Personalized statistical modeling of soft tissue structures in the knee

Abstract

Background and Objective: As in vivo measurements of knee joint contact forces remain challenging, computational musculoskeletal modeling has been popularized as an encouraging solution for non-invasive estimation of joint mechanical loading. Computational musculoskeletal modeling typically relies on laborious manual segmentation as it requires reliable osseous and soft tissue geometry. To improve on feasibility and accuracy of patient-specific geometry predictions, a generic computational approach that can easily be scaled, morphed and fitted to patient-specific knee joint anatomy is presented.Methods: A personalized prediction algorithm was established to derive soft tissue geometry of the knee, originating solely from skeletal anatomy. Based on a MRI dataset (n = 53), manual identification of soft-tissue anatomy and landmarks served as input for our model by use of geometric morphometrics. Topographic distance maps were generated for cartilage thickness predictions. Meniscal modeling relied on wrapping a triangular geometry with varying height and width from the anterior to the posterior root. Elastic mesh wrapping was applied for ligamentous and patellar tendon path modeling. Leave-one-out validation experiments were conducted for accuracy assessment.Results: The Root Mean Square Error (RMSE) for the cartilage layers of the medial tibial plateau, the lateral tibial plateau, the femur and the patella equaled respectively 0.32 mm (range 0.14–0.48), 0.35 mm (range 0.16–0.53), 0.39 mm (range 0.15–0.80) and 0.75 mm (range 0.16–1.11). Similarly, the RMSE equaled respectively 1.16 mm (range 0.99–1.59), 0.91 mm (0.75–1.33), 2.93 mm (range 1.85–4.66) and 2.04 mm (1.88–3.29), calculated over the course of the anterior cruciate ligament, posterior cruciate ligament, the medial and the lateral meniscus.Conclusion: A methodological workflow is presented for patient-specific, morphological knee joint modeling that avoids laborious segmentation. By allowing to accurately predict personalized geometry this method has the potential for generating large (virtual) sample sizes applicable for biomechanical research and improving personalized, computer-assisted medicine.

The relation between meniscal dynamics and tibiofemoral kinematics

“…For every subject and dedicated scan, manual segmentation of both the medial and lateral meniscus was performed using the Mimics ® software 16 . The segmentation process involved meticulous determination of the meniscal edges, aided by the built-in Livewire function.…”

“…In this method, the mean cartilage thickness was scaled based on the length of the femur and then applied to the corresponding articular surface to achieve subject-specific predictions. By establishing correspondence between the osseous meshes, the localization of the cartilage layer could be generalized to any new shape 16 .…”

“…Different triangles were then formed from the initial tube to represent the meniscal shape. In the final optimization step, the edges of triangles penetrating cartilage or bone were adjusted to accurately reflect the femoral condyle geometry 16 .…”

“…Furthermore, elastic deformation of the geometric morphometric model ensured the preservation of anatomical correspondence. The latter is defined as the ability to maintain the spatial relationship between corresponding anatomical structures across different poses or deformations 16 . In Fig.…”

“…To address these limitations, we have incorporated the latest advancements in 3D technology and computational modeling to develop a personalized and dynamic morphological knee model, inspired by the work of Van Oevelen and colleagues. In this model, the meniscal geometry is represented as a deformable, volume-preserving mesh that conforms to the underlying osseous morphology 16 . By utilizing this approach, we can bypass the time-consuming manual segmentation process and obtain a straightforward three-dimensional analysis of the menisci.…”