Exhibit Hall | Forum 9
Purpose: To evaluate the ability of several fitting methods to reduce bias and uncertainty in T₂* mapping of iron-agar phantoms, particularly for low T₂* values and low-SNR environments.
Methods: Phantoms composed of 2% agar and ferumoxytol (range: 0–60 μg Fe/mL) were imaged with a 3T MR scanner (Siemens MAGNETOM Prisma-Fit, Erlangen, Germany). Multi-gradient-echo (mGRE) sequences were acquired (TE = 2.4–37.4 ms, 8 echoes) along the short axis of the 50-mL phantom tubes. Image SNR was modulated by adjusting the number of signal averages (NEX = 1–16). T₂* maps were reconstructed off-line using a custom MATLAB script (R2021a, Mathworks, Natick, MA) that fitted the multi-gradient-echo data to 2- and 3-parameter exponential and logarithmic models. 2-parameter exponential fitting using only the first 3–7 echoes (truncated fitting) was also performed and compared with fitting using all 8 echoes. For each noise level, the bias (change in mean T₂* value from the 16-NEX acquisition) and uncertainty (standard deviation in T₂*) were computed within each phantom for three independent scans.
Results: 2-parameter fitting produced a positive T₂* bias that worsened with decreasing SNR, while 3-parameter fits remained relatively stable over the SNR range tested. The 3-parameter exponential model also exhibited the smallest uncertainty for SNR levels of ~8 or less. Logarithmic fits consistently produced greater bias and uncertainty than their exponential counterparts at low SNR levels (SNR < ~10). Truncated fits increased bias and noise for low-iron concentrations with high-SNR, but this effect was reversed for high iron concentrations and low SNR (SNR < ~8), with fewer fitted echoes reducing bias and, to an extent, uncertainty.
Conclusion: For low-SNR regimes, using a 3-parameter model or excluding longer TEs from fitting reduces both bias and uncertainty in T₂* measurements. This may improve T₂* estimates for high-susceptibility, low-SNR anatomy, such as in the heart.
Funding Support, Disclosures, and Conflict of Interest: This work was supported in part by NHLBI HL133407, HL136578, and HL147133.
MRI, Image Analysis, Quantitative Imaging