-recalled-echo (GRE) sequences for cardiac and liver imaging. A T2∗ single-echo GRE sequence was used as reference. Simulations were performed to assess accuracy and precision from 2000 measurements. Inter- and intraobserver variability was obtained in a patient study (n = 23). Results Simulations: Accuracy, in terms of the mean differences between the proposed method and true T2∗ ranged from 0-0.73 ms. Precision, in terms of confidence intervals of repeated measurements, was 0.06-4.74 ms showing agreement between the proposed uncertainty estimate and simulations. Phantom study: Bias and variability were 0.26 ± 4.23 ms (cardiac sequence) and -0.23 ± 1.69 ms (liver sequence). Patient study: Intraobserver variability was similar for experienced and inexperienced observers (0.03 ± 1.44 ms versus 0.16 ± 2.33 ms). Interobserver variability was 1.0 ± 3.77 ms for the heart and -0.52 ± 2.75 ms for the liver. Conclusion The proposed algorithm was shown to provide robust T2∗ measurements and uncertainty estimates over the range of clinically relevant T2∗ values.
- MRI relaxometry
- offline image processing
- uncertainty estimation
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging