European Radiology Experimental (Jul 2020)

Heavy metal in radiology: how to reliably differentiate between lodged copper and lead bullets using CT numbers

  • Dominic Gascho,
  • Niklaus Zoelch,
  • Henning Richter,
  • Alexander Buehlmann,
  • Philipp Wyss,
  • Michael J. Thali,
  • Sarah Schaerli

DOI
https://doi.org/10.1186/s41747-020-00168-z
Journal volume & issue
Vol. 4, no. 1
pp. 1 – 12

Abstract

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Abstract Background The in situ classification of bullets is of interest in forensic investigations when the bullet cannot be removed. Although computed tomography (CT) is usually performed on shooting victims, visual assessment, or caliber measurements using CT can be challenging or infeasible if the bullets are deformed or fragmented. Independent from the bullet’s intactness, x-ray attenuation values (CT numbers) may provide information regarding the material of the bullet. Methods Ethical approval was not required (animal cadavers) or waived by the ethics committee (decedents). Copper and lead bullets were fired into animal cadavers, which then underwent CT scanning at four energy levels (80, 100, 120, and 140 kVp). CT numbers were measured within regions of interest (ROIs). In addition to comparing CT numbers, the dual-energy index (DEI), representing the ratio between the CT numbers of two energy levels, was calculated. The most appropriate method was applied for decedents with fatal gunshot wounds. Results CT numbers demonstrated no significant difference between copper and lead bullets, and false classifications can easily occur. DEI calculations revealed significant differences between the two groups of bullets. The 120/140 DEIs calculated from the maximum CT numbers obtained from ROIs at the edge of copper versus lead bullets presented a significant difference (p = 0.002) and a gap between the CT numbers of copper and lead bullets and was successfully applied for the decedents. Conclusions This study presents a viable method for distinguishing copper and lead bullets in situ via CT and highlights the potential pitfalls of incorrect classifications.

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