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During functional activities, patients with isolated PCL injuries experience significant knee instability that cannot be identified by standard nonweightbearing static laxity measurements. The finding that different activities create different degrees of instability may have important implications for rehabilitation and activity limitations for PCL-deficient individuals.

Quantitative knowledge of the distal femur morphology is critical to understanding the relation between the anatomy and function of the knee joint. Prior knowledge was contaminated by manual procedures and subjective visual inspections in extracting geometric information from image data. This article proposes a new computational framework to enable automated analysis of the distal femur articular geometry based on 3D surface data. The framework consists of a pattern recognition algorithm for sectioning the sagittal-view condyle profiles, a least-squares algorithm for fitting and analyzing the profiles, and an optimization algorithm for establishing a unified sagittal plane. An application of the proposed framework to 12 knee surface models demonstrated that it can analyze the condyle contour profiles and extract geometric measures automatically and accurately. The proposed framework also facilitated a simulation-based analysis of the uncertainty associated with conventional manual approaches, elucidating how subjective determination of the sagittal plane and flexion facet can hinder accurate understanding of the distal femur morphology and related kinematics.

We elucidated the gender and condylar effects on distal femur morphology (DFM) while evaluating a newly developed computational framework that enables fully automated analyses of DFM in an objectively defined sagittal plane. Ninety high-resolution CT-acquired distal femur models from 51 males and 39 females were analyzed. The models were accurately characterized (mean least-squares fitting residual < 0.16 mm), and re-oriented to a unified sagittal plane; three morphometric measures were extracted from each model: the semi-major (a) and semi-minor (b) axis lengths of the best-fitted ellipse, and the radius (r) of the smallest flexion facet—a circle with the smallest radius best-fitted to the posterior articulating surface. Statistical analyses employing non-parametric repeated-measures ANOVA found: no significance difference between condyles or between limbs in any of the morphometric measures; significant gender effects on a, b, and r, but no gender effect on the aspect ratio (a/b). An inspection of statistical distributions of medial-lateral condyle size differences also revealed a gender difference. The findings promote a better understanding of DFM and its relation to knee mechanics and have implications on computer-aided surgery of the knee and gender-specific implant design.