of the leg, and thus the mechanical stresses, from the diseased portion to the healthy

section of the knee compartment. By transferring the location of mechanical stresses, the

degenerative disease process may be slowed or possibly reversed. The advantages of

HTO are appealing to younger and active patients who receive recommendations to avoid

TKR.

 Need for Accurate Patient-Specific Models

 Innovative patient-specific models and simulations would be valuable for

addressing problems in orthopedics and sports medicine, as well as for evaluating and

enhancing corrective surgical procedures (Arnold et al., 2000; Arnold and Delp, 2001;

Chao et al., 1993; Chao and Sim, 1995; Delp et al., 1998; Delp et al., 1996; Delp et al.,

1990; Pandy, 2001). For example, a patient-specific dynamic model may be useful for

planning intended surgical parameters and predicting the outcome of HTO.

 The main motivation for developing a patient-specific computational model and a

two-level optimization method to enhance the lower-extremity portion is to predict the

post-surgery peak knee adduction moment in HTO patients. Conventional surgical

planning techniques for HTO involve choosing the amount of necessary tibial angulation

from standing radiographs (or x-rays). Unfortunately, alignment correction estimates

from static x-rays do not accurately predict long-term clinical outcome after HTO

(Andriacchi, 1994; Tetsworth and Paley, 1994). Researchers have identified the peak

external knee adduction moment as an indicator of clinical outcome while investigating

the gait of HTO patients (Andriacchi, 1994; Bryan et al., 1997; Hurwitz et al., 1998;

Prodromos et al., 1985; Wang et al., 1990). Currently, no movement simulations (or

other methods for that matter) allow surgeons to choose HTO surgical parameters to

achieve a chosen post-surgery knee adduction moment.