The noninvasive reconstruction of 3D temperature field in a biological body with Monte Carlo method

Neurocomputing

Volumn 72, Issue 1-3, 2008, Pages 128-133

  Li, Kai Yang ^a   Dong, Yong Gang ^a   Chen, Cheng ^a   Zhang, Shao Ping ^a  

^a BEIJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

Author keywords

Bayesian model; Bio heat conduction; Inverse problem; Markov chain; Monte Carlo

Indexed keywords

BAYESIAN NETWORKS; DIAGNOSIS; HEAT CONDUCTION; INVERSE PROBLEMS; MARKOV PROCESSES; MATHEMATICAL MODELS; MONTE CARLO METHODS; PILES; RESTORATION;

3D TEMPERATURE FIELDS; BAYESIAN MODEL; BIO-HEAT CONDUCTION; BIOLOGICAL BODIES; HEAT EQUATIONS; HEAT SOURCES; HIERARCHICAL BAYESIAN MODELS; MARKOV CHAIN; MARKOV CHAIN MONTE CARLO METHODS; MEDICAL DIAGNOSES; MONTE CARLO; RANDOM WALKS; SURFACE TEMPERATURES;

THREE DIMENSIONAL;

ARTICLE; BAYES THEOREM; BODY TEMPERATURE; CONTROLLED STUDY; HEAT TRANSFER; IMAGE RECONSTRUCTION; MONTE CARLO METHOD; NON INVASIVE MEASUREMENT; PRIORITY JOURNAL; PROBABILITY; TEMPERATURE MEASUREMENT; THERMAL CONDUCTIVITY; THREE DIMENSIONAL IMAGING;

EID: 55949111628     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2008.03.016     Document Type: Article

References (14)

1. Andrieu C., Freitas N.D., Doucet A., and Gordan M.I. An introduction to MCMC for machine learning. Mach. Learn. 50 (2003) 5-43
2. Beilina L., and Clason C. An adaptive hybrid FEM/FDM method for an inverse scattering problem in scanning acoustic microscopy. SIAM J. Sci. Comput. 28 (2006) 382-402
3. Chen M.M., Pedersen C.O., and Chato J.C. On the feasibility of obtaining three-dimensional information from thermographic measurements. ASME J. Biomech. Eng. 99 (1997) 58-64
4. Coccioli R., Pelosi G., and Selleri S. Characterization of dielectric materials with the finite-element method, microwave theory and techniques. IEEE Trans. Microwave Theory Tech. 47 (1999) 1106-1112
5. Deng Z.S., and Liu J. Noninvasive identification of time-dependent heat source by the Monte Carlo method. J. Eng. Thermophys. 24 1 (2003) 124-126 (in Chinese)
6. Deng Z.S., and Liu J. Mathematical modeling of temperature mapping over skin surface and its implementation in the thermal disease diagnostics. Comput. Biol. Med. 30 (2004) 495-521
7. Divo E.A., and Kassab A.J. Boundary Element Methods for Heat Conduction: With Applications in Non-Homogeneous Media (2003), WIT Press, Southampton
8. Geman S., and Geman D. Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. Trans. Pattern Anal. Mach. Intell. 6 (1984) 721-741
9. Gilks W.R., Richardson S., Spiegelhalter D.J., et al. Markov Chain Monte Carlo in Practice (1996), Chapman and Hall, New York
10. Kaipio J.P., Kolehmainen V., Somersalo E., and Vauhkonen M. Statistical inversion and Monte Carlo sampling methods in electrical impedance tomography. Inverse Probl. 16 (2000) 1487-1522
11. H. Kudrolli, W. Worstell, V. Zavarzin, An inverse Monte Carlo algorithm for 3D PET image reconstruction, In: Nuclear Science Symposium, Conference Record, IEEE, vol. 2, 1998, pp. 1332-1336.
12. Tikhonov A.N., and Arsenin V.Y. Solution of Ill-Posed Problems (1979), Halsted, New York
13. Wang J., and Zabaras N. A Bayesian inference approach to the inverse heat conduction problem. Int. J. Heat Mass Transfer 47 (2004) 3927-3941
14. Wang J., and Zabaras N. Hierarchical Bayesian models for inverse problems in heat conduction. Inverse Probl. 21 (2005) 183-206