The resolution of Inverse Problems, especially those resulting in the Medical Diagnostics, is usually difficult because of the inherent noise and inaccuracies present in the data used for the reconstruction. A typical example is given by the Electrical Impedance Imaging, used for the long-term monitoring of “anomalies” present in patients’ bodies. The adoption of soft computing schemes, thanks to their intrinsic capability of dealing with data affected by inaccuracies, reveals effective in this field. As an example, the use of Artificial Neural Networks is proposed here to reconstruct the evolution of a liver tumor treated with thermal ablation.

Soft Computing Approaches for the Resolution of Electromagnetic Inverse Problems

FORMISANO, Alessandro;MARTONE, Raffaele
2007

Abstract

The resolution of Inverse Problems, especially those resulting in the Medical Diagnostics, is usually difficult because of the inherent noise and inaccuracies present in the data used for the reconstruction. A typical example is given by the Electrical Impedance Imaging, used for the long-term monitoring of “anomalies” present in patients’ bodies. The adoption of soft computing schemes, thanks to their intrinsic capability of dealing with data affected by inaccuracies, reveals effective in this field. As an example, the use of Artificial Neural Networks is proposed here to reconstruct the evolution of a liver tumor treated with thermal ablation.
A., Formisano; Vincenzo, Cutrupi; Fabrizio, Ferraioli; Formisano, Alessandro; Martone, Raffaele
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11591/173455
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