Repeatability and Image Quality of IDEAL-IQ in Human Lumbar Vertebrae for Fat and Iron Quantification across Acquisition Parameters
Ben Shan, Haiyan Ding, Qianzao Lin, Xiaohua Zuo, Lili Lin, Dongyang Yu, Chunhong Hu
Source abstract
Echo asymmetry and least square estimation-IQ (IDEAL-IQ) were used to quantify fat and iron to verify the effects of collection parameters on repeatability and image quality of water and fat in human vertebral body. Six IDEAL-IQ sequences were used to scan 48 healthy adult women. Reproducibility of fat and iron quantification and image quality were assessed for six IDEAL-IQ sequences. The results showed that the correlation index (0.987, 0.721) of FF and R2 ∗ between scans of sequence 2 was higher than that of other sequences, and the consistency of fat quantification was better than that of iron (0.860 vs. 0.579) ( P < 0.001 ). Sequence 2 had the highest image quality score (4.9) and the lowest CV score (9.2%). In the FF figure, SNR (18.8) and CNR ( 17.8 ± 6.4 ) were the highest, while CV was the lowest (36.7%, 36.1%). In the R2 ∗ figure, sequence 3 had the highest SNR (21.8) and CNR (20.5), but its CV (51.8% and 56.1%) was significantly higher than that of sequence 2. The occurrence of fat-water exchange (FWS) was lowest in sequence 2 and sequence 4 (0, N = 96 ). In conclusion, the fat quantification of IDEAL-IQ was robust to the changes of collection parameters, and section thickness (ST) had a certain effect on maintaining good repeatability of R2 ∗ quantification. The higher the ST was, the better the image quality of FF and R2 ∗ was maintained and stable and the less the occurrence of FWS.
Evidence graph
No public relationships recorded yet.
Integrity note: This page is a factual metadata record created by deterministic ingestion. It is not a claim that the work moves a mathematical frontier or has been independently verified.