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Identification of an embedded tumor using the thermography process and the topological sensitivity analysis method. (English) Zbl 1530.92106

MSC:

92C55 Biomedical imaging and signal processing
35K05 Heat equation
49N45 Inverse problems in optimal control
Full Text: DOI

References:

[1] LahiriB, BagavathiappanS, JayakumarT, PhilipJ. Medical applications of infrared thermography: a review. Infrared Phys Technol. 2012;55:221‐235.
[2] MagalhaesC, VardascaR, MendesJ. Recent use of medical infrared thermography in skin neoplasms. Skin Res Technol. 2018;1‐3:1‐5.
[3] ShiG, HanF, LiangC, WangL, LiK. A novel method of thermal tomography tumor diagnosis and its clinical practice. Appl Therm Eng. 2014;73:408‐415.
[4] HeadJF, WangF, LipariCA, ElliottRL. The important role of infrared imaging in breast cancer. IEEE Eng Med Biol Mag. 2000;19:52‐57.
[5] Di CarloA. Thermography and the possibilities for its applications in clinical and experimental dermatology. Clinics Dermatol. 1995;13:329‐336.
[6] BuzugTM, SchumannS, PfaffmannL, ReinholdU, RuhlmannJ. Functional infrared imaging for skin‐cancer screening IEEE; 2006:2766‐2769.
[7] WangH, WadeDRJ, KamJ. Ir imaging of blood circulation of patients with vascular disease. Def Secur Int Soc Opt Photon. 2004;5405:115‐123.
[8] Santa CruzG. Dynamic infrared imaging of cutaneous melanoma and normal skin in patients treated with bnct. Appl Radiat Isot. 2009;67:54‐58.
[9] HelmyA, HoldmannM, RizkallaM. Application of thermography for non‐invasive diagnosis of thyroid gland disease. IEEE Trans Biomed Eng. 2008;55:1168‐1175.
[10] TanJH, NgE, AcharyaUR, CheeC. Infrared thermography on ocular surface temperature: a review. Infrared Phys Technol. 2009;52:97‐108.
[11] ShadaAL, DengelLT, PetroniGR, SmolkinME, ActonS, SlingluffCL. Infrared thermography of cutaneous melanoma metastases. J Surgical Res. 2013;182:9‐14.
[12] ÇetingulMP, HermanC. Quantification of the thermal signature of a melanoma lesion. Int J Therm Sci. 2011;50:421‐431.
[13] BonmarinM, Le GalFA. Lock‐in thermal imaging for the early‐stage detection of cutaneous melanoma: a feasibility study. Comput Biol Med. 2014;47:36‐43.
[14] KeyserlingkJR, AhlgrenP, YuE, BelliveauN, YassaM. Functional Infrared Imaging of the Breast: Historical Perspectives, Current Application and Future Considerations. CRC Press; 2006.
[15] HatwarR, HermanC. Inverse method for quantitative characterisation of breast tumours from surface temperature data. Int J Hyperth. 2017;33:1‐17.
[16] AmriA, PulkoSH, WilkinsonAJ. Potentialities of steady‐state and transient thermography in breast tumour depth detection: a numerical study. Comput Methods Programs Biomed. 2016;123:68‐80.
[17] AmmerK, RingE. Standard Procedures for Infrared Imaging in Medicine. CRC Press; 2006.
[18] MarchJ, HandM, GrossmanD. Practical application of new technologies for melanoma diagnosis: part i. Noninvasve approaches. J Am Acad Dermatol. 2015;72:929‐941.
[19] IljaJ, WrobelLC, HriberjekM, MarnJ. The use of design of experiments for steady‐state and transient inverse melanoma detection problems. Int J Therm Sci. 2019;135:256‐275.
[20] ChengTY, HermanC. Analysis of skin cooling for quantitative dynamic infrared imaging of near‐surface lesions. Int J Therm Sci. 2014;86:175‐188.
[21] StickerM, HorstmannI, NichelC, RochlingA, HoffmannK, AltmeyerP. Blood flow compared in benign melanocytic naevi, malignant melanomas and basal cell carcinomas. Clin Exp Dermatol. 1999;24:107‐111.
[22] ÇetingulM, HermanC. A heat transfer model of skin tissue for detection of lesions: sensitivity analysis. Phys Med Biol. 2010;55:5933‐5951.
[23] SongCW, RheeJG, LevittSH. Blood flow in normal tissues and tumors during hyperthermia. J Natl Cancer Inst. 1980;64:119‐124.
[24] IljaJ, WrobelLC, HriberjekM, MarnJ. Numerical modelling of skin tumour tissue with temperature‐dependent properties for dynamic thermography. Comput Biol Med. 2019;112:103‐167.
