Osteophyte volume calculation using dissimilarity‐excluding Procrustes registration of archived bone models from healthy volunteers

• Published in: Journal of orthopaedic research Vol. 38; no. 6; pp. 1307 - 1315
• Main Authors: Morton, Amy M., Akhbari, Bardiya, Moore, Douglas C., Crisco, Joseph J.
• Format: Journal Article
• Language: English
• Published: United States 01.06.2020
• Subjects: carpometacarpal; morphology; osteophyte; registration; superimposition; osteophyte; registration; carpometacarpal; superimposition; morphology
• ISSN: 0736-0266; 1554-527X; 1554-527X
• DOI: 10.1002/jor.24569

Osteophytes are associated with later stage osteoarthritis and are most commonly described using semiquantitative radiographic grading systems. A detailed understanding of osteophyte formation is, in part, limited by the ability to quantify bone pathology. Osteophytes can be quantified relative to pre‐osteoarthritic bone, or to the contralateral bone if it is healthy; however, in many cases, neither are available as references. We present a method for computing three‐dimensional (3D) osteophyte models using a library of healthy control bones. An existing data set containing the computed tomography scans of 90 patients with first carpometacarpal osteoarthritis (OA) and 46 healthy subjects were utilized. A healthy bone that best fit each OA subject's bone was determined using a dissimilarity‐excluding Procrustes registration technique (DEP) that minimized the influence of dissimilar features (ie, osteophytes). The osteophyte model was then computed through Boolean subtraction of the reference bone model from the OA bone model. DEP reference bones conformed significantly better to the OA bones (P < .0001) than by finite difference iterative closest point registration (root mean squared distances, 0.33 ± 0.05 and 0.41 ± 0.16 mm, respectively). The effect of library size on dissimilarity measure was investigated by leave‐k‐out cross‐validation randomly reducing k from 46 to 1. A library of n ≥ 31 resulted in less than 10% difference from the theoretical minimum value. The proposed method enables quantification of osteophytes when the disease‐free bone or the healthy contralateral bone is not available for any 3D data set. Quantifying osteophyte formation and growth may aid in understating the associated mechanisms in OA.