Formation Dominates Resorption With Increasing Mineralized Density and Time Postfracture in Cortical but Not Trabecular Bone: A Longitudinal HRpQCT Imaging Study in the Distal Radius

Clinical evaluation of fracture healing is often limited to an assessment of fracture bridging from radiographic images, without consideration for other aspects of bone quality. However, recent advances in HRpQCT offer methods to accurately monitor microstructural bone remodeling throughout the healing process. In this study, local bone formation and resorption were investigated during the first year post fracture in both the fractured (n = 22) and contralateral (n = 19) radii of 34 conservatively treated patients (24 female, 10 male) who presented with a unilateral radius fracture at the Innsbruck University Hospital, Austria. HRpQCT images and clinical metrics were acquired at six time points for each patient. The standard HRpQCT image acquisition was captured for all radii, with additional distal and proximal image acquisitions for the fractured radii. Measured radial bone densities were isolated with a voxel‐based mask and images were rigidly registered to images from the previous imaging session using a pyramid‐based approach. From the registered images, bone formation and resorption volume fractions were quantified for multiple density‐based thresholds and compared between the fractured and contralateral radius and relative to demographics, bone morphometrics, and fracture metrics using regression. Compared with the contralateral radius, both bone formation and resorption were significantly increased in the fractured radius throughout the study for nearly all evaluated thresholds. Higher density cortical bone formation continually increased throughout the duration of the study and was significantly greater than resorption during late‐stage healing in both the fractured and intact regions of the radius. With the small and diverse study population, only weak relationships between fracture remodeling and patient‐specific parameters were unveiled. However this study provides methods for the analysis of local bone remodeling during fracture healing and highlights relevant considerations for future studies, specifically that remodeling postfracture is likely to continue beyond 12‐months postfracture. © 2021 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

[1]  Penny R. Atkins,et al.  Automated segmentation of fractured distal radii by 3D geodesic active contouring of in vivo HR-pQCT images. , 2021, Bone.

[2]  S. Boyd,et al.  Sex‐ and Site‐Specific Reference Data for Bone Microarchitecture in Adults Measured Using Second‐Generation HR‐pQCT , 2020, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.

[3]  Caitlyn J. Collins,et al.  Automated Segmentation of Fractured Distal Radii by 3D Geodesic Active Contouring of in vivo HR-pQCT Images , 2020, bioRxiv.

[4]  L. Gabel,et al.  Longitudinal bone microarchitectural changes are best detected using image registration , 2020, Osteoporosis International.

[5]  M. Osaki,et al.  Analysis of fracture healing process by HR-pQCT in patients with distal radius fracture , 2020, Journal of Bone and Mineral Metabolism.

[6]  Michael T. Kuczynski,et al.  The utility of multi-stack alignment and 3D longitudinal image registration to assess bone remodeling in rheumatoid arthritis patients from second generation HR-pQCT scans , 2019, BMC Medical Imaging.

[7]  S. Hofmann,et al.  The association between mineralised tissue formation and the mechanical local in vivo environment: Time-lapsed quantification of a mouse defect healing model , 2019, Scientific Reports.

[8]  Johannes L. Schönberger,et al.  SciPy 1.0: fundamental algorithms for scientific computing in Python , 2019, Nature Methods.

[9]  S. Boyd,et al.  The Correction of Systematic Error due to Plaster and Fiberglass Casts on HR-pQCT Bone Parameters Measured In Vivo at the Distal Radius. , 2019, Journal of clinical densitometry.

[10]  M. Maumy-Bertrand,et al.  Determining the number of components in PLS regression on incomplete data set , 2018, Statistical applications in genetics and molecular biology.

[11]  B. van Rietbergen,et al.  Least-detectable and age-related local in vivo bone remodelling assessed by time-lapse HR-pQCT , 2018, PloS one.

[12]  H. Dimai,et al.  Use of dual-energy X-ray absorptiometry (DXA) for diagnosis and fracture risk assessment; WHO-criteria, T- and Z-score, and reference databases. , 2017, Bone.

[13]  Ralph Müller,et al.  Feasibility of rigid 3D image registration of high-resolution peripheral quantitative computed tomography images of healing distal radius fractures , 2017, PloS one.

