Aging calvaria: Introduction of a numerical method to improve information extraction from computed tomography images. (June 2017)

• Record Type: Journal Article
• Authors: Obert, Martin; Rodenheber, Laura; Kampschulte, Marian; Krombach, Gabriele A.; Verhoff, Marcel A.
• Is Part Of: Journal of forensic radiology and imaging. Volume 9(2017)
• Page Start: 16
• Page End: 27
• Publication Date: 2017-06
• Subjects: Age-at-death predictions -- CT Histogram analysis -- Automated image analysis -- Non-linear function fit -- Multinomial logistic regression -- High-resolution flat-panel computed tomography; Forensic radiography -- Periodicals; Magnetic resonance imaging -- Periodicals; Diagnostic imaging -- Periodicals; Diagnostic imaging; Forensic radiography; Magnetic resonance imaging; Periodicals; 614.1
• DOI: 10.1016/j.jofri.2017.01.002
• Languages: English
• ISSNs: 2212-4780

Abstract

Abstract: Objectives: This study explored whether age-relevant information may be hidden in density histograms of computed tomography (CT) images of calvaria and whether this information can be extracted by a novel "histogram's functional shape" (HFS) analysis method. The method was compared with other CT-based bone analysis methods. Materials and Methods: Software was written that reanalyzed flat-panel CT data from European human skulls (120 female; 221 male). Calvarial data were segmented from these CT images and density histograms were produced thereof. A nonlinear curve fit was then used to calculate the HFS method. Seventeen multinomial logistic regression (MLR) model calculations were performed in a competition calculation in which the results of the HFS method, and other radiologically defined bone image markers were used as covariates to predict age-at-death (AAD). Age predictions for individual skulls were calculated for five equidistant age groups ranging from 0 to 100 years. Results and Conclusions: The HFS method could be applied successfully. Chi 2 HFS was «0.05 in all 675 skull density histogram analyses. When MLR model calculations that use covariates from one method only were compared, the HFS method had a higher AAD prediction power. The overall ranking list is led by the models that used multiple covariates from different methods: The best correct group assignment was 62.5% (Nagelkerke's pseudo R 2 (NR 2 ) =0.76) for females, and 51.6% (NR 2 =0.43) for males. Hence, a novel image marker was introduced, and it was shown that the use of combined methods is superior to individual methods. Highlights: The HFS method decodes age-relevant information encrypted in CT-density curves. HFS results have a higher age prediction power than bone density or fractal concepts. Multinomial logistic regression is a powerful tool to calculate age predictions. The more quantitative image markers are combined, the better the age predictions.