Multi-row detector computed tomography (MDCT) based generalizable tools for bone microstructure and strength analysis and their applications
Creators: name: Guha, Indranil
Publisher: name: University of Iowa
Year: 2023
Subjects: Finite element analysis (FEA)
GAN-CIRCLE
MDCT Image Harmonization
Nonlinear FEA
trabecular bone strength
public abstract
Osteoporosis
Biomechanics

description: Osteoporosis is an age-related bone disease associated with reduced bone mineral density (BMD) and microstructural degeneration leading to increased fragility and greater fracture-risk. Dual-energy X-ray absorptiometry (DXA) computed BMD is the clinical standard for diagnosis of osteoporosis. However, BMD explains only 60-70% variability in bone strength and fracture-risk and the remaining variability is characterized by trabecular bone (Tb) strength and its microstructural basis. Recently, emerging whole-body multi-row detector CT (MDCT) has drawn major attention as a viable modality for in vivo bone microstructural imaging at peripheral and central skeletal sites due to its fast scan speed, larger field-of-view, ability to provide quantitative BMD, and wide availability in clinical set up. Although high-resolution features of MDCT scanners allow visualization of Tb microstructure, its low spatial resolution results in partial volume errors causing smaller trabeculae to be artifactually broken following segmentation. This makes computation of bone microstructural and strength measures from MDCT scans erroneous. Furthermore, differences in resolution and noise features among different MDCT scanners contribute to artifactual variabilities in values of Tb microstructural measures derived from different scanners which poses major challenge in multi-site study design.
description: New algorithms have been developed for accurate and consistent assessment of Tb microstructural quality and strength. A generative adversarial network (GAN)-based deep learning (DL) method is developed and validated for harmonization of scans from low-resolution and high-resolution MDCT scanners. The DL network not only improves the structural quality of the low-resolution images but also increases the agreement (CCC ∈ [0.95 0.99]) between Tb microstructural measures derived from low- and high-resolution scans. Also, a nonlinear continuum finite element analysis (FEA) method is developed and validated for MDCT-based computation of Tb strength without requiring segmentation of Tb micro-network, while characterizing mechanical stress-strain behavior at the microstructural level. Tb strength estimated using the proposed FEA method strongly correlates (r ∈ [0.86 0.90]) with micro-CT and experimentally derived reference values and also shows very high repeat scan as well as inter-scanner reproducibility (ICC ∈ [0.93 0.98]).
description: The methods developed in this thesis provide a novel quantitative imaging tools for assessment of bone microstructural quality and strength at relatively low-resolution imaging. The major contributions of this thesis include: (1) development of a GAN-based DL network for high-resolution reconstruction of Tb images from low-resolution MDCT scans; (2) evaluation of the agreement between Tb measures derived from DL-predicted and true high-resolution images; (3) development of an MDCT-based nonlinear FEA method for computation of compressive and shear modulus of Tb; (4) evaluation of the accuracy of MDCT derived modulus measures with micro-CT and experimentally derived reference measures and also examining their inter-scanner reproducibility; (5) evaluation of the relationships of different modulus measures with sex, body size, and composition parameters.