• Ei tuloksia

Generation of a subject-specific model requires a lot of manual work and time in segmentation of soft tissues, meshing and making models to converge. In future studies, the methodology presented in this thesis should be coupled with semi-automatic or fully automatic segmentation techniques [197, 260, 261, 338–344]

and automated meshing tools [262]. In a recent automated model generation approach the only required inputs to generate the entire FE model are the anatomical dimensions from MRI or CT [195]. As motion capture systems are not readily available in clinical settings, a simple and fast method should be developed to obtain and implement patient’s gait. For instance, differences between patient-specific and population-specific (e.g. normal/early/advanced OA populations) motions could be studied [293, 345]. If the population-specific approach would produce similar results with the patient-specific method, it could be used without motion capture. These aforementioned methods would ease the applications of the FE models in large cohort studies to identify areas susceptible to OA development [37–39, 197, 239, 321]. In patients with traumatic knee joint injuries, this methodology may help identify optimal surgical or conservative rehabilitation options to delay the OA onset and progression.

9 Summary and Conclusion

In this thesis, a methodology for rapidly identifying areas prone to OA onset and progression using subject-specific computational knee joint modeling is presented.

The areas identified to have an elevated risk of cartilage degeneration were verified against patient follow-up information. The following conclusions can be drawn based on the present findings:

1. Subject-specific FE models with a simplified geometry and motion, including only cartilage menisci and ligaments, can produce similar cartilage responses as more complicated FE models, which include more tissue structures and complicated motion implementation. These models are much faster and easier to implement (Study I).

2. The FE model predictions match MRI follow-up information both qualitatively and quantitatively for both healthy controls and ACL reconstructed patients.

There is a strong relationship between the predicted maximum principal stress and the measured change inT2relaxation time (Studies IIandIII). For rapid assessment of OA risk level, excessive maximum principal stresses may be a good initial indicator for OA onset and progression at the compartment level.

3. Cartilage lesions following ACL injury and reconstruction should be included in FE models, since they may contribute to OA onset and development. Large tissue deformations around cartilage lesions and/or extensive PG leakage through damaged surfaces may lead to a decrease in FCD content (Study IV).

Subject-specific FE models with simplified geometry, motion and materials can rapidly identify patients at risk of developing OA. In addition, the effect of surgical intervention, such as ACL reconstruction, on OA progression, could be estimated.

If coupled with automatic FE model generation methods, this could be applied as a clinical tool to personalize and optimize OA treatment plans. This could delay the need for total knee replacement surgery, decreasing the financial burden on both the patient and society and increase patient quality of life.

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