Deep Learning Based High-Resolution Reconstruction of Trabecular Bone Microstructures from Low-Resolution CT Scans using GAN-CIRCLE

Indranil Guha; Syed Ahmed Nadeem; Chenyu You; Xiaoliu Zhang; Steven M Levy; Ge Wang; James C Torner; Punam K Saha

doi:10.1117/12.2549318

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Journal article

Deep Learning Based High-Resolution Reconstruction of Trabecular Bone Microstructures from Low-Resolution CT Scans using GAN-CIRCLE

Indranil Guha, Syed Ahmed Nadeem, Chenyu You, Xiaoliu Zhang, Steven M Levy, Ge Wang, James C Torner and Punam K Saha

Proceedings of SPIE, the international society for optical engineering, Vol.11317, 113170U

02/2020

DOI: 10.1117/12.2549318

PMID: 32201450

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https://www.ncbi.nlm.nih.gov/pmc/articles/7085412View

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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]).

Osteoporosis

deep learning

GAN-CIRCLE

high-resolution reconstruction

microstructure

trabecular bone

Details

Title: Subtitle: Deep Learning Based High-Resolution Reconstruction of Trabecular Bone Microstructures from Low-Resolution CT Scans using GAN-CIRCLE
Creators: Indranil Guha - University of Iowa
Syed Ahmed Nadeem - University of Iowa
Chenyu You - Yale University
Xiaoliu Zhang - University of Iowa
Steven M Levy - University of Iowa
Ge Wang - Rensselaer Polytechnic Institute
James C Torner - University of Iowa
Punam K Saha - University of Iowa
Resource Type: Journal article
Publication Details: Proceedings of SPIE, the international society for optical engineering, Vol.11317, 113170U
DOI: 10.1117/12.2549318
PMID: 32201450
NLM abbreviation: Proc SPIE Int Soc Opt Eng
ISSN: 0277-786X
eISSN: 1996-756X
Language: English
Date published: 02/2020
Academic Unit: Preventive and Community Dentistry; Radiology; Electrical and Computer Engineering; Epidemiology; Surgery; Injury Prevention Research Center; Neurosurgery
Record Identifier: 9984198016302771

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