Non-invasive estimation of liver iron concentration (LIC) by R2-MRI is often used for detection, grading and treatment monitoring in patients with suspected or known iron overload. The only current R2-MRI LIC estimation method with regulatory clearance is FerriScan®, a proprietary analysis for biexponential R2-relaxometry. We implemented a nonproprietary biexponential R2-relaxometry using a "dictionary-search" algorithm, to reproduce the FerriScan® results. In 38 patients with known or suspected iron overload, we demonstrated excellent reproducibility (by linearity and absolute agreement) in R2 and LIC between FerriScan® and dictionary-search analyses, suggesting generalizability of the R2-MRI approach for LIC estimation.
This single-center retrospective study, approved by Investigational Review Board and compliant with Health Insurance Portability and Accountability Act, included 40 MRI exams in 38 patients with a clinical indication for MRI of the liver to evaluate for known or suspected iron overload. Subjects underwent a standardized FerriScan® protocol (Table 1). Spin-echo (SE) data were securely transmitted to Resonance Health for off-site post-processing and analysis via a proprietary method that utilizes a simulated annealing curve-fitting algorithm [11]. Using the proprietary analysis report, the average R2 of the single segmented liver section was extracted. The corresponding estimated LIC (in mg Fe/g dry liver tissue) was calculated using the conversion formula developed for the proprietary method [9].
SE data were also post-processed and analyzed on-site using the dictionary-search curve-fitting algorithms written in MATLABTM (The MathWorks, Inc., Natick, MA). The liver was manually segmented on the same axial slice approximating the same region of interest (ROI) as used in the proprietary analysis. The SE data were corrected for scanner gain drift, background noise offset, and respiratory motion artifacts as previously described [12-14]. Biexponential R2-relaxometry was performed via a recently-introduced dictionary-search algorithm (Figure 1) by fitting the post-processed, noise-corrected signal intensity data, S(t), measured at different echo time (t), to a signal model with slow (R2s) and fast (R2f) relaxation components [12],
S(t) ∝ pe-R2st + (1 - p)e-R2ft ,
where p is the slow-component fraction. For each pixel, the weighted average of the slow and fast R2 components were calculated as: R2 = p × R2s + (1 – p) × R2f. Average liver R2 was calculated by averaging the pixel R2 values of the ROI, as done in the proprietary method. Each average liver R2 was converted to LIC using the validated formula developed for the proprietary method [9]. Inter-method reproducibility for liver R2 and LIC were assessed for linearity (using linear regression analysis) and absolute agreement (using intraclass correlation [ICC] and Bland-Altman analysis). For point estimates, 95% confidence intervals (CI) were calculated; P-values < 0.05 were considered statistically significant.
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