International Journal of Legal Medicine

, Volume 131, Issue 3, pp 699–712 | Cite as

Automatic CT-based finite element model generation for temperature-based death time estimation: feasibility study and sensitivity analysis

  • Sebastian Schenkl
  • Holger Muggenthaler
  • Michael Hubig
  • Bodo Erdmann
  • Martin Weiser
  • Stefan Zachow
  • Andreas Heinrich
  • Felix Victor Güttler
  • Ulf Teichgräber
  • Gita Mall
Original Article


Temperature-based death time estimation is based either on simple phenomenological models of corpse cooling or on detailed physical heat transfer models. The latter are much more complex but allow a higher accuracy of death time estimation, as in principle, all relevant cooling mechanisms can be taken into account.

Here, a complete workflow for finite element-based cooling simulation is presented. The following steps are demonstrated on a CT phantom:
  • Computer tomography (CT) scan

  • Segmentation of the CT images for thermodynamically relevant features of individual geometries and compilation in a geometric computer-aided design (CAD) model

  • Conversion of the segmentation result into a finite element (FE) simulation model

  • Computation of the model cooling curve (MOD)

  • Calculation of the cooling time (CTE)

For the first time in FE-based cooling time estimation, the steps from the CT image over segmentation to FE model generation are performed semi-automatically. The cooling time calculation results are compared to cooling measurements performed on the phantoms under controlled conditions. In this context, the method is validated using a CT phantom. Some of the phantoms’ thermodynamic material parameters had to be determined via independent experiments.

Moreover, the impact of geometry and material parameter uncertainties on the estimated cooling time is investigated by a sensitivity analysis.


Temperature-based death time estimation Finite element method Semi-automatic CT segmentation and FE model generation Validation Sensitivity analysis Cooling experiments 



We gratefully acknowledge the technical CT aid of Mrs. Antje Kubin as MTRA in IDIR II as well as the information contributions of beemaster Mr. Bernd Woker, Neuengönna concerning the phantom material beeswax.

Compliance with ethical standards

No human participants and/or animals were involved in the study.

