In Gwun Jang

In bone-remodeling studies, it is believed that the morphology of bone is affected by its internal mechanical loads. From the 1970s, high computing power enabled quantitative studies in the simulation of bone remodeling or bone adaptation. Among them, Huiskes et al. (1987, "Adaptive Bone Remodeling Theory Applied to Prosthetic Design Analysis," J. Biomech. Eng., 20, pp. 1135-1150) proposed a strain energy density based approach to bone remodeling and used the apparent density for the characteriz

Non-pneumatic tyres have been developed and are being investigated, but are not very prevalent. Many design studies are still needed from the viewpoint of material, pattern, and structures. However, no systematic research for such important design issues has been reported in the literature up to now. In this article, topology optimization was utilized to determine optimal topological patterns of non-pneumatic tyres in the design process, with the goal of matching the static stiffness of the curr

Abstract Design space optimization for topology based on fixed grid is proposed and its superiority to conventional topology optimization is shown. In the conventional topology optimization, the design domain is fixed. It is, however, desirable to make the design domain evolve into a better one during optimization process by increasing or decreasing the number of design pixels or variables, which we call design space optimization. A breakthrough in obtaining sensitivities when design space expan

Inspired by the self-optimizing capabilities of bone, a new concept of bone microstructure reconstruction has been recently introduced by using 2D synthetic skeletal images. As a preliminary clinical study, this paper proposes a topology optimization-based method that can estimate 3D trabecular bone microstructure for the volume of interest (VOI) from 3D computed tomography (CT) scan data with enhanced computational efficiency and phenomenological accuracy. For this purpose, a localized finite e

OBJECTIVES: This study proposes a regression model for the phantomless Hounsfield units (HU) to bone mineral density (BMD) conversion including patient physical factors and analyzes the accuracy of the estimated BMD values. METHODS: The HU values, BMDs, circumferences of the body, and cross-sectional areas of bone were measured from 39 quantitative computed tomography images of L2 vertebrae and hips. Then, the phantomless HU-to-BMD conversion was derived using a multiple linear regression model.