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

Massimo Midiri, Daniele De Marchi, Anna Ramazzotti, Alessia Pepe, Benedetta Salani, Luigi Landini, Vincenzo Positano, Paolo Cianciulli, Maria Filomena Santarelli, Massimo Lombardi, Brunella Favilli, Eliana Cracolici

Risultato della ricerca: Article

84 Citazioni (Scopus)

Abstract

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.
Lingua originaleEnglish
pagine (da-a)188-197
Numero di pagine10
RivistaMagnetic Resonance Imaging Clinics of North America
Volume27
Stato di pubblicazionePublished - 2009

Fingerprint

Iron Overload
Magnetic Resonance Imaging
Liver
Software
Observer Variation
Selection Bias
beta-Thalassemia
Cluster Analysis
Iron

All Science Journal Classification (ASJC) codes

  • Biophysics
  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Cita questo

Midiri, M., De Marchi, D., Ramazzotti, A., Pepe, A., Salani, B., Landini, L., ... Cracolici, E. (2009). Improved T2* assessment in liver iron overload by magnetic resonance imaging. Magnetic Resonance Imaging Clinics of North America, 27, 188-197.

Improved T2* assessment in liver iron overload by magnetic resonance imaging. / Midiri, Massimo; De Marchi, Daniele; Ramazzotti, Anna; Pepe, Alessia; Salani, Benedetta; Landini, Luigi; Positano, Vincenzo; Cianciulli, Paolo; Santarelli, Maria Filomena; Lombardi, Massimo; Favilli, Brunella; Cracolici, Eliana.

In: Magnetic Resonance Imaging Clinics of North America, Vol. 27, 2009, pag. 188-197.

Risultato della ricerca: Article

Midiri, M, De Marchi, D, Ramazzotti, A, Pepe, A, Salani, B, Landini, L, Positano, V, Cianciulli, P, Santarelli, MF, Lombardi, M, Favilli, B & Cracolici, E 2009, 'Improved T2* assessment in liver iron overload by magnetic resonance imaging.', Magnetic Resonance Imaging Clinics of North America, vol. 27, pagg. 188-197.
Midiri, Massimo ; De Marchi, Daniele ; Ramazzotti, Anna ; Pepe, Alessia ; Salani, Benedetta ; Landini, Luigi ; Positano, Vincenzo ; Cianciulli, Paolo ; Santarelli, Maria Filomena ; Lombardi, Massimo ; Favilli, Brunella ; Cracolici, Eliana. / Improved T2* assessment in liver iron overload by magnetic resonance imaging. In: Magnetic Resonance Imaging Clinics of North America. 2009 ; Vol. 27. pagg. 188-197.
@article{2baa173bc0594e62917898d26265d37e,
title = "Improved T2* assessment in liver iron overload by magnetic resonance imaging.",
abstract = "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.",
keywords = "liver iron overload",
author = "Massimo Midiri and {De Marchi}, Daniele and Anna Ramazzotti and Alessia Pepe and Benedetta Salani and Luigi Landini and Vincenzo Positano and Paolo Cianciulli and Santarelli, {Maria Filomena} and Massimo Lombardi and Brunella Favilli and Eliana Cracolici",
year = "2009",
language = "English",
volume = "27",
pages = "188--197",
journal = "Magnetic Resonance Imaging Clinics of North America",
issn = "1064-9689",
publisher = "W.B. Saunders Ltd",

}

TY - JOUR

T1 - Improved T2* assessment in liver iron overload by magnetic resonance imaging.

AU - Midiri, Massimo

AU - De Marchi, Daniele

AU - Ramazzotti, Anna

AU - Pepe, Alessia

AU - Salani, Benedetta

AU - Landini, Luigi

AU - Positano, Vincenzo

AU - Cianciulli, Paolo

AU - Santarelli, Maria Filomena

AU - Lombardi, Massimo

AU - Favilli, Brunella

AU - Cracolici, Eliana

PY - 2009

Y1 - 2009

N2 - 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.

AB - 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.

KW - liver iron overload

UR - http://hdl.handle.net/10447/47266

M3 - Article

VL - 27

SP - 188

EP - 197

JO - Magnetic Resonance Imaging Clinics of North America

JF - Magnetic Resonance Imaging Clinics of North America

SN - 1064-9689

ER -