Detector nonlinearity in measuring the bone mineral density based on neural networks.
Authors
Affiliations (2)
Affiliations (2)
- Neuroscience Research Institute, Seoul National University Medical Research Center, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Korea (the Republic of).
- Division of Semiconductor and Electronics Engineering, Hankuk University of Foreign Studies, 81, Oedae-ro, Mohyeon-eup, Cheoin-gu, Yongin-si, 17035, Korea (the Republic of).
Abstract
A dual-layer flat panel detector (DFD) allows for the acquisition of dual-energy images with a single x-ray exposure without scanning. In this paper, we aim to perform noise sensitivity analyses on a single-shot approach using DFD in measuring bone mineral density (BMD) and propose a method for calculating BMD using features acquired from a nonlinear response detector without a nonlinearity correction scheme.
 
Approach. We evaluate the condition numbers of the BMD estimate functions and observe the mean squared error (MSE) for ranges of additive and multiplicative errors to compare with a dual-shot FPD approach. To accurately describe the BMD surface, we use a fully connected neural network (FCNN) model instead of the conventional multiple regression model. We conduct experiments to observe and model the nonlinear response of DFD, followed by simulations based on a BMD model with nonlinear intensities.
 
Main results. Extensive simulations demonstrate that BMD measurements using nonlinear intensities without correction outperform methods that calculate BMD after nonlinear correction. Furthermore, the approach based on the FCNN model significantly outperforms the conventional approach using a multiple regression model.
 
Significance. This approach enables the implementation of a simple, robust bone densitometry system that effectively bypasses complex detector nonlinearity issues, offering a highly accurate and cost-effective alternative to traditional dual-energy x-ray absorptiometry (DXA) scanning systems.