Human Knee Phantom for Spectral CT: Validation of a Material Decomposition Algorithm.

Osteoarthritis is the most common degenerative joint disease. Spectral computed tomography generates energy-resolved data which enable identification of materials within the sample and offer improved soft tissue contrast compared to conventional X-ray CT. In this work, we propose a realistic numerical phantom of a knee to assess the feasibility of spectral CT for osteoarthritis. The phantom is created from experimental synchrotron CT mono-energetic images. After simulating spectral CT data, we perform material decomposition using Gauss-Newton method, for different noise levels. Then, we reconstruct virtual mono-energetic images. We compare decompositions and mono-energetic images with the phantom using mean-squared error. When performing material decomposition and tomographic reconstruction, we obtain less than 1 % error for both, using noisy data. Moreover, it is possible to see cartilage with naked eye on virtual mono-energetic images. This phantom has great potential to assess the feasibility and current limitations of spectral CT to characterize knee osteoarthritis.