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Technical Brief

A Technique for Quantifying Wrist Motion Using Four-Dimensional Computed Tomography: Approach and Validation

[+] Author and Article Information
Kristin Zhao

Rehabilitation Medicine Research Center,
Department of Physical Medicine and Rehabilitation,
Mayo Clinic,
200 First Street SW,
Rochester, MN 55905
e-mail: zhao.kristin@mayo.edu

Ryan Breighner

Biomechanics Laboratory,
Division of Orthopedic Research,
Mayo Clinic,
200 First Street SW,
Rochester, MN 55905
e-mail: Breighner.Ryan@mayo.edu

David Holmes, III

Department of Physiology and Biomedical Engineering,
Mayo Clinic,
200 First Street SW,
Rochester, MN 55905
e-mail: Holmes.David3@mayo.edu

Shuai Leng

Department of Radiology,
Mayo Clinic,
200 First Street SW,
Rochester, MN 55905
e-mail: Leng.Shuai@mayo.edu

Cynthia McCollough

Department of Radiology,
Mayo Clinic,
200 First Street SW,
Rochester, MN 55905
e-mail: mccollough.cynthia@mayo.edu

Kai-Nan An

Fellow ASME Biomechanics Laboratory,
Division of Orthopedic Research,
Mayo Clinic,
200 First Street SW,
Rochester, MN 55905
e-mail: an.kainan@mayo.edu

1Corresponding author.

Manuscript received September 9, 2014; final manuscript received April 12, 2015; published online June 3, 2015. Assoc. Editor: Zong-Ming Li.

J Biomech Eng 137(7), 074501 (Jul 01, 2015) (5 pages) Paper No: BIO-14-1446; doi: 10.1115/1.4030405 History: Received September 09, 2014; Revised April 12, 2015; Online June 03, 2015

Accurate quantification of subtle wrist motion changes resulting from ligament injuries is crucial for diagnosis and prescription of the most effective interventions for preventing progression to osteoarthritis. Current imaging techniques are unable to detect injuries reliably and are static in nature, thereby capturing bone position information rather than motion which is indicative of ligament injury. A recently developed technique, 4D (three dimensions + time) computed tomography (CT) enables three-dimensional volume sequences to be obtained during wrist motion. The next step in successful clinical implementation of the tool is quantification and validation of imaging biomarkers obtained from the four-dimensional computed tomography (4DCT) image sequences. Measures of bone motion and joint proximities are obtained by: segmenting bone volumes in each frame of the dynamic sequence, registering their positions relative to a known static posture, and generating surface polygonal meshes from which minimum distance (proximity) measures can be quantified. Method accuracy was assessed during in vitro simulated wrist movement by comparing a fiducial bead-based determination of bone orientation to a bone-based approach. The reported errors for the 4DCT technique were: 0.00–0.68 deg in rotation; 0.02–0.30 mm in translation. Results are on the order of the reported accuracy of other image-based kinematic techniques.

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Figures

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Fig. 4

Time series data of bead-based and bone-based rotation values of the scaphoid during a wrist flexion–extension trial

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Fig. 3

Custom CT-compatible cadaveric wrist simulator for simulating wrist flexion/extension and radial/ulnar deviation motions via a linear motor (fiducial markers not shown)

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Fig. 2

Proximity values represented on the scaphoid, lunate, and radius articulating surfaces of a right wrist at one time point (from the 18 total) of a 4DCT sequence (palmar view). Proximity values are calculated as the minimum distance from all mesh vertices on the first surface mesh to the second bone mesh. The resulting proximity maps are depicted as a contour color map indicating the minimum distances between the articulating bone surfaces.

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Fig. 1

A single three-dimensional volume-rendered image volume from a 4DCT image sequence (high resolution, not shown). Image volumes are reconstructed at each of 18 time points over the 2 s movement cycles. Images are output in DICOM format for determination of metrics and imaging biomarkers.

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