Julien Ognard1,2, David Bourhis3,4, Romain Cadieu1, Michel Grenier5, Claire Saccardy6, Zarrin Alavi7, Douraied Ben Salem1,2. 1. Forensic Imaging Unit, University Hospital of Brest, 29609, Brest Cedex, France. 2. Latim, Inserm, UMR 1101, Brest Occidental University (UBO), 29238, Brest, France. 3. Nuclear Medicine Department, University Hospital of Brest, 29609, Brest Cedex, France. 4. GETBO EA 3878, Brest Occidental University (UBO), 29238, Brest, France. 5. General Directorate for Civil Security and Crisis Management, Explosive Ordinance Disposal office, French Ministry of Interior, 75008, Paris, France. 6. Forensic Institute, University Hospital of Brest, 29609, Brest Cedex, France. 7. Inserm, Clinical Investigation Center 1412, University Hospital of Brest, 29609, Brest Cedex, France. zarrin.alavi@chu-brest.fr.
Abstract
OBJECTIVE: Detection of explosives is a challenge due to the use of improvised and concealed bombs. Post-bomb strike bodies are handled by emergency and forensic teams. We aimed to determine whether medical dual-energy computed tomography (DECT) algorithm and prediction model can readily detect and distinguish a range of explosives on the human body during disaster victim identification (DVI) processes of bombings. MATERIALS AND METHODS: A medical DECT of 8 explosives (Semtex, Pastex, Hexamethylene triperoxide diamine, Acetone peroxide, Nitrocellulose, Pentrite, Ammonium Nitrate, and classified explosive) was conducted ex-vivo and on an anthropomorphic phantom. Hounsfield unit (HU), electron density (ED), effective atomic number (Zeff), and dual energy index (DEI),were compared by Wilcoxon signed rank test. Intra-class (ICC) and Pearson correlation coefficients (r) were computed. Explosives classification was performed through a prediction model with test-retest samples. RESULTS: Except for DEI (p = 0.036), means of HU, ED, and Zeff were not statistically different (p > 0.05) between explosives ex-vivo and on the phantom (r > 0.80). Intra- and inter-reader ICC were good to excellent: 0.806 to 0.997 and 0.890, respectively. Except for the phantom DEI, all measurements from each individual explosive differed significantly. HU, ED, Zeff, and DEI differed depending on the type of explosive. Our decision tree provided Zeff and ED for explosives classification with high accuracy (83.7%) and excellent reliability (100%). CONCLUSION: Our medical DECT algorithm and prediction model can readily detect and distinguish our range of explosives on the human body. This would avoid possible endangering of DVI staff.
OBJECTIVE: Detection of explosives is a challenge due to the use of improvised and concealed bombs. Post-bomb strike bodies are handled by emergency and forensic teams. We aimed to determine whether medical dual-energy computed tomography (DECT) algorithm and prediction model can readily detect and distinguish a range of explosives on the human body during disaster victim identification (DVI) processes of bombings. MATERIALS AND METHODS: A medical DECT of 8 explosives (Semtex, Pastex, Hexamethylene triperoxide diamine, Acetone peroxide, Nitrocellulose, Pentrite, Ammonium Nitrate, and classified explosive) was conducted ex-vivo and on an anthropomorphic phantom. Hounsfield unit (HU), electron density (ED), effective atomic number (Zeff), and dual energy index (DEI),were compared by Wilcoxon signed rank test. Intra-class (ICC) and Pearson correlation coefficients (r) were computed. Explosives classification was performed through a prediction model with test-retest samples. RESULTS: Except for DEI (p = 0.036), means of HU, ED, and Zeff were not statistically different (p > 0.05) between explosives ex-vivo and on the phantom (r > 0.80). Intra- and inter-reader ICC were good to excellent: 0.806 to 0.997 and 0.890, respectively. Except for the phantom DEI, all measurements from each individual explosive differed significantly. HU, ED, Zeff, and DEI differed depending on the type of explosive. Our decision tree provided Zeff and ED for explosives classification with high accuracy (83.7%) and excellent reliability (100%). CONCLUSION: Our medical DECT algorithm and prediction model can readily detect and distinguish our range of explosives on the human body. This would avoid possible endangering of DVI staff.
Authors: Elise D Roele; Veronique C M L Timmer; Lauretta A A Vaassen; Anna M J L van Kroonenburgh; A A Postma Journal: Curr Radiol Rep Date: 2017-03-29