[25] SwetterSM. Malignant melanoma. Available Online: http://emedicine.medscape.com/article/1100753‐overview/; 2010.
[26] Skin Cancer Foundation website. Available Online: http://www.skincancer.org/Skin‐Cancer‐Facts/; 2010.
[27] ElderD. Tumor progression, early diagnosis and prognosis of melanoma. Acta Oncol. 1999;38:535‐547.
[28] FecherLA, CummingsSD, KeefeMJ, AlaniRM. Toward a molecular classification of melanoma. J Clin Oncol. 2007;25:1606‐1620.
[29] Pirtini ÇetingulM, HermanC. A heat transfer model of skin tissue for the detection of lesions: sensitivity analysis. Phys Med Biol. 2010;55:5933‐5951.
[30] IljazaJ, WrobelbLC, HribersekaM, MarnaJ. Numerical modelling of skin tumour tissue with temperature‐dependent properties for dynamic thermography. Comput Biol Med. 2019;112:1‐15.
[31] BhowmikA, RepakaR. Estimation of growth features and thermophysical properties of melanoma within 3D human skin using genetic algorithm and simulated annealing. Int J Heat Mass Transfer. 2016;98:81‐95.
[32] PennesHH. Analysis of tissue and arterial blood temperatures in the resting human forearm. J Appl Physiol. 1948;1(2):93‐122.
[33] SilvaABC, LaszczykJ, WrobelLC, RibeiroFL, NowakAJ. A thermoregulation model for hypothermic treatment of neonates. Med Eng Phys. 1999;38:988‐998.
[34] FialaD, LomasKJ, StohrerM. A computer model of human thermoregulation for a wide range of environmental conditions: the passive system. J Appl Physiol. 1999;87:1957‐1972.
[35] FialaD, HavenithG, BrodeP, KampmannB, JendritzkyG. UTCI‐Fiala multi‐node model of human heat transfer and temperature regulation. Int J Biometeorol. 2012;56:429‐441.
[36] WernerJ, BuseM. Temperature profiles with respect to inhomogeneity and geometry of the human body. J Appl Physiol. 1988;65:1110‐1118.
[37] MitchellJW, GalvezTL, HengleJ, MyersGE, SiebeckerKL. Thermal response of human legs during cooling. J Appl Physiol. 1970;29:859‐865.
[38] LazczykJE, NowakAJ. Computational modelling of neonate’s brain cooling. Int J Numer Methods Heat Fluid Flow. 2016;26:571‐590. · Zbl 1356.92044
[39] PatelJK, KondaS, PerezOA, AminiS, ElgartG, BermanB. Newer technologies/techniques and tools in the diagnosis of melanoma. Eur J Dermatol. 2008;18:617‐631.
[40] MarchesiniR, BonoA, BartoliC, LualdiM, TomatisS, CascinelliN. Optical imaging and automated melanoma detection: questions and answers. Melanoma Res. 2002;12:279‐286.
[41] MarghoobAA, SwindleLD, MoriczCZ, NegronFAS, SlueB, HalpernAC, KopfAW. Instruments and new technologies for the in vivo diagnosis of melanoma. J Am Acad Dermatol. 2003;49:777‐797.
[42] MeloAR, LoureiroMMS, LoureiroF. Blood perfusion parameter estimation in tumors by means of a genetic algorithm. Procedia Comput Sci. 2017;108:1384‐1393.
[43] PartridgePW, WrobelLC. An inverse geometry problem for the localisation of skin tumours by thermal analysis. Eng Anal Bound Elem. 2007;31(10):803‐811. · Zbl 1195.80019
[44] PartridgePW, WrobelLC. A coupled dual reciprocity bem/genetic algorithm for identification of blood perfusion parameters. Int J Numer Methods Heat Fluid Flow. 2009;19(1):25‐38. · Zbl 1419.76734
[45] LunaJM, Romero‐MendezR, Hernandez‐GuerreroA, FE‐B. Procedure to estimate thermophysical and geometrical parameters of embedded cancerous lesions using thermography. J Biomech Eng. 2012;134(3):1‐9.
[46] LunaJM, Hernandez GuerreroA, Romero MendezR, Luviano OrtizJL. Solution of the inverse bio‐heat transfer problem for a simplified dermatological application: case of skin cancer. Ing Mecanica Tecnol y Desarro. 2014;4(6):219‐228.
[47] ParuchM, MajchrzakE. Identification of tumor region parameters using evolutionary algorithm and multiple reciprocity boundary element method. Eng Appl Artif Intell. 2007;20(5):647‐655.
[48] DombrovskyLA, TimchenkoV, PathakC, PiazenaH, MullerW, JacksonM. Radiative heating of superficial human tissues with the use of water‐filtered infrared‐a radiation: a computational modeling. Int J Heat Mass Transf. 2015;85:311‐320.
