Measuring Bone Healing
Describes the application of my fast (GPU-accelerated) radiological image processing to extract descriptors of bone tissue healing. I developed the software in C++ using CUDA and OpenGL. Full details of the study are in the article (below) that was featured on the cover of the scientific journal Veterinary Comparative Orthopaedic Traumatology.
Qualitative & Quantitative Radiological Measures of Fracture Healing
Authors: J.R. Field and G.S. Ruthenbeck.
Abstract:
The formulation of appropriate postoperative strategies, following fracture repair, currently involves an understanding of radiological and clinical outcome measures. This study has evaluated several modalities used to assess the progression of bone healing in a sheep tibial segmental defect model. Measures of defect optical density and volumetric data including bone density (BD), bone volume (BV) and bone mass (BM) were compared with qualitative data involving visual appraisal of radiographs [% bridging callus and modified radiographic union score tibia (mRUST)] and a clinical outcome measure (locomotory function). Percent bridging callus and mRUST measures displayed strong correlation (r = 0.999), while locomotory function was weakly correlated with bridging callus (r = 0.029) and mRUST (r = 0.046). There was moderate to strong correlation between the qualitative and quantitative data. Bone density, BV and BM showed strong correlations within this dataset (BD-BV, r = 0.814; BD-BM, r = 0.818; BV-BM, r = 1.000). Likewise, optical density measures were strongly correlated with BD (r = 0.824), BV (r = 0.957) and BM (r = 0.959). The utilization of both qualitative and quantitative data, in assessment of the progression of fracture healing, has provided valuable insight. Measures of optical density have been shown to make a substantial contribution to this assessment and which should be considered for use in studies evaluating fracture healing.
This article was featured on the cover of the journal (January, 2018).