An inverse geometry problem for the localisation of skin tumours by thermal analysis

Partridge, P. W.; Wrobel, L.C.

doi:10.1016/j.enganabound.2007.02.002

Cited by 54 publications

(45 citation statements)

References 15 publications

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“…The above choice is justified by the results of the tests carried out by Partridge and Wrobel (2007) with several radial basis functions, including the 'classical' function r and the thin plate spline 2 r log r, with and without augmentation. Results obtained using each of these functions were found to differ little.…”

Section: And (11)mentioning

confidence: 99%

“…This paper extends the algorithm developed by Partridge and Wrobel (2007) for the identification of the coefficients of linear and quadratic variations of blood perfusion. A simple GA, as described in the literature (Castro and Partridge, 2006), is adequate for the problem.…”

Section: Introductionmentioning

confidence: 99%

“…Previous works on inverse analysis of biological bodies were carried out by Ren et al (1995), who applied the Boundary Element Method (BEM) to identify heat sources in biological bodies based on the simultaneous measurement of temperature and heat flux at the skin surface, by Majchrzak and Paruch (2004), who estimated the (constant) thermophysical parameters of a tumour using a least-squares algorithm based on sensitivity coefficients, and by Partridge and Wrobel (2007), who presented a BEM inverse analysis based on a Genetic Algorithm (GA) (Goldberg, 1989;Goldberg et al, 1997) to identify the position and size of shallow tumours using skin temperature measurements. This paper extends the algorithm developed by Partridge and Wrobel (2007) for the identification of the coefficients of linear and quadratic variations of blood perfusion.…”

Section: Introductionmentioning

confidence: 99%

“…Herein, a numerical technique for identification of the temperature-dependent blood perfusion parameters in Pennes' equation is proposed based on the Dual Reciprocity Method (DRM), which has already been used for the direct solution of the bioheat equation (Deng and Liu, 2000;Deng and Liu, 2004a;Deng and Liu, 2004b;Lu et al, 1998;Liu and Xu, 2000). It is assumed that the size and location of the tumour are known from previous diagnostics; in the DRM, the tumour is treated as a sub-region and in addition to the nodes used to model the boundary of the tumour, no other internal points are required.Previous works on inverse analysis of biological bodies were carried out by Ren et al (1995), who applied the Boundary Element Method (BEM) to identify heat sources in biological bodies based on the simultaneous measurement of temperature and heat flux at the skin surface, by Majchrzak and Paruch (2004), who estimated the (constant) thermophysical parameters of a tumour using a least-squares algorithm based on sensitivity coefficients, and by Partridge and Wrobel (2007), who presented a BEM inverse analysis based on a Genetic Algorithm (GA) (Goldberg, 1989;Goldberg et al, 1997) to identify the position and size of shallow tumours using skin temperature measurements. This paper extends the algorithm developed by Partridge and Wrobel (2007) for the identification of the coefficients of linear and quadratic variations of blood perfusion.…”

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confidence: 99%

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A coupled dual reciprocity BEM/genetic algorithm for identification of blood perfusion parameters

