title: A new algorithm to improve assessment of cortical bone geometry in pQCT.
abstract: High-resolution peripheral quantitative computed tomography (HR-pQCT) is now considered the leading imaging modality in bone research. However, access to HR-pQCT is limited and image acquisition is mainly constrained only for the distal third of appendicular bones. Hence, the conventional pQCT is still commonly used despite inaccurate threshold-based segmentation of cortical bone that can compromise the assessment of whole bone strength. Therefore, this study addressed whether the use of an advanced image processing algorithm, called OBS, can enhance the cortical bone analysis in pQCT images and provide similar information to HR-pQCT when the same volumes of interest are analyzed. Using pQCT images of European Forearm Phantom (EFP), and pQCT and HR-pQCT images of the distal tibia from 15 cadavers, we compared the results from the OBS algorithm with those obtained from common pQCT analyses, HR-pQCT manual analysis (considered as a gold standard) and common HR-pQCT analysis dual threshold technique.We found that the use of OBS segmentation method for pQCT image analysis of EFP data did not result in any improvement but reached similar performance in cortical bone delineation as did HR-pQCT image analyses. The assessments of cortical cross-sectional bone area and thickness by OBS algorithm were overestimated by less than 4% while area moments of inertia were overestimated by ~5–10%, depending on reference HR-pQCT analysis method. In conclusion, this study showed that the OBS algorithm performed reasonably well and it offers a promising practical tool to enhance the assessment of cortical bone geometry in pQCT.
authors.affiliation: Department of Electronics and Communications Engineering, Tampere University of Technology, Korkeakoulunkatu 3, 33720 Tampere, Finland; Institute of Bioscience and Medical Technology (BioMediTech), Tampere, Finland. Electronic address: tomas.cervinka@hotmail.com.
authors.forename: Tomas
authors.initials: T
authors.lastname: Cervinka
authors.affiliation: Bone Research Group, UKK Institute, Kaupinpuistonkatu 1, 33500 Tampere, Finland. Electronic address: harri.sievanen@uta.fi.
authors.forename: Harri
authors.initials: H
authors.lastname: Sievänen
authors.affiliation: Department of Health and Rehabilitation Sciences, Western University, London, Canada. Electronic address: dlala@uwo.ca.
authors.forename: Deena
authors.initials: D
authors.lastname: Lala
authors.affiliation: Centre of Excellence in Skeletal Health Assessment, University of Toronto, Toronto, Canada. Electronic address: angela.m.cheung@gmail.com.
authors.forename: Angela M
authors.initials: AM
authors.lastname: Cheung
authors.affiliation: Department of Kinesiology, University of Waterloo, Waterloo, Canada. Electronic address: lora.giangregorio@uwaterloo.ca.
authors.forename: Lora
authors.initials: L
authors.lastname: Giangregorio
authors.affiliation: Department of Electronics and Communications Engineering, Tampere University of Technology, Korkeakoulunkatu 3, 33720 Tampere, Finland; Institute of Bioscience and Medical Technology (BioMediTech), Tampere, Finland. Electronic address: jari.hyttinen@tut.fi.
authors.forename: Jari
authors.initials: J
authors.lastname: Hyttinen
mesh_terms: D000465:Algorithms; D001842:Bone and Bones / Q000000981:diagnostic imaging*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods / Q000706:statistics & numerical data; D019047:Phantoms, Imaging; D011856:Radiographic Image Enhancement / Q000379:methods; D014057:Tomography, X-Ray Computed / Q000379:methods* / Q000706:statistics & numerical data
keywords: Bone strength; Cortical bone; HR-pQCT; Segmentation; pQCT