Parametric sensitivity analysis of radiofrequency ablation with efficient experimental design

Ng, E. Y. K.; Jamil, Muhammad

doi:10.1016/j.ijthermalsci.2014.01.024

Cited by 30 publications

(16 citation statements)

References 43 publications

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“…From the view of avoiding overfit, a smaller s may prevent overfitting in greater degree than a larger s. This study used grid-search [49] method to find the optimal value of s, i.e., we vary the value of s from 1 to 5 with increment of 1, and check the corresponding average accuracies. The one associated with the largest accuracy is the optimal value of s.…”

Section: Parameter Estimation For Smentioning

confidence: 99%

Dual-Tree Complex Wavelet Transform and Twin Support Vector Machine for Pathological Brain Detection

et al. 2016

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(Aim) Classification of brain images as pathological or healthy case is a key pre-clinical step for potential patients. Manual classification is irreproducible and unreliable. In this study, we aim to develop an automatic classification system of brain images in magnetic resonance imaging (MRI). (Method) Three datasets were downloaded from the Internet. Those images are of T2-weighted along axial plane with size of 256ˆ256. We utilized an s-level decomposition on the basis of dual-tree complex wavelet transform (DTCWT), in order to obtain 12s "variance and entropy (VE)" features from each subband. Afterwards, we used support vector machine (SVM) and its two variants: the generalized eigenvalue proximal SVM (GEPSVM) and the twin SVM (TSVM), as the classifiers. In all, we proposed three novel approaches: DTCWT + VE + SVM, DTCWT + VE + GEPSVM, and DTCWT + VE + TSVM. (Results) The results showed that our "DTCWT + VE + TSVM" obtained an average accuracy of 99.57%, which was not only better than the two other proposed methods, but also superior to 12 state-of-the-art approaches. In addition, parameter estimation showed the classification accuracy achieved the largest when the decomposition level s was assigned with a value of 1. Further, we used 100 slices from real subjects, and we found our proposed method was superior to human reports from neuroradiologists. (Conclusions) This proposed system is effective and feasible.

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Section: Parameter Estimation For Smentioning

confidence: 99%

Dual-Tree Complex Wavelet Transform and Twin Support Vector Machine for Pathological Brain Detection

et al. 2016

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“…FEM modelling was used to analyses radiofrequency cancer ablation [17] by solving RF Joule (resistive) heating and Penne's bio-heat equation [18]. FEM modelling was further used to study the influence of blood vessel on the thermal lesion formation (the heat sink effects) during RFA [19] and the influence of other parameters such as water evaporation [20] and tissue conductivities with efficient RFA experiment design [21,22]. In this study, an anatomical 3D FEM model was used for RFA simulation.…”

Section: B 3d Fem Modellingmentioning

confidence: 99%

“…All the properties can be found from literature [23]. The ablation zone was estimated using an iso-thermal plot for a tissue temperature of 50°C and above [15,21] since heating of tissue at 50°C and above will produce irreversible cellular damage and result in ablation [24,25]. FEM simulation was also used for the design of the RF electrode probe, its optimisation and validation.…”

Section: B 3d Fem Modellingmentioning

confidence: 99%

Bi-component conformal electrode for radiofrequency sequential ablation and circumferential separation of large tumours in solid organs: development and in-vitro evaluation

Wang

Luo

Coleman

et al. 2016

IEEE Trans. Biomed. Eng.

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“…During medical procedures based on radiofrequency ablation caused by the electric field, a current with frequencies in the range of tens to several hundred kHz is induced. In such a frequency range, the electromagnetic wave length is much larger than the depth of the human body; therefore, the current flow is via electrical conductivity and can be analyzed as a so-called quasi-static formulation, which allows coupling the electrical and thermal conductivity problem [8,[17][18][19][20][21][22]. To determine the internal heat source resulting from the influence of the electric field, it is necessary to know the intensity of the electric field, which is dependent on the electric conductivity coefficient.…”

Section: Introductionmentioning

confidence: 99%

“…Besides determining the temperature field in the biological tissue affected by the tumor and subjected to external thermal action, it is also necessary to estimate the degree of tissue destruction under the influence of high temperature. In the literature, e.g., [15,20,23,24], we can find information that the temperature above 45 degrees of Celsius is the so-called ablation temperature, i.e., causing irreversible destruction of biological tissue (the duration of the heating process is also important). It is obvious that tissue destruction is reflected in the values of its parameters, which may assume nonlinear values depending on the processes involved.…”

Section: Introductionmentioning

confidence: 99%

Mathematical Modeling of Breast Tumor Destruction Using Fast Heating during Radiofrequency Ablation

Paruch

2019

Materials

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In oncology, hyperthermia is understood as a planned, controlled technique of heating cancerous changes in order to destroy their cells or stop their growth. In clinical practice, hyperthermia is used in combination with radiotherapy, chemotherapy, or immunological therapy. During the hyperthermia, the tissue is typically exposed to a temperature in the range of 40-45 • C, the exception is thermoablation, during which the temperatures reach much higher values. Thermoablation is characterized by the use of high temperatures up to 90 • C. The electrode using the radiofrequency is inserted into the central area of the tumor. Interstitial thermoablation is used to treat, among others, breast and brain cancer. The therapy consists of inducing coagulation necrosis in an area that is heated to very high temperatures. Mathematical modeling is based on the use of a coupled thermo-electric model, in which the electric field is described by means of the Laplace equation, while the temperature field is based on the Pennes equation. Coupling occurs at the level of the additional source function in the Pennes equation. The temperature field obtained in this way makes it possible to calculate the Arrhenius integral as a determinant of the destruction of biological tissue. As a result of numerical calculations regarding the temperature field and the Arrhenius integral, it can be concluded that, with the help of numerical tools and mathematical modeling, one can simulate the process of destroying cancerous tissue.

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Parametric sensitivity analysis of radiofrequency ablation with efficient experimental design

Cited by 30 publications

References 43 publications

Dual-Tree Complex Wavelet Transform and Twin Support Vector Machine for Pathological Brain Detection

Dual-Tree Complex Wavelet Transform and Twin Support Vector Machine for Pathological Brain Detection

Bi-component conformal electrode for radiofrequency sequential ablation and circumferential separation of large tumours in solid organs: development and in-vitro evaluation

Mathematical Modeling of Breast Tumor Destruction Using Fast Heating during Radiofrequency Ablation

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