Image Registration in Longitudinal Bone Assessment Using Computed Tomography

• Published in: Current osteoporosis reports 2023-08, Vol.21 (4), p.372-385
• Main Authors: Liu, Han, Durongbhan, Pholpat, Davey, Catherine E., Stok, Kathryn S.
• Format: Article
• Language: English
• Subjects: Algorithms; Bone and Bones - diagnostic imaging; Epidemiology; Humans; Image Processing, Computer-Assisted; Medicine; Medicine & Public Health; Musculoskeletal Diseases; Orthopedics; Tomography, X-Ray Computed; Topical Collection on Imaging
• ISSN: 1544-1873; 1544-2241; 1544-2241
• DOI: 10.1007/s11914-023-00795-6

Purpose of Review Rigid image registration is an important image processing tool for the assessment of musculoskeletal chronic disease. In this paper, we critically review applications of rigid image registration in terms of similarity measurement methods over the past three years (2019–2022) in the context of monitoring longitudinal changes to bone microstructure and mechanical properties using computed tomography. This review identifies critical assumptions and trade-offs underlying different similarity measurement methods used in image registration and demonstrates the effect of using different similarity measures on registration outcomes. Recent Findings Image registration has been used in recent studies for: correcting positional shifts between longitudinal scans to quantify changes to bone microstructural and mechanical properties over time, developing registration-based workflows for longitudinal assessment of bone properties in pre-clinical and clinical studies, and developing and validating registration techniques for longitudinal studies. Summary In evaluating the recent literature, it was found that the assumptions at the root of different similarity measures used in rigid image registration are not always confirmed and reported. Each similarity measurement has its advantages and disadvantages, as well as underlying assumptions. Breaking these assumptions can lead to poor and inaccurate registration results. Thus, care must be taken with regards to the choice of similarity measurement and interpretation of results. We propose that understanding and verifying the assumptions of similarity measurements will enable more accurate and efficient quantitative assessments of structural changes over time.