Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Summary

We analyzed joint kinematics from four-dimensional computed tomography data. The sequential 3D-3D registration method semiautomatically provides the kinematics of the moving bone with respect to the subject bone from four-dimensional computed tomography data.

Abstract

Four-dimensional computed tomography (4DCT) provides a series of volume data and visualizes joint motions. However, numerical analysis of 4DCT data remains difficult because segmentation in all volumetric frames is time-consuming. We aimed to analyze joint kinematics using a sequential 3D-3D registration technique to provide the kinematics of the moving bone with respect to the fixed bone semiautomatically using 4DCT DICOM data and existing software. Surface data of the source bones are reconstructed from 3DCT. The trimmed surface data are respectively matched with surface data from the first frame in 4DCT. These trimmed surfaces are sequentially matched until the last frame. These processes provide positional information for target bones in all frames of the 4DCT. Once the coordinate systems of the target bones are decided, translation and rotation angles between any two bones can be calculated. This 4DCT analysis offers advantages in kinematic analyses of complex structures such as carpal or tarsal bones. However, fast or large-scale motions cannot be traced because of motion artifacts.

Introduction

Joint kinematics have been described using a number of methodologies, such as motion capture sensors, 2D-3D registration, and cadaveric studies. Each method has specific advantages and disadvantages. For example, motion capture sensors can measure fast, large-scale motions using infrared cameras with or without sensors on the subject^1,2. However, these methods measure skin motion to infer joint kinematics, and therefore contain skin motion errors³.

Cadaveric studies have been used to evaluate ranges of motion, instability, and contact areas^4....

Protocol

All methods described here have been approved by the Institutional Review Board of Keio University School of Medicine.

NOTE: Joint kinematics are measured by reconstructing the motion of a moving bone around a fixed bone. For knee joint kinematics, the femur is defined as the fixed bone and the tibia is defined as the moving bone.

1. CT imaging protocol

1. Set up the CT machine. Acquire CT examinations with a 320-detector-row CT system to allow for multiple phases of 3D volume data with 160 mm craniocaudal coverage. For example, in the analysis of knee kinematics, the image acquisition consists of 51 vol....

Results

We describe the motion of the tibia during knee extension. The knee joint was positioned in the CT gantry. A triangle pillow was used to support the femur at the starting position. The knee was extended to a straight position over the course of 10 s. Radiation exposure was measured. In addition to 4DCT, static 3DCT of the whole femur, tibia, and patella was performed. Surface data of the whole femur and tibia were reconstructed. The threshold for HU numbers of the bone cortex was set as 2.......

Discussion

Our method allows visualization and quantification of the motions of whole bones and provides numerical positional data of the moving bone with respect to the fixed bone from 4DCT data. Many tools have been suggested for measuring joint kinematics. Motion skin markers can analyze total body motions over a long time. However, this method contains skin motion errors³. Joint kinematics should be estimated from the motion of adjacent bones. The 2D-3D registration method uses fluoroscopy and infers 3D .......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
4DCT scanner	Canon medical systems (Tochigi, Japan)	N/A	4DCT scan, Static 3DCT scan
AVIZO(9.3.0)*	Thermo Fisher Scientific (OR, USA)		Image processing software. Surface reconstruction from CT DICOM data and point cloud data. * Ryan, T. M. & Walker, A. Trabecular bone structure in the humeral and femoral heads of anthropoid primates. Anat Rec (Hoboken). 293 (4), 719-729, doi:10.1002/ar.21139, (2010).
Meshlab**	ISTI (Pisa, Italy)	N/A	Surface trimming and landmark picking ** MeshLab: an Open-Source Mesh Processing Tool. Sixth Eurographics Italian Chapter Conference, page 129-136, 2008. P. Cignoni, M. Callieri, M. Corsini, M. Dellepiane, F. Ganovelli, G. Ranzuglia
VTK(6.3.0)***	Kitware (New York, USA)	N/A	Iterative Closest Points algorithm. Used in python language programming. *** https://vtk.org
Python(3.6.1)	Python Software Foundation	N/A	DICOM file processing to extract the point cloud from the bone cortex ('dicom.py' module). Calculation of the rotation matrices. (Numpy module) Sequential image regestration using ICP algorithm