Automated quantitative analysis of peri-articular bone microarchitecture in HR-pQCT knee images

There is growing interest in applying HR-pQCT to image the knee, particularly in the study of osteoarthritis, which necessitates the development and validation of novel image analysis workflows. In this work, we present and validate the first fully automated workflow for in vivo quantitative assessment of peri-articular bone density and microarchitecture in the human knee. Bone segmentation models were trained by transfer learning with a large dataset of radius and tibia images (N=2,598) and fine-tuned on a knee image dataset (N=131), atlas-based registration was used to identify medial and lateral contact surfaces, and morphological operations combined these intermediate outputs to generate peri-articular regions of interest (ROIs) for morphological analysis. Accuracy was assessed with an external validation dataset (N=131), where predicted and reference morphological parameters showed excellent correspondence (0.86 ≤ R2 ≤ 0.99), with moderate bias present in predictions of subchondral bone plate density (-80 mg HA/cm3) and thickness (+0.15 mm). Precision was assessed with a triple-repeat measures dataset (N=29), where the short-term precision RMS%CV estimates ranged from 0.7% to 3.5% when rigid registration was used to synchronize ROI generation across images. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was supported financially by a grant from the Canadian Institutes of Health Research (CIHR) [PJT 162189] and a Training Graduate PhD Salary Award from Arthritis Society Canada [TGP-21-0000000093]. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Conjoint Health Research Ethics Board of The University of Calgary gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The raw image data used in this study are not publicly available.