A semiautomatic postprocessing of liver R2* measurement for assessment of liver iron overload

Purpose: The purpose was to propose and evaluate a semiautomatic postprocessing method to measure liver R2* values in patients with a broad range of liver iron content. Materials and Methods: Multiecho gradient echo magnetic resonance images were acquired in patients diagnosed with thalassemia or other types of congenital anemias. Liver R2* values were measured using a routine manually defined region-of-interest (mROI) method and a semiautomatic (SA) method. In the semiautomatic method, pixelwise (pSA) and averaged (aSA) signal fitting was performed on the segmented liver tissues after hepatic vessel extraction. The pixelwise fitting approach resulted in a liver R2* map with an overlay of nonfitted pixels associated with noise performance. The following aSA approach derived overall R2* by fitting the averaged signal intensities of all pixels within the liver ROI excluding vessels and nonfitted pixels. The measurement accuracy and interobserver agreement using mROI and the two semiautomatic approaches (pSA and aSA) were evaluated. Results: In a total of 45 exams with R2* ranging from 30 to 1500 s(-1), the R2* measurements using all three methods were overall highly correlated and concordant with each other. R2* values measured by aSA were consistently higher than those measured by mROI. At lower R2* (<1000 s(-1)), R2* values measured by pSA were consistent with aSA but higher than mROI; with increasing R2*, the pSA method became less stable and underestimated R2* due to increased noise level. The interobserver agreement was higher for the aSA method compared to pSA and mROI. Conclusion: The semiautomatic postprocessing method provides a promising tool for reliable liver R2* measurement with additional information for overall evaluation of iron distribution and measurement confidence. This method may offer the potential of reducing interoperator variability and improving diagnostic confidence in patients with liver iron overload. (C) 2012 Elsevier Inc. All rights reserved.