MRgFUS (Magnetic Resonance guided Focused UltraSound) is a new and non-invasive technique to treat different diseases in the oncology field, that uses Focused Ultrasound (FUS) to induce necrosis in the lesion. Temperature change measurements during ultrasound thermo-therapies can be performed through magnetic resonance monitoring by using Proton Resonance Frequency (PRF) thermometry. It measures the phase variation resulting from the temperature-dependent changes in resonance frequency by subtracting one phase baseline image from actual phase. Referenceless thermometry aims to re-duce artefacts caused by tissue motion and frequency drift, fitting the back-ground phase outside the heated region. The aim of this contribution is to pro-pose a novel background phase reconstruction method using Radial Basis Func-tion (RBF) interpolation. The effectiveness of the method has been demonstrat-ed by comparing it against the classical PRF shift and polynomial referenceless approach. The comparison evaluates temperature rises in uterine fibroids during MRgFUS treatments on a set of 10 patients.

Agnello, L., Militello, C., Gagliardo, C., Vitabile, S. (2015). Radial Basis Function Interpolation for Referenceless Thermometry Enhancement. In S. Bassis (a cura di), Advances in Neural Networks: Computational and Theoretical Issues. Berlin : Springer International Publishing [10.1007/978-3-319-18164-6_19].

Radial Basis Function Interpolation for Referenceless Thermometry Enhancement

AGNELLO, Luca;MILITELLO, Carmelo;GAGLIARDO, Cesare;VITABILE, Salvatore
2015-01-01

Abstract

MRgFUS (Magnetic Resonance guided Focused UltraSound) is a new and non-invasive technique to treat different diseases in the oncology field, that uses Focused Ultrasound (FUS) to induce necrosis in the lesion. Temperature change measurements during ultrasound thermo-therapies can be performed through magnetic resonance monitoring by using Proton Resonance Frequency (PRF) thermometry. It measures the phase variation resulting from the temperature-dependent changes in resonance frequency by subtracting one phase baseline image from actual phase. Referenceless thermometry aims to re-duce artefacts caused by tissue motion and frequency drift, fitting the back-ground phase outside the heated region. The aim of this contribution is to pro-pose a novel background phase reconstruction method using Radial Basis Func-tion (RBF) interpolation. The effectiveness of the method has been demonstrat-ed by comparing it against the classical PRF shift and polynomial referenceless approach. The comparison evaluates temperature rises in uterine fibroids during MRgFUS treatments on a set of 10 patients.
Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazioni
http://link.springer.com/chapter/10.1007%2F978-3-319-18164-6_19
Agnello, L., Militello, C., Gagliardo, C., Vitabile, S. (2015). Radial Basis Function Interpolation for Referenceless Thermometry Enhancement. In S. Bassis (a cura di), Advances in Neural Networks: Computational and Theoretical Issues. Berlin : Springer International Publishing [10.1007/978-3-319-18164-6_19].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/102290
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