Improved T2* assessment in liver iron overload by magnetic resonance imaging.

In the clinical MRI practice, it is common to assess liver iron overload by T2* multi-echo gradient-echo images. However, there is no full consensus about the best image analysis approach for the T2* measurements. The currently used methods involve manual drawing of a region of interest (ROI) within MR images of the liver. Evaluation of a representative liver T2* value is done by fitting an appropriate model to the signal decay within the ROIs vs. the echo time. The resulting T2* value may depend on both ROI placement and choice of the signal decay model. The aim of this study was to understand how the choice of the analysis methodology may affect the accuracy of T2* measurements. A software model of the iron overloaded liver was inferred from MR images acquired from 40 thalassemia major patients. Different image analysis methods were compared exploiting the developed software model. Moreover, a method for global semiautomatic T2* measurement involving the whole liver was developed. The global method included automatic segmentation of parenchyma by an adaptive fuzzy-clustering algorithm able to compensate for signal inhomogeneities. Global liver T2* value was evaluated using a pixel-wise technique and an optimized signal decay model. The global approach was compared with the ROI-based approach used in the clinical practice. For the ROI-based approach, the intra-observer and inter-observer coefficients of variation (CoVs) were 3.7% and 5.6%, respectively. For the global analysis, the CoVs for intra-observers and inter-observers reproducibility were 0.85% and 2.87%, respectively. The variability shown by the ROI-based approach was acceptable for use in the clinical practice; however, the developed global method increased the accuracy in T2* assessment and significantly reduced the operator dependence and sampling errors. This global approach could be useful in the clinical arena for patients with borderline liver iron overload and/or requiring follow-up studies.

A robust method for assessment of iron overload in liver by magnetic resonance imaging.

Assessment of iron overload in liver by T2* magnetic resonance imaging (MRI) is a widely used clinical procedure. In the common clinical practice, measurement is performed locally by manually drawing a small region of interest in liver. This procedure may be affected by a noticeable intra- and inter-observer variability. In this study, a new approach is proposed that performs a global semiautomatic measurement of T2* involving the whole liver extension. Parenchyma is automatically segmented by an adaptive fuzzy-clustering algorithm. The liver T2* global value is evaluated using a pixel-wise approach by introducing an appropriate signal decay model. The proposed method was tested on a synthetic software model and on MR images acquired from 30 thalassemia major patients. The methods was demonstrated to increase the measure precision in T2* assessment and to significantly reduce the intra- and inter-observer variability.