Computational and image processing methods for analysis and automation of anatomical alignment and joint spacing in reconstructive surgery.

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Chaudhary, UN; Kelly, CN; Wesorick, BR; Reese, CM; Gall, K; Adams, SB; Sapiro, G; Di Martino, JM

Published Date

  • March 2022

Published In

Volume / Issue

  • 17 / 3

Start / End Page

  • 541 - 551

PubMed ID

  • 35099684

Electronic International Standard Serial Number (EISSN)

  • 1861-6429

Digital Object Identifier (DOI)

  • 10.1007/s11548-021-02548-1

Language

  • eng

Conference Location

  • Germany