Related Experiment Video
Updated: Oct 5, 2025
Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
Published on: November 23, 2019
Computational and image processing methods for analysis and automation of anatomical alignment and joint spacing in
Usamah N Chaudhary1, Cambre N Kelly2, Benjamin R Wesorick2
1University of Texas SouthwesternMedical Center Dallas, TX, 76126, USA. usamah.chaudhary@utsouthwestern.edu.
Purpose:
Reconstructive surgeries to treat a number of musculoskeletal conditions, from arthritis to severe trauma, involve implant placement and reconstructive planning components. Anatomically matched 3D-printed implants are becoming increasingly patient-specific; however, the preoperative planning and design process requires several hours of manual effort from highly trained engineers and clinicians. Our work mitigates this problem by proposing algorithms for the automatic re-alignment of unhealthy anatomies, leading to more efficient, affordable, and scalable treatment solutions.
Methods:
Our solution combines global alignment techniques such as iterative closest points with novel joint space refinement algorithms. The latter is achieved by a low-dimensional characterization of the joint space, computed from the distribution of the distance between adjacent points in a joint.
Results:
Experimental validation is presented on real clinical data from human subjects. Compared with ground truth healthy anatomies, our algorithms can reduce misalignment errors by 22% in translation and 19% in rotation for the full foot-and-ankle and 37% in translation and 39% in rotation for the hindfoot only, achieving a performance comparable to expert technicians.
Conclusion:
Our methods and histogram-based metric allow for automatic and unsupervised alignment of anatomies along with techniques for global alignment of complex arrangements such as the foot-and-ankle system, a major step toward a fully automated and data-driven re-positioning, designing, and diagnosing tool.
