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

International Journal of Computer Assisted Radiology and Surgery volume 17, pages 541–551 (2022)Cite this article

Abstract

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.

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Data availability

All raw data are made available in the supplementary material.

Code availability

Code will be made fully available on GitHub.

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Acknowledgements

The work of GS and JMDM is partially supported by the National Science Foundation, the Department of Defense, and gifts from Amazon Web Services, Microsoft, Google, and Cisco.

Funding

No funding was received to assist with the preparation of this manuscript.

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Authors and Affiliations

  1. University of Texas SouthwesternMedical Center Dallas, TX, 76126, USA

    Usamah N. Chaudhary

  2. restor3d, Inc., 311 West Corporation St., Durham, NC, 27701, USA

    Cambre N. Kelly, Benjamin R. Wesorick & Cameron M. Reese

  3. Pratt School of Engineering, Duke University, Durham, NC, 27701, USA

    Ken Gall

  4. Duke University Medical Center, Durham, NC, 27705, USA

    Samuel B. Adams

  5. Duke University, Durham, NC, 27705, USA

    Guillermo Sapiro & J. Matias Di Martino

Authors
  1. Usamah N. Chaudhary
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  2. Cambre N. Kelly
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  3. Benjamin R. Wesorick
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  4. Cameron M. Reese
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  5. Ken Gall
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  6. Samuel B. Adams
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  7. Guillermo Sapiro
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  8. J. Matias Di Martino
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Corresponding author

Correspondence to Usamah N. Chaudhary.

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Conflict of interest

UNC, GS, JMDM are consultants for restor3d (Durham, NC). CNK, BRW, CMR are employees of restor3d (Durham, NC). CNK, BRW, CMR, KG, SBA have stock and/or stock options in restor3d (Durham, NC).

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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Supplementary Information

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Supplementary file 1 (pdf 118 KB)

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Chaudhary, U.N., Kelly, C.N., Wesorick, B.R. et al. Computational and image processing methods for analysis and automation of anatomical alignment and joint spacing in reconstructive surgery. Int J CARS 17, 541–551 (2022). https://doi.org/10.1007/s11548-021-02548-1

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  • DOI: https://doi.org/10.1007/s11548-021-02548-1

Keywords

  • Reconstructive surgery
  • Pre-surgical planning
  • Joint spacing
  • Automatic realignment