Improved T2* assessment in liver iron overload by magnetic resonance imaging
| Autor: | Brunella Favilli, Massimo Lombardi, Eliana Cracolici, Benedetta Salani, Anna Ramazzotti, Paolo Cianciulli, Alessia Pepe, Luigi Landini, Vincenzo Positano, Daniele De Marchi, Massimo Midiri, Maria Filomena Santarelli |
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| Přispěvatelé: | Positano, V, Salani, B, Pepe, A, Santarelli, MF, De Marchi, D, Ramazzotti, A, Favilli, B, Cracolici, E, Midiri, M, Cianciulli, P, Lombardi, M, Landini, L, Palmieri, F, DI SALVO, Giovanni, Perrotta, Silverio, Ragozzino, A. |
| Rok vydání: | 2008 |
| Předmět: |
Adult
Male Iron Overload Biomedical Engineering Biophysics Image processing Signal Software Region of interest Image Processing Computer-Assisted Medicine Liver iron Humans Radiology Nuclear Medicine and imaging liver iron overload Observer Variation Reproducibility medicine.diagnostic_test business.industry beta-Thalassemia Reproducibility of Results Pattern recognition Magnetic resonance imaging Magnetic Resonance Imaging Liver Data Interpretation Statistical Automatic segmentation Female Artificial intelligence business Nuclear medicine Algorithms |
| Zdroj: | Magnetic resonance imaging. 27(2) |
| ISSN: | 0730-725X |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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