2007 Fiscal Year Final Research Report Summary
In-vivo kinematic analysis of the ACL-reconstructed knee using fuzzy ROI based 2-D/3-D image matching
| Project/Area Number |
18591681
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Orthopaedic surgery
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| Research Institution | Hyogo College of Medicine |
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Principal Investigator |
YAGI Masayoshi Hyogo College of Medicine, Faculty of Medicine, Instructor (80418970)
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| Co-Investigator(Kenkyū-buntansha) |
YOSHIYA Shinichi Hyogo College of Medicine, Faculty of Medicine, Professor (00201070)
KOBASHI Syoji Hyogo Prefectural College, Dept. of engineering, Associate Professor (00332966)
FUKUDA Yuko Hyogo College of Medicine, Faculty of Medicine, Instructor (30368534)
IMAMURA Fumiaki Hyogo College of Medicine, Faculty of Medicine, Instructor (50411997)
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| Project Period (FY) |
2006 – 2007
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| Keywords | Knee / Kinematics / Ligament / CT |
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| Research Abstract |
INTRODUCTION
2-D/3-D image matching has been investigated as one of the attempts in kinematic analysis of the knees with ligamentous injuries. To date, however, high accuracy has not been achieved, and the clinical application is still limited. In this study, we proposed a fuzzy ROI (region of interest) based registration technique to improve the measurement quality. The purpose of this study was to evaluate the accuracy of our proposed method, and to assess its feasibility in the evaluation of the ACL-reconstructed knee.
METHODS
The 3-D MDCT image was obtained using a 16-MDCT scanner, while the 2-D image was obtained with a digital radiography equipment fitted with a wide-vision flat panel detector. In the fuzzy ROI (region of interest) based2-D/3-D image mating, two types of ROIs (“attention region" with characteristic bony land mark and "taboo region" with murky bony contour) were determined. In the subsequent analysis, different values were applied to the ROIs and matching score was calculated. In the error analysis, computer-simulated image was generated and analyzed. The calculated errors were compared between the conventional gradient based registration and the fuzzy ROI based registration. Subsequently, our proposed method was applied to the ACL-reconstructed knees.
RESULTS
Application of the fuzzy ROI based registration resulted in improved accuracy. The resultant error in rotation was calculated to be less than 0.5 degree with the application of our proposed method. In the analysis of the ACL-reconstructed knee and the contralateral intact knee, both overconstrained and underconstrained kinematic patterns were quatitatively identified.
DISCUSSION
This study showed that introduction of fuzzy ROI logic into the 2-D/3-D image matching could improve accuracy and efficiency of the analysis. Kinematic comparison between the normal and ACL-reconstructed knees will provide valuable information in evaluation of the quality of our surgical procedure.
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