667-04-011 ISMRM Abstract

Combined Shape Modeling Approaches for Patellofemoral Instability

Primary: Musculoskeletal - Other Musculoskeletal

Secondary: Analysis Methods - Classification and Prediction

667-04-011 · Novel Visualization and Assessment of Osseous Tissues · Thursday, 14 May, 2:35 PM–3:30 PM · Digital Posters Row H

Keywords: Knee disorder Computational modeling Bone shape

Accepted

James Peters ¹, Mingrui Yang^2,3, Richard Lartey^2,3,4, Carl Winalski^2,3,5,6, John J Elias, Xiaojuan Li^2,3,4,5,6

¹Biomedical Engineering, Cleveland Clinic, Cleveland, United States of America

²Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, United States of America

³Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, United States of America

⁴Department of Biomedical Engineering, Cleveland Clinic Research, Cleveland Clinic, Cleveland, United States of America

⁵Cleveland Clinic, Cleveland, United States of America

⁶Department of Diagnostic Radiology, Imaging Institute, Cleveland Clinic, Cleveland, United States of America

Presenting Author: James Peters

Synopsis

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References

1. Post & Fithian. Patellofemoral Instability A Consensus Statement From the AOSSM/PFF Patellofemoral Instability Workshop. Orthop J Sports Med. 2018 Jan;6(1):2325967117750352

2. Jimenez et al. Relationship Between Patellar Morphology and Known Anatomic Risk Factors for Patellofemoral Instability. Orthop J Sports Med. 2021 Mar;9(3):2325967120988690

3. Yang et al. 3D Patella and Femur Bone Shape Modelling for Patella Instability Patients. Osteoarthr. Cartil. 2020;28:S279-S280

4. Nagawa et al. Three‑dimensional magnetic resonance imaging‑based statistical shape analysis and machine learning‑based prediction of patellofemoral instability. Sci Rep. 2024 May;14(1):11390

5. Myronenko A, Song X. Point set registration: coherent point drift. IEEE Trans. Pattern Anal. Mach. Intell. 2010 Dec;32(12):2262-75

6. Jolliffe & Cadima. Principal component analysis: a review and recent developments. Phil. Trans. R. Soc. 2016;A.37420150202

7. Rohlf JF, Slice D. Extensions of the Procrustes Method for the Optimal Superimposition of Landmarks. Syst Zoo. 1990 Mar;39(1):40-59

8. Dowson & Landau. The Fréchet distance between multivariate normal distributions. JMVA. 1982 Sept;12(3):450-455

9. Dryden & Mardia. (2016). Statistical Shape Analysis, with Applications in R. Wiley

10. Bowes MA et al. Machine-learning, MRI bone shape and important clinical outcomes in osteoarthritis: data from the Osteoarthritis Initiative. Ann Rheum Dis. 2021;80:502–508

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/667-04-011