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

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  1. 1.
    Marshall T, Hoare F (1962) Estimating the time of death: the rectal cooling after death and its mathematical expression. J Forensic Sci 7:56–81Google Scholar
  2. 2.
    Henssge C (1988) Death time estimation in case work. I. The rectal temperature time of death nomogram. Forensic Sci Int 38:209–236. doi: 10.1016/0379-0738(88)90168-5 CrossRefPubMedGoogle Scholar
  3. 3.
    Mall G, Eisenmenger W (2005a) Estimation of time since death by heat-flow finite-element model. Part I: method, model, calibration and validation. Legal Med 7:1–14. doi: 10.1016/j.legalmed.2004.06.006 CrossRefPubMedGoogle Scholar
  4. 4.
    Mall G, Eisenmenger W (2005b) Estimation of time since death by heat-flow finite-element model part II: application to non-standard cooling conditions and preliminary results in practical casework. Legal Med 7:69–80. doi: 10.1016/j.legalmed.2004.06.007 CrossRefPubMedGoogle Scholar
  5. 5.
    Tawhai MH, Hunter P, Tschirren J, Reinhardt J, McLennan G, Hoffman EA. (2004) CT-based geometry analysis and finite element models of the human and ovine bronchial tree. J Appl Physiol 97(6):2310–21Google Scholar
  6. 6.
    Chen G, Schmutz B, Epari D et al (2010) A new approach for assigning bone material properties from CT images into finite element models. J Biomech 43:1011–1015. doi: 10.1016/j.jbiomech.2009.10.040 CrossRefPubMedGoogle Scholar
  7. 7.
    Pednekar A, Bandekar AN, Kakadiaris IA, Naghavi M. (2005) Automatic Segmentation of Abdominal Fat from CT Data. Application of Computer Vision, 2005 WACV/MOTIONS ’05 Volume 1 Seventh IEEE Workshops on. pp. 308–15Google Scholar
  8. 8.
    Zhao B, Colville J, Kalaigian J, Curran S, Jiang L, Kijewski P, Schwartz LH (2006) Automated quantification of body fat distribution on volumetric computed tomography. J Comput Assist Tomogr 30:777–783CrossRefPubMedGoogle Scholar
  9. 9.
    Ohshima S, Yamamoto S, Yamaji T et al (2008) Development of an automated 3D segmentation program for volume quantification of body fat distribution using CT. Japanese Journal of Radiological Technology 64:1177–1181CrossRefPubMedGoogle Scholar
  10. 10.
    Kim Y, Lee S, Kim T, Park J, Choi S, Kim K (2013) Body fat assessment method using CT images with separation mask algorithm. J Digit Imaging 26:155–162. doi: 10.1007/s10278-012-9488-0 CrossRefPubMedGoogle Scholar
  11. 11.
    Deuflhard P, Schiela A, Weiser M (2012) Mathematical cancer therapy planning in deep regional hyperthermia. Acta Numerica 21:307–378. doi: 10.1017/S0962492912000049 CrossRefGoogle Scholar
  12. 12.
    Clarys JP, Provyn S, Marfell-Jones MJ (2005) Cadaver studies and their impact on the understanding of human adiposity. Ergonomics 48:1445–1461. doi: 10.1080/00140130500101486 CrossRefPubMedGoogle Scholar
  13. 13.
    Janssens V, Thys P, Clarys JP et al (1994) Post-mortem limitations of body composition analysis by computed tomography. Ergonomics 37:207–216. doi: 10.1080/00140139408963639 CrossRefPubMedGoogle Scholar
  14. 14.
    Romvári R, Dobrowolski A, Repa I et al (2006) Development of a computed tomographic calibration method for the determination of lean meat content in pig carcasses. Acta Vet Hung 54:1–10. doi: 10.1556/AVet.54.2006.1.1 CrossRefPubMedGoogle Scholar
  15. 15.
    Allen P, Branscheid W, Dobrowolski A, Horn P, Romvari R (2004). Schlachtkörperwertbestimmung beim Schwein - Röntgen-Computertomographie als mögliche Referenzmethode. FLEISCHWIRTSCHAFT 84(3):109–12Google Scholar
  16. 16.
    Rogalla P, Meiri N, Hoksch B et al (1998) Low-dose spiral computed tomography for measuring abdominal fat volume and distribution in a clinical setting. Eur J Clin Nutr 52:597–602CrossRefPubMedGoogle Scholar
  17. 17.
    Birnbaum BA, Hindman N, Lee J, Babb JS (2007) Multi-detector row CT attenuation measurements: assessment of intra- and interscanner variability with an anthropomorphic body CT phantom. Radiology 242:109–119. doi: 10.1148/radiol.2421052066 CrossRefPubMedGoogle Scholar
  18. 18.
    Hepburn HR (1986) Composition and synthesis of beeswax. In: Honeybees and Wax. Springer, Berlin Heidelberg, pp 44–56CrossRefGoogle Scholar
  19. 19.
    Ruguo Z, Hua Z, Hong Z, Ying F, Kun L, Wenwen Z (2011) Thermal analysis of four insect waxes based on differential scanning calorimetry (DSC). Procedia Engineering 18:101–106. doi: 10.1016/j.proeng.2011.11.016 CrossRefGoogle Scholar
  20. 20.
    Buchwald R, Breed MD, Greenberg AR (2008) The thermal properties of beeswaxes: unexpected findings. J Exp Biol 211:121–127. doi: 10.1242/jeb.007583 CrossRefPubMedGoogle Scholar
  21. 21.
    Höhne GWH, Hemminger WF, Flammersheim HJ (2003) Theoretical fundamentals of differential scanning calorimeters. In: Differential Scanning Calorimetry. Springer, Berlin Heidelberg, pp 31–63CrossRefGoogle Scholar
  22. 22.