[14]  P. Divall,et al.  Risk of hip fracture following a wrist fracture-A meta-analysis. , 2017, Injury.

[15]  P. Geusens,et al.  Fracture Repair in the Distal Radius in Postmenopausal Women: A Follow‐Up 2 Years Postfracture Using HRpQCT , 2016, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.

[16]  P. Geusens,et al.  Effect of a Cast on Short-Term Reproducibility and Bone Parameters Obtained from HR-pQCT Measurements at the Distal End of the Radius. , 2016, The Journal of bone and joint surgery. American volume.

[17]  A. Bhardwaj,et al.  Factors Associated with One-Year Outcome after Distal Radial Fracture Treatment , 2015, Journal of orthopaedic surgery.

[18]  J. Cowie,et al.  Factors Associated with One-Year Outcome after Distal Radial Fracture Treatment , 2015, Journal of orthopaedic surgery.

[19]  B. van Rietbergen,et al.  Bone remodelling in humans is load-driven but not lazy , 2014, Nature Communications.

[20]  B. van Rietbergen,et al.  Early Changes in Bone Density, Microarchitecture, Bone Resorption, and Inflammation Predict the Clinical Outcome 12 Weeks After Conservatively Treated Distal Radius Fractures: An Exploratory Study , 2014, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.

[21]  B. van Rietbergen,et al.  Assessment of the healing process in distal radius fractures by high resolution peripheral quantitative computed tomography. , 2014, Bone.

[22]  B. van Rietbergen,et al.  Challenges in longitudinal measurements with HR-pQCT: evaluation of a 3D registration method to improve bone microarchitecture and strength measurement reproducibility. , 2014, Bone.

[23]  J. Eisman,et al.  Progressively increasing fracture risk with advancing age after initial incident fragility fracture: The Tromsø Study , 2013, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.

[24]  J. Kanis,et al.  Standardized nomenclature, symbols, and units for bone histomorphometry: A 2012 update of the report of the ASBMR Histomorphometry Nomenclature Committee , 2013, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.

[25]  Anna-Maria Liphardt,et al.  Quality control for bone quality parameters affected by subject motion in high-resolution peripheral quantitative computed tomography. , 2012, Bone.

[26]  Ralph Müller,et al.  In vivo micro-computed tomography allows direct three-dimensional quantification of both bone formation and bone resorption parameters using time-lapsed imaging. , 2011, Bone.

[27]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[28]  Aaron Schindeler,et al.  Bone remodeling during fracture repair: The cellular picture. , 2008, Seminars in cell & developmental biology.

[29]  J. Macdermid,et al.  Baseline Predictors of Pain and Disability One Year Following Extra-Articular Distal Radius Fractures , 2007, Hand.

[30]  C. Court-Brown,et al.  Epidemiology of adult fractures: A review. , 2006, Injury.

[31]  J. Macdermid,et al.  Pain and disability reported in the year following a distal radius fracture: A cohort study , 2003, BMC musculoskeletal disorders.

[32]  A. Parfitt Misconceptions (2): turnover is always higher in cancellous than in cortical bone. , 2002, Bone.

[33]  M. Bouxsein,et al.  Age-related differences in cross-sectional geometry of the forearm bones in healthy women , 1994, Calcified Tissue International.

[34]  M. Drezner,et al.  Bone histomorphometry: Standardization of nomenclature, symbols, and units: Report of the asbmr histomorphometry nomenclature committee , 1987, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.

[35]  D. Toher,et al.  Parametric and Non-Parametric Tests for the Comparison of Two Samples Which Both Include Paired and Unpaired Observations , 2018 .

[36]  P. Geusens,et al.  Feasibility of rigid 3 D image registration of high-resolution peripheral quantitative computed tomography images of healing distal radius fractures , 2017 .

[37]  JB. Pialat,et al.  Visual grading of motion induced image degradation in high resolution peripheral computed tomography: impact of image quality on measures of bone density and micro-architecture. , 2012, Bone.

[38]  Skipper Seabold,et al.  Statsmodels: Econometric and Statistical Modeling with Python , 2010, SciPy.

[39]  Michael Unser,et al.  A pyramid approach to subpixel registration based on intensity , 1998, IEEE Trans. Image Process..