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Proceedings Paper

Deep learning based high-resolution reconstruction of trabecular bone microstructures from low-resolution CT scans using GAN-CIRCLE
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Paper 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 highresolution 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]).

Paper Details

Date Published: 28 February 2020
PDF: 11 pages
Proc. SPIE 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging, 113170U (28 February 2020); doi: 10.1117/12.2549318
Show Author Affiliations
Indranil Guha, The Univ. of Iowa (United States)
Syed Ahmed Nadeem, The Univ. of Iowa (United States)
Chenyu You, Yale Univ. (United States)
Xiaoliu Zhang, The Univ. of Iowa (United States)
Steven M. Levy, The Univ. of Iowa (United States)
Ge Wang, Rensselaer Polytechnic Institute (United States)
James C. Torner, The Univ. of Iowa (United States)
Punam K. Saha, The Univ. of Iowa (United States)


Published in SPIE Proceedings Vol. 11317:
Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging
Andrzej Krol; Barjor S. Gimi, Editor(s)

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