0
Technical Brief

Finite Element Method Based Three-Dimensional Thermal Tomography for Disease Diagnosis of Human Body

[+] Author and Article Information
Chao Jin

Department of Biomedical Engineering,
School of Medicine,
Tsinghua University,
Beijing 100084, China;
Department of Diagnostic Radiology,
The First Affiliated Hospital of Xi'an Jiaotong University,
Xi'an 710061, China

Zhi-Zhu He

Beijing Key Lab of Cryo-Biomedical Engineering and Key
Lab of Cryogenics,
Technical Institute of Physics and Chemistry,
Chinese Academy of Sciences,
Beijing 100190, China
e-mail: zzhe@mail.ipc.ac.cn

Jing Liu

Department of Biomedical Engineering,
School of Medicine,
Tsinghua University,
Beijing 100084, China;
Beijing Key Lab of Cryo-Biomedical Engineering
and Key Lab of Cryogenics,
Technical Institute of Physics and Chemistry,
Chinese Academy of Sciences,
Beijing 100190, China
e-mail: jliubme@tsinghua.edu.cn

1Corresponding authors.

Contributed by the Heat Transfer Division of ASME for publication in the JOURNAL OF HEAT TRANSFER. Manuscript received December 30, 2014; final manuscript received May 3, 2016; published online June 7, 2016. Assoc. Editor: Zhixiong Guo.

J. Heat Transfer 138(10), 104501 (Jun 07, 2016) (6 pages) Paper No: HT-14-1839; doi: 10.1115/1.4033612 History: Received December 30, 2014; Revised May 03, 2016

A finite element method (FEM)-based thermal approach to reconstruct the disease-associated heat source distribution has been developed. The congruent relationship between the heat sources and the observed temperature is established from the FEM solution matrix. The regularization method based parameter iteration algorithm is further developed to solve the inverse bioheat transfer problems. Typical simulations on sphere model and real digital human head have been performed to validate the feasibility and efficacy of the current method. In addition, the regularization parameter is optimized to speed up the reconstruction process and reduce the thermal noises. All the results indicate that both the heat source distribution and three-dimensional (3D) temperature field within the biological body were successfully reconstructed with acceptable accuracy.

FIGURES IN THIS ARTICLE
<>
Copyright © 2016 by ASME
Your Session has timed out. Please sign back in to continue.

References

Reilly, T. , and Down, A. , 1992, “ Investigation of Circadian Rhythms in Anaerobic Power and Capacity of the Legs,” J. Sport Med. Phys. Fitness, 32(4), pp. 343–347.
Oddo, M. , Schaller, M. D. , Feihl, F. , Ribordy, V. , and Liaudet, L. , 2006, “ From Evidence to Clinical Practice: Effective Implementation of Therapeutic Hypothermia to Improve Patient Outcome After Cardiac Arrest,” Crit. Care Med., 34(7), pp. 1865–1873. [CrossRef] [PubMed]
Gal, R. , Slezak, M. , Zimova, I. , Cundrle, I. , Ondraskova, H. , and Seidlova, D. , 2009, “ Therapeutic Hypothermia After Out-of-Hospital Cardiac Arrest With the Target Temperature 34–35 Degrees C,” Bratisl. Lek. Listy, 110(4), pp. 222–225. [PubMed]
Yan, J. F. , and Liu, J. , 2008, “ Nanocryosurgery and Its Mechanisms for Enhancing Freezing Efficiency of Tumor Tissues,” Nanomedicine, 4(1), pp. 79–87. [PubMed]
Liu, J. , and Deng, Z. S. , 2008, Tumor Hyperthermia Physics, Science Press, Beijing, China (in Chinese).
Yang, J. , Li, K. Y. , and Zhang, S. P. , 2006, “ The Non-Invasive 3-D Temperature Image Reconstruction of Organism by Ansys,” International Conference on Machine Learning and Cybernetics, Institute of Electrical and Electronics Engineers, Dalian, China, pp. 4268–4272.
Deng, Z. S. , and Liu, J. , 2002, “ Monte Carlo Method to Solve Multidimensional Bioheat Transfer problem,” Numer. Heat Transfer, Part B, 42(6), pp. 543–567. [CrossRef]
Li, K. Y. , Dong, Y. G. , Chen, C. , and Zhang, S.-P. , 2008, “ The Noninvasive Reconstruction of 3D Temperature Field in a Biological Body With Monte Carlo Method,” Neurocomputing, 72, pp. 128–133. [CrossRef]
Paulsen, K. D. , and Jiang, H. , 1995, “ Spatially Varying Optical Property Reconstruction Using a Finite Element Diffusion Equation Approximation,” Med. Phys., 22(6), pp. 691–701. [CrossRef] [PubMed]
Pennes, H. H. , 1948, “ Analysis of Tissue and Arterial Blood Temperatures in the Resting Human Forearm,” J. Appl. Physiol., 1(2), pp. 93–122. [PubMed]
Yu, D. H. , and Tang, H. Z. , 2003, Numerical Solution of Differential Equation, Science Press, Beijing, China (in Chinese).
Khennane, A. , 2013, Introduction to Finite Element Analysis Using MATLAB® and Abaqus, CRC Press, Boca Raton, FL.
Alberty, J. , Carstensen, C. , and Funken, S. A. , 1999, “ Remarks Around 50 Lines of Matlab: Short Finite Element Implementation,” Numer. Algorithms, 20, pp. 117–137. [CrossRef]
Gu, X. , Zhang, Q. , Larcom, L. , and Jiang, H. , 2004, “ Three-Dimensional Bioluminescence Tomography With Model-Based Reconstruction,” Opt. Express, 12(17), pp. 3996–4000. [CrossRef] [PubMed]
Li, S. , Zhang, Q. , and Jiang, H. , 2006, “ Two-Dimensional Bioluminescence Tomography: Numerical Simulations and Phantom Experiments,” Appl. Opt., 45(14), pp. 3390–3394. [CrossRef] [PubMed]
Cong, W. , Wang, G. , Kumar, D. , Liu, Y. , Jiang, M. , Wang, L. , Hoffman, E. , McLennan, G. , McCray, P. , Zabner, J. , and Cong, A. , 2005, “ Practical Reconstruction Method for Bioluminescence Tomography,” Opt. Express, 13(18), pp. 6756–6771. [CrossRef] [PubMed]
Hansen, P. C. , 1999, “ The L-Curve and Its Use in the Numerical Treatment of Inverse Problems,” Computational Inverse Problems in Electrocardiology, WIT Press, Southampton, UK, pp. 119–142.
Cao, X. , Zhang, B. , Wang, X. , Liu, F. , Liu, K. , Luo, J. , and Bai, J. , 2013, “ An Adaptive Tikhonov Regularization Method for Fluorescence Molecular Tomography,” Med. Biol. Eng. Comput., 51(8), pp. 849–858. [CrossRef] [PubMed]
Liu, F. , Cao, X. , He, W. , Song, J. , Dai, Z. , Zhang, B. , Luo, J. , Li, Y. , and Bai, J. , 2012, “ Monitoring of Tumor Response to Cisplatin by Subsurface Fluorescence Molecular Tomography,” J. Biomed. Opt., 17, p. 040504. [CrossRef] [PubMed]
Fang, Q. , and Boas, D. A. , 2009, “ Tetrahedral Mesh Generation From Volumetric Binary and Grayscale Images,” IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Institute of Electrical and Electronics Engineers, Boston, MA, pp. 1142–1145.
Yacoob, S. M. , and Hassan, N. S. , 2012, “ FDTD Analysis of a Noninvasive Hyperthermia System for Brain Tumors,” Biomed. Eng. Online 11:47.
Collins, C. M. , Liu, W. , Wang, J. , Gruetter, R. , Vaughan, J. T. , Ugurbil, K. , and Smith, M. B. , 2004, “ Temperature and SAR Calculations for a Human Head Within Volume and Surface Coils at 64 and 300 MHz,” J. Magn. Reson. Imaging, 19(5), pp. 650–656. [CrossRef] [PubMed]

