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Updated: Feb 28, 2026

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
Published on: November 23, 2019
Motion-Informed, Patient-Specific Femoral Localization for MPFL Reconstruction Using 4D-CT and Constrained
Jiaying Wei1,2,3, Xinhao Zhang4, Jia Li5
1Department of Orthopedics, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China.
Abstract:
Background: Accurate femoral localization is a critical factor influencing graft length-change behavior in medial patellofemoral ligament reconstruction (MPFLR). However, the commonly used Schöttle point is derived from static radiographs and does not account for subject-specific patellofemoral kinematics during active knee motion. In this study, we integrated four-dimensional computed tomography (4D-CT) with constrained optimization to establish a motion-informed, patient-specific femoral localization framework. Methods: A total of 1382 4D-CT knee datasets were screened, and 58 knees were selected for detailed kinematic modeling. Subject-specific femoral and patellar point clouds were reconstructed from time-resolved CT data acquired during voluntary knee flexion. Within a predefined 5-15 mm neighborhood of the Schöttle point, a constrained sequential quadratic programming (SQP) approach was applied to identify an individualized femoral point (I-point) that minimized MPFL length variability while enforcing a femoral-surface constraint. Results: Compared with the Schöttle point, the I-point demonstrated a distinct spatial distribution, characterized primarily by a proximal shift along the femoral axis (PERMANOVA pseudo-F = 4.457, p = 0.006). Across 0-90° of knee flexion, the I-point was associated with reduced MPFL length variation and approached a relatively stable length-change profile near mid-flexion. Conclusions: These findings indicate that integrating 4D-CT-derived kinematics with constrained optimization can provide quantitative, imaging-based, motion-informed guidance for patient-specific femoral localization. This imaging-based framework may serve as a preoperative decision-support tool for personalized MPFLR planning.