Partridge

Wrobel

2009

International Journal of Numerical Methods for Heat &Amp; Fluid Flow

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Purpose -The paper presents an inverse analysis procedure based on a coupled numerical formulation through which the coefficients describing non-linear thermal properties of blood perfusion may be identified. Design/methodology/approach -The coupled numerical technique involves a combination of the Dual Reciprocity Boundary Element Method and a GeneticAlgorithm for the solution of the Pennes bioheat equation. Both linear and quadratic temperature-dependent variations are considered for the blood perfusion. Findings -The proposed DRBEM formulation requires no internal discretisation and, in this case, no internal nodes either, apart from those defining the interface tissue/tumour. It is seen that the skin temperature variation changes as the blood perfusion increases, and in certain cases flat or nearly flat curves are produced. The proposed algorithm has difficulty to identify the perfusion parameters in these cases, although a more advanced GA may provide improved results.Practical implications -The coupled technique allows accurate inverse solutions of the Pennes bioheat equation for quantitative diagnostics on the physiological conditions of biological bodies and for optimisation of hyperthermia for cancer therapy.Originality/value -The proposed technique can be used to guide hyperthermia cancer treatment, which normally involves heating tissue to 42-43 o C. When heated up to this range of temperatures, the blood flow in normal tissues, e.g. skin and muscle, increases significantly, while blood flow in the tumour zone decreases. Therefore, the consideration of temperature-dependent blood perfusion in this case is not only essential for the correct modelling of the problem, but should provide larger skin temperature variations, making the identification problem easier Keywords Boundary element method, Dual reciprocity method, Genetic algorithm, Inverse analysis, Tumours, Blood perfusion Paper type Research paper IntroductionIt is well known that the body surface temperature is controlled by blood circulation, local metabolism and heat transfer between the skin and the environment (Deng and Liu, 2004a). It is also known that several types of tumours, e.g. skin or breast, can lead to an increase in local blood flow, and thus to an increase in the local temperature (Liu and Xu, 2000). On the other hand, thrombosis or vascular sclerosis decreases the blood flowing to the skin, resulting in lower skin temperatures (Liu and Xu, 2000).The Pennes bioheat equation can be used for the quantitative diagnostics of physiological conditions on biological bodies, e.g. for simulations of regional hyperthermia for cancer therapy (Tompkins et al., 1994;Erdmann et al., 1998;Lang et al., 1999). The parameters considered in Pennes' equation are usually assumed to be constant except for the blood perfusion, which varies with temperature (Tompkins et al., 1994;Erdmann et al., 1998;Lang et al., 1999;Rai and Rai, 1999;Liu and Xu, 2000). Herein, a numerical technique for identification of the temperature-dependent blood perfusi...

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Section: And (11)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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A coupled dual reciprocity BEM/genetic algorithm for identification of blood perfusion parameters

Partridge

Wrobel

2009

International Journal of Numerical Methods for Heat &Amp; Fluid Flow

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“…As a branch of the inverse problem of heat transfer, inverse geometrical problem [1] of heat conduction has a broad application prospect in industrial equipment testing, nondestructive testing [2], geometry optimization [3], biological lesions [4], and other fields. Two-dimensional steady-state space is first discretized in inverse geometrical boundary of the heat transfer system, and discretization methods mainly include finite difference method [5] (FDM) and finite element method [6] (FEM) and boundary element method [7] (BEM), which can be used for solving heat transfer problems, inverting the geometric boundary through various optimization techniques on the basis of solving the forward problem.…”

Section: Introductionmentioning

confidence: 99%

Two-Dimensional Steady-State Boundary Shape Inversion of CGM-SPSO Algorithm on Temperature Information

Wang

Jia

Sun

et al. 2017

Advances in Materials Science and Engineering

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Addressing the problem of two-dimensional steady-state thermal boundary recognition, a hybrid algorithm of conjugate gradient method and social particle swarm optimization (CGM-SPSO) algorithm is proposed. The global search ability of particle swarm optimization algorithm and local search ability of gradient algorithm are effectively combined, which overcomes the shortcoming that the conjugate gradient method tends to converge to the local solution and relies heavily on the initial approximation of the iterative process. The hybrid algorithm also avoids the problem that the particle swarm optimization algorithm requires a large number of iterative steps and a lot of time. The experimental results show that the proposed algorithm is feasible and effective in solving the problem of two-dimensional steady-state thermal boundary shape.

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Identification of an embedded tumor using the thermography process and the topological sensitivity analysis method

Abdelwahed

Chorfi

Ghezaiel

2021

Math Methods in App Sciences

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This work is concerned with a geometric inverse problem related to the thermography concept. The aim is the identification of the shape, size and location of a small embedded tumor from measured temperature on the skin surface.The temperature distribution in the biological tissue is governed by the Pennes model equation. Our proposed approach is based on the Kohn-Vogelius formulation and the topological sensitivity analysis method. The ill-posed geometric inverse problem is reformulated as a topology optimization one. The topological gradient is exploited for locating the zone containing the embedded tumor.The size and shape of the infected zone are approximated using the solution of a scalar parameter estimate problem. The efficiency and accuracy of the proposed approach are justified by some numerical simulations.

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An inverse geometry problem for the localisation of skin tumours by thermal analysis

Cited by 54 publications

References 15 publications

A coupled dual reciprocity BEM/genetic algorithm for identification of blood perfusion parameters

A coupled dual reciprocity BEM/genetic algorithm for identification of blood perfusion parameters

Two-Dimensional Steady-State Boundary Shape Inversion of CGM-SPSO Algorithm on Temperature Information

Identification of an embedded tumor using the thermography process and the topological sensitivity analysis method

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