[49] ChengT‐Y, HermanC. Analysis of skin cooling for quantitative dynamic infrared imaging of near‐surface lesions. Int J Therm Sci. 2014;86:175‐88.
[50] BhowmikA, RepakaSC. Thermographic evaluation of early melanoma within the vascularized skin using combined non‐newtonian blood flow and bioheat models. Comput Biol Med. 2014;53:206‐219.
[51] BhowmikA, RepakaR, MulaveesalaR, MishraSC. Suitability of frequency modulated thermal wave imaging for skin cancer detection: a theoretical prediction. J Therm Biol. 2015;51:65‐82.
[52] BhowmikA, RepakaR. Estimation of growth features and thermophysical properties of melanoma within 3‐D human skin using genetic algorithm and simulated annealing. Int J Heat Mass Transf. 2016;98:81‐95.
[53] BonmarinM, Le GalF‐A. Lock‐in thermal imaging for the early‐stage detection of cutaneous melanoma: a feasibility study. Comput Biol Med. 2014;47:36‐43.
[54] KohnR, VogeliusM. Determining conductivity by boundary measurements. Commun Pur Appl Math. 1984;37(3):289‐298. · Zbl 0586.35089
[55] AndrieuxS, BarangerTN, BenabdaA. Solving Cauchy problems by minimizing an energy‐like functional. Inverse Probl. 2006;22(1):115. · Zbl 1089.35084
[56] AbdelwahedM, HassineM, MasmoudiM. Optimal shape design for fluid flow using topological perturbation technique. J Math Anal Appl. 2009;356(2):548‐563. · Zbl 1172.35049
[57] BenabdaA, HassineM, JaouaM, MasmoudiM. Topological sensitivity analysis for the location of small cavities in stokes flow. SIAM J Control Optim. 2009;48(5):2871‐2900. · Zbl 1202.49054
[58] HassineM, MasmoudiM. The topological asymptotic expansion for the quasi‐stokes problem. ESAIM: Control, Optimisation Calc Var. 2004;10(4):478‐504. · Zbl 1072.49027
[59] AmstutzS. Sensitivity analysis with respect to a local perturbation of the material property. Asymptot Anal. 2006;49(1, 2):87‐108. · Zbl 1187.49036
[60] CaubetF, DambrineM, KatebD. Shape optimization methods for the inverse obstacle problem with generalized impedance boundary conditions. Inverse Probl. 2013;29(11):115011. · Zbl 1292.65069
[61] NovotnyAA, SokolowskiJ. Topological Derivatives in Shape Optimizationof MechanicsI (ed.), SeriesM (ed.), eds. Berlin, Heidelberg: Springer‐Verlag; 2013.
[62] NovotnyAA, SokolowskiJ. An Introduction to the Topological Derivative Methodin MathematicsSB (ed.), ed. Switzerland: Springer‐Nature; 2020. · Zbl 1444.49001
[63] NovotnyAA, SokolowskiJ. Applications of the Topological Derivative Method. Studies in SystemsDecision (ed.), Series.C (ed.), eds. Switzerland: Springer‐Nature; 2019. · Zbl 1460.74001
[64] AmstutzS, HorchaniI, MasmoudiM. Crack detection by the topological gradient method. Control Cybern. 2005;34(1):81‐101. · Zbl 1167.74437
[65] FerchichiJ, HassineM, KhenousH. Detection of point‐forces location using topological algorithm in stokes flows. Appl Math Comput. 2013;219(12):7056‐7074. · Zbl 1426.76147
[66] GuzinaBB, BonnetM. Small‐inclusion asymptotic of misfit functionals for inverse problems in acoustics. Inverse Probl. 2006;22(5):1761. · Zbl 1105.76055
[67] GarreauS, GuillaumeP, MasmoudiM. The topological asymptotic for PDE systems: the elasticity case. SIAM J Control Optim. 2001;39(6):1756‐1778. · Zbl 0990.49028
[68] LarnierS, FehrenbachJ, MasmoudiM. The topological gradient method: from optimal design to image processing. Milan J Math. 2012;80(2):411‐441. · Zbl 1262.94005
[69] BelaidLJ, JaouaM, MasmoudiM, SialaL. Image restoration and edge detection by topological asymptotic expansion. Comptes Rendus Mathematique. 2006;342(5):313‐318. · Zbl 1086.68141
[70] HandamardJ. Lectures on the Cauchy Problems in Linear Partial Differential Equations. New Haven: Yale University Press; 1923. · JFM 49.0725.04
[71] HechtF. New development in freefem++. J Numer Math. 2012;20(3‐4):251‐265. · Zbl 1266.68090
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