Standardizing ACL tunnel placement: an automated method for knee quadrant computation

Area of Science:

• Orthopedic surgery
• Medical imaging analysis
• Computational anatomy

Background:

• Accurate anatomical tunnel placement in anterior cruciate ligament (ACL) reconstruction is critical for optimal functional recovery and minimizing complications.
• Current methods for quantifying ACL position on 3D models are often subjective and lack efficiency, hindering precise surgical planning and evaluation.

Purpose of the Study:

• To develop and validate a fully automated framework for standardized 3D quadrant coordinate computation of femoral and tibial ACL footprints.
• To enable objective preoperative planning and postoperative evaluation in ACL reconstruction surgery.

Main Methods:

• Utilized an nnUNet-based network to reconstruct 3D femur and tibia models from CT or MRI data.
• Employed automated template registration and morphological analysis for anatomical plane determination and individualized quadrant coordinate system generation.
• Validated the pipeline on CT and MRI datasets, comparing accuracy, repeatability, and time efficiency against manual methods.

Main Results:

• The automated method demonstrated high accuracy with 3D centroid distances of 1.72 ± 0.94 mm (femur) and 1.47 ± 1.06 mm (tibia), comparable to manual methods.
• Achieved excellent repeatability (0.992) in quadrant calculations, surpassing manual annotation consistency (ICCs 0.961-0.882).
• Significantly reduced processing time to an average of 4.7 ± 1.3 seconds, compared to 8.5 ± 2.1 minutes for manual annotation.

Conclusions:

• Presented the first fully automated, modality-independent method for 3D quadrant coordinate computation in knee surgery.
• The framework provides robust and standardized ACL anatomical locations across CT and MRI data.
• Enhances clinical efficiency for preoperative planning and postoperative assessment in ACL reconstruction.