Dual-energy X-ray absorptiometry (DXA) is widely used for clinical assessment of bone mineral density (BMD). Recent evidence shows that DXA images may also contain microstructural information of trabecular bones. However, no current image processing techniques could aptly extract the information. Inspired by the success of deep learning techniques in medical image analyses, we hypothesized in this study that DXA image-based deep learning models could predict the major microstructural features of trabecular bone with a reasonable accuracy. To test the hypothesis, 1249 trabecular cubes (6 mm × 6 mm × 6 mm) were digitally dissected out from the reconstruction of seven human cadaveric proximal femurs using microCT scans. From each cube, simulated DXA images in designated projections were generated, and the histomorphometric parameters (i.e., BV/TV, BS, Tb.Th, DA, Conn. D, and SMI) of the cube were determined using Image J. Convolutional neural network (CNN) models were trained using the simulated DXA images to predict the histomorphometric parameters of trabecular bone cubes. The results exhibited that the CNN models achieved high fidelity in predicting these histomorphometric parameters (from R = 0.80 to R = 0.985), showing that the DL models exhibited the capability of predicting the microstructural features using DXA images. This study also showed that the number and resolution of input simulated DXA images had considerable impacts on the prediction accuracy of the DL models. These findings support the hypothesis of this study and indicate a high potential of using DXA images in prediction of osteoporotic bone fracture risk.
Copyright: © 2020 Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Date of publication
Xiao, Pengwei; Zhang, Tinghe; Dong, Xuanliang Neil; Han, Yan; Huang, Yufei; and Wang, Xiaodu, "Prediction of trabecular bone architectural features by deep learning models using simulated DXA images" (2020). Health and Kinesiology Faculty Publications and Presentations. Paper 32.