Total knee replacement (TKR) surgery is routinely prescribed for patients with severe knee osteoarthritis to alleviate the pain and restore the kinematics. Although this procedure was proven to be successful in reducing the joint pain, the number of failures and the low patients’ satisfaction suggest that while the number of reoperations is small, the surgery frequently fail to restore the function in full. The main cause are surgical techniques which inadequately address the problem of balancing the knee soft tissues.
The preoperative planning technique allows to manufacture subject-specific cutting guides that improves the placement of the prosthesis, however the knee soft tissue is ignored.
The objective of this dissertation was to create an optimized preplanning procedure to compute the soft tissue balance along with the placement of the prosthesis to ensure mechanical stability.
The dissertation comprises the development of CT based static and quasi-static knee models able to estimate the postoperative length of the collateral lateral ligaments using a dataset of seven TKR patients; In addition, a subject-specific dynamic musculoskeletal model of the lower limb was created using in vivo knee contact forces to perform the same analysis during walking. The models were evaluated by their ability to predict the postoperative elongation using a threshold based on the 10 % of the preoperative length, through which the model detected whether an elongation was acceptable.
The results showed that the subject-specific static model is the best solution to be included in the optimized, subject-specific, preoperative planning framework; full order musculoskeletal model allowed to estimate the postoperative length of the ligaments during walking, and at least in principle while performing any other activity.
Unlike the current methodology used in clinic this optimized preoperative planning framework might help the surgeon to understand how the position of the TKR affects the knee soft tissue
