@inproceedings{c468c9da3d2b4c048f87b170feb0068c,
title = "Deep learning based high-resolution reconstruction of trabecular bone microstructures from low-resolution CT scans using GAN-CIRCLE",
abstract = "Osteoporosis is a common age-related disease characterized by reduced bone density and increased fracture-risk. Microstructural quality of trabecular bone (Tb), commonly found at axial skeletal sites and at the end of long bones, is an important determinant of bone-strength and fracture-risk. High-resolution emerging CT scanners enable in vivo measurement of Tb microstructures at peripheral sites. However, resolution-dependence of microstructural measures and wide resolution-discrepancies among various CT scanners together with rapid upgrades in technology warrant data harmonization in CT-based cross-sectional and longitudinal bone studies. This paper presents a deep learning-based method for high-resolution reconstruction of Tb microstructures from low-resolution CT scans using GAN-CIRCLE. A network was developed and evaluated using post-registered ankle CT scans of nineteen volunteers on both low- and high-resolution CT scanners. 9,000 matching pairs of low- and high-resolution patches of size 64×64 were randomly harvested from ten volunteers for training and validation. Another 5,000 matching pairs of patches from nine other volunteers were used for evaluation. Quantitative comparison shows that predicted high-resolution scans have significantly improved structural similarity index (p < 0.01) with true high-resolution scans as compared to the same metric for low-resolution data. Different Tb microstructural measures such as thickness, spacing, and network area density are also computed from low- and predicted high-resolution images, and compared with the values derived from true high-resolution scans. Thickness and network area measures from predicted images showed higher agreement with true high-resolution CT (CCC = [0.95, 0.91]) derived values than the same measures from low-resolution images (CCC = [0.72, 0.88]).",
keywords = "Deep learning, GAN-CIRCLE, High-resolution reconstruction, Microstructure, Osteoporosis, Trabecular bone",
author = "Indranil Guha and Nadeem, {Syed Ahmed} and Chenyu You and Xiaoliu Zhang and Levy, {Steven M.} and Ge Wang and Torner, {James C.} and Saha, {Punam K.}",
note = "Funding Information: This work was supported in part by the NIH grant R01 HL142042. Publisher Copyright: {\textcopyright} 2020 SPIE; Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging ; Conference date: 18-02-2020 Through 20-02-2020",
year = "2020",
doi = "10.1117/12.2549318",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Andrzej Krol and Gimi, {Barjor S.}",
booktitle = "Medical Imaging 2020",
}