Figures

Grahic Jump Location
Fig. 1

The schematic diagram of the biological body (including two domains Ω1 and Ω2) and its boundary conditions

Grahic Jump Location
Fig. 2

The geometrical model of digital head and mesh generation: (a) the geometrical model of human head, (b) the mesh results of head tissue section, (c) the geometrical model of gray matter, (d) the mesh results of gray matter section, and (e) the mesh density distribution near the interface between the head tissue and gray matter

Grahic Jump Location
Fig. 3

The calculated results of the initial solving condition and the reconstructed target: (a) the normal 3D temperature distribution, (c) the preset heat source induced 3D temperature distribution within the sphere biological body, and (b) and (d) represent the numerical results on the yOz section for cases (a) and (c), respectively

Grahic Jump Location
Fig. 4

The reconstructed results of the heat source distribution within the spherical biological body: (a) the view of the reconstructed 3D temperature field's slice images and (b) the monitoring results of residual curve during the iterative process for solving the inverse problem

Grahic Jump Location
Fig. 5

The accuracy analysis of the reconstructed 3D temperature field: (a) the reconstructed 3D temperature distribution within the sphere biological body, (c) the deviation distribution between the reconstructed target and reconstructed result, and (b) and (d) represent the numerical results on the yOz section for cases (a) and (c), respectively

Grahic Jump Location
Fig. 6

The quantitative analysis for the reconstructed accuracy and efficiency with the regularization parameter. (a) The deviation distribution between the reconstructed target and reconstructed result at (a) λ = 10−2, (c) λ = 10−4, and (e) λ = 10−6; the residual error curve during the iterative process at(b) λ = 10−2 (computational time = 850.5 s), (d) λ = 10−4 (computational time = 197.7 s), and (f) λ = 10−6 (computational time = 180.3 s).

Grahic Jump Location
Fig. 8

The reconstructed results and accuracy analysis of the heat source distribution within the human head: (a) the view of the reconstructed 3D temperature field's slice images, (b) the reconstructed 3D temperature distribution within the human head, (c) the deviation distribution between the reconstructed target and the original result, and (d) the monitoring results of residual curve during the iterative process for solving the inverse problem

Grahic Jump Location
Fig. 7

The calculated results of the initial solving condition and reconstructed target: (a) the normal 3D temperature distribution and (b) the preset heat source induced 3D temperature distribution within the human head

Tables

Errata

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In