MRI vs CT-based 2D-3D auto-registration accuracy for quantifying shoulder motion using biplane video-radiography

Mohsen Akbari-Shandiz, Rebekah L. Lawrence, Arin M Ellingson, Casey P Johnson, Kristin D. Zhao, Paula M Ludewig

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Biplane 2D-3D registration approaches have been used for measuring 3D, in vivo glenohumeral (GH) joint kinematics. Computed tomography (CT) has become the gold standard for reconstructing 3D bone models, as it provides high geometric accuracy and similar tissue contrast to video-radiography. Alternatively, magnetic resonance imaging (MRI) would not expose subjects to radiation and provides the ability to add cartilage and other soft tissues to the models. However, the accuracy of MRI-based 2D-3D registration for quantifying glenohumeral kinematics is unknown. We developed an automatic 2D-3D registration program that works with both CT- and MRI-based image volumes for quantifying joint motions. The purpose of this study was to use the proposed 2D-3D auto-registration algorithm to describe the humerus and scapula tracking accuracy of CT- and MRI-based registration relative to radiostereometric analysis (RSA) during dynamic biplanar video-radiography. The GH kinematic accuracy (RMS error) was 0.6–1.0 mm and 0.6–2.2° for the CT-based registration and 1.4–2.2 mm and 1.2–2.6° for MRI-based registration. Higher kinematic accuracy of CT-based registration was expected as MRI provides lower spatial resolution and bone contrast as compared to CT and suffers from spatial distortions. However, the MRI-based registration is within an acceptable accuracy for many clinical research questions.

Original languageEnglish (US)
Pages (from-to)375-380
Number of pages6
JournalJournal of Biomechanics
Volume82
DOIs
StatePublished - Jan 3 2019

Bibliographical note

Funding Information:
We gratefully acknowledge the generosity of the donors and their families. Funding was provided from the Minnesota Partnership for Biotechnology and Medical Genomics (MNP IF #14.02), University of Minnesota OVPR Infrastructure Grant , Institute for Engineering in Medicine Grant , NIH/NICHD K12HD073945 , NIH/NICHD F31HD087069 , NIH/NIBIB P41 EB015894 , and the Foundation for Physical Therapy.

Keywords

  • Automatic 2D-3D registration
  • Glenohumeral kinematics
  • Motion tracking accuracy
  • Radiostereometric analysis (RSA)
  • Shoulder motion tracking

PubMed: MeSH publication types

  • Comparative Study
  • Journal Article
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

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