Quantitative Susceptibility Mapping (QSM) Minimizes Interference From Cellular Pathology in R2☆ Estimation of Liver Iron Concentration

Background: A challenge for R2 and R2(star) methods in measuring liver iron concentration (LIC) is that fibrosis, fat, and other hepatic cellular pathology contribute to R2 and R2(star) and interfere with LIC estimation. Purpose: To examine the interfering effects of fibrosis, fat, and other lesions on R2(star) LIC estimation and to use quantitative susceptibility mapping (QSM) to reduce these distortions. Study Type: Prospective. Phantoms, Subjects: Water phantoms with various concentrations of gadolinium (Gd), collagen (Cl, modeling fibrosis), and fat; nine healthy controls with no known hepatic disease, nine patients with known or suspected hepatic iron overload, and nine patients with focal liver lesions. Field Strength/Sequence: The phantoms and human subjects were imaged using a 3D multiecho gradient-echo on clinical 1.5T and 3T MRI systems. Assessment: QSM and R2(star) images were postprocessed from the same gradient-echo data. Fat contributions to susceptibility and R2(star) were corrected in signal models for LIC estimation. Statistical Tests: Polynomial regression analyses were performed to examine relations among susceptibility, R2(star) and true [Gd] and [Cl] in phantoms, and among susceptibility and R2(star) in patient livers. Results: In phantoms, R2(star) had a strong nonlinear dependency on [Cl], [fat], and [Gd], while susceptibility was linearly dependent (R-2>0.98). In patients, R2(star) was highly sensitive to liver pathological changes, including fat, fibrosis, and tumors, while QSM was relatively insensitive to these abnormalities (P=0.015). With moderate iron overload, liver susceptibility and R2(star) were not linearly correlated over a common R2(star) range [0, 100] sec(-1) (P = 0.35). Data Conclusion: R2(star) estimation of LIC is prone to substantial nonlinear interference from fat, fibrosis, and other lesions. QSM processing of the same gradient echo MRI data can effectively minimize the effects of cellular pathology.