    Arkar C, Medved S (2005) Influence of accuracy of thermal property data of a phase change material on the result of a numerical model of a packed bed latent heat storage with spheres. Thermochim Acta 438:192–201. doi: 10.1016/j.tca.2005.08.032 CrossRefGoogle Scholar
  23. 23.
    Kong J-Y (1982) The intrinsic thermal—conductivity of some wet proteins in relation to their hydrophobicity—analysis on gelatin gel. Agric Biol Chem 46:783–788Google Scholar
  24. 24.
    Timbers GE, Gochnauer TA (1982) Note on the thermal conductivity of beeswax. J Apic Res 29:232–235CrossRefGoogle Scholar
  25. 25.
    Çengel YAG, Afshin J (2015) Heat and mass transfer. McGraw-Hill Education New York, NYGoogle Scholar
  26. 26.
    Stefan Zachow MZ, Hans-Christian Hege. (2007) 3D reconstruction of individual anatomy from medical image data: Segmentation and geometry processing. In: Berlin ZI, ed. CADFEM Users’ Meeting 2007 DresdenGoogle Scholar
  27. 27.
    Sellier K (1958) Determination of the time of death by extrapolation of the temperature decrease curve. Acta Medicinae Socialis et Legalis 11:279–302Google Scholar
  28. 28.
    Smart JL (2010) Estimation of time of death with a Fourier series unsteady-state heat transfer model. J Forensic Sci 55:1481–1487. doi: 10.1111/j.1556-4029.2010.01467.x CrossRefPubMedGoogle Scholar
  29. 29.
    Zienkiewicz OC, Taylor RL, Zhu JZ (2005) The finite element method; volume 1: the Basis volume 2: solid and structural mechanics, 6th edn. Elsevier Ltd, OxfordGoogle Scholar
  30. 30.
    Deuflhard P, Weiser M. (2011) Numerische Mathematik 3: Adaptive Lösung partieller Differentialgleichungen. Walter de Gruyter GmbH & Co. KG, Berlin New YorkGoogle Scholar
  31. 31.
    Stalling D, Westerhoff M, Hege H (2005) Amira: a highly interactive system for visual data analysis. In: Hansen CDJ, Chris R (eds) The visualization handbook. Elsevier Butterworth-Heinemann Burlington, MA, pp 749–767CrossRefGoogle Scholar
  32. 32.
    Götschel S, Weiser M, Schiela A (2012) Solving optimal control problems with the Kaskade 7 finite element toolbox. In: Dedner A, Flemisch B, Klöfkorn R (eds) Advances in DUNE: Proceedings of the DUNE User Meeting, Held in October 6th–8th 2010 in Stuttgart, Germany. Springer, Berlin Heidelberg Berlin, Heidelberg, pp 101–112CrossRefGoogle Scholar
  33. 33.
    Handels H. (2009) Grundlagen diagnose- und therapieunterstützender Bildverarbeitungssysteme. Medizinische Bildverarbeitung. Vieweg+Teubner. pp. 49–69Google Scholar
  34. 34.
    Handels H. (2009) Segmentierung medizinischer Bilddaten. Medizinische Bildverarbeitung. Vieweg+Teubner. pp. 95–156Google Scholar
  35. 35.
    Alkadhi H, Frauenfelder T. (2011) Polytrauma. Wie funktioniert CT? Springer Berlin Heidelberg. pp. 153–62Google Scholar
  36. 36.
    Glasbey CA, Robinson CD (2002) Estimators of tissue proportions from X-ray CT images. Biometrics 58:928–936. doi: 10.1111/j.0006-341X.2002.00928.x CrossRefPubMedGoogle Scholar
  37. 37.
    Hofer M. (2010) CT-Kursbuch. Didamed-Verlag DüsseldorfGoogle Scholar
  38. 38.
    Fullerton GD (1980) Fundamentals of CT tissue characterization. In: Fullerton GD, Zagzebski JA (eds) Medical physics of CT and ultrasound. American Association of Physicists in Medicine Monograph 6, AAPM New York, NY, pp 125–162Google Scholar
  39. 39.
    Duck FA (1990) Physical properties of tissue. Academic, LondonGoogle Scholar
  40. 40.
    Washburn EW, West CJ (1986) International critical tables of numerical data, physics, chemistry and technology. University Microfilm International, Cornell UniversityGoogle Scholar
  41. 41.
    Schade I (2015) Eigenschaften des Bienenwachses. 20 May 2015
  42. 42.
    Timbers GE, Robertson GD, Gochnauer TA (1977) Thermal properties of beeswax and beeswax-paraffin mixtures. J Apic Res 16:49–55. doi: 10.1080/00218839.1977.11099860 CrossRefGoogle Scholar
  43. 43.
    The Engineering Toolbox (Online Reference). (2015) 06 Nov. 2015
  44. 44.
    Mohsenin NN (1980) Thermal properties of foods and agricultural materials. Gordon and Breach New York, NYGoogle Scholar
  45. 45.
    Park JB (2007) Biomaterials. Springer Science New York, NYGoogle Scholar
  46. 46.
    Zilske M, Lamecker H, Zachow S (2008) Adaptive remeshing of non-manifold surfaces. Eurographics 27Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Sebastian Schenkl
    • 1
  • Holger Muggenthaler
    • 1
  • Michael Hubig
    • 1
  • Bodo Erdmann
    • 2
  • Martin Weiser
    • 2
  • Stefan Zachow
    • 2
  • Andreas Heinrich
    • 3
  • Felix Victor Güttler
    • 3
  • Ulf Teichgräber
    • 3
  • Gita Mall
    • 1
  1. 1.Institute of Forensic MedicineJena University Hospital—Friedrich Schiller University JenaJenaGermany
  2. 2.Zuse Institute BerlinBerlinGermany
  3. 3.Institute of Diagnostic and Interventional RadiologyJena University Hospital—Friedrich Schiller University JenaJenaGermany

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