Bone and muscle assessment in patients undergoing total hip arthroplasty using HU based analysis - Þröstur Pétursson; Benedikt Magnússon; Benedikt Helgason; Gigja Magnúsdóttir; Grétar Halldórsson; Jan Tribel; Halldór Jónsson jr; Paolo Gargiulo (PAGEPress Publications, 2012)
Total hip arthroplasty (THA) is performed with or without the use of bone cement. The lack of reliable clinical guidelines for deciding which one to implement has encouraged this approach of joint clinical and engineering with the following objectives: 1. Validate quadriceps muscles and femur bone atrophy by extracting the mineral density from Computer Tomographic (CT) images. 2. Validate computational processes based on 3-D modeling and Finite Element Methods (FEM). A clinical trial was started, where 36 volunteer patients underwent THA surgery for the first time: 18 receiving cemented implant and 18 receiving uncemented implant. The patients were CT scanned prior-, immediately after and 12 months post-surgery. The CT data are further processed to segment muscles and bones and to create 3D-models for the simulation and for calculating bone mineral density (BMD). Furthermore quadriceps muscle density Hounsfield (HU) based value is calculated from the segmented file on healthy and operated leg. These preliminary results indicate computational tools and methods that are able to quantitatively analyse patient’s condition pre and post-surgery. The BMD and muscle density measurement in correlation with the fracture risk analysis display a potential method for eligibility to receive non-cemented implant; the preliminary results show that also elderly that according with current clinical evaluation receives a cemented implant are suitable for the non-cemented type. The risk for structural failure during THA surgery is estimated by calculating femoral bone fracture risk index (FRI) as a ratio between compressive stress during surgery and estimated failure stress on bone. The correlations with the BMD observations during the clinical trial will assess and validate this potential predictor tool.