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Anatomical evaluation of CT-MRI combined femoral model

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Abstract
Background: Both CT and MRI are complementary to each other in that CT can produce a distinct contour of bones, and MRI can show the shape of both ligaments and bones. It will be ideal to build a CT-MRI combined model to take advantage of complementary information of each modality. This study evaluated the accuracy of the combined femoral model in terms of anatomical inspection. Methods: Six normal porcine femora (180 +/- 10 days, 3 lefts and 3 rights) with ball markers were scanned by CT and MRI. The 3D/3D registration was performed by two methods, i.e. the landmark-based 3 points-to-3 points and the surface matching using the iterative closest point (ICP) algorithm. The matching accuracy of the combined model was evaluated with statistical global deviation and locally measure anatomical contour-based deviation. Statistical analysis to assess any significant difference between accuracies of those two methods was performed using univariate repeated measures ANOVA with the Turkey post hoc test. Results: This study revealed that the local 2D contour-based measurement of matching deviation was 0.5 +/- 0.3 mm in the femoral condyle, and in the middle femoral shaft. The global 3D contour matching deviation of the landmark-based matching was 1.1 +/- 0.3 mm, but local 2D contour deviation through anatomical inspection was much larger as much as 3.0 +/- 1.8 mm. Conclusion: Even with human-factor derived errors accumulated from segmentation of MRI images, and limited image quality, the matching accuracy of CT-&-MRI combined 3D models was 0.5 +/- 0.3 mm in terms of local anatomical inspection.
Author(s)
Lee, Yeon S.Seon, Jong K.Shin, Vladimir I.Kim, Gyu-HaJeon, Moongu
Issued Date
2008-01
Type
Article
DOI
10.1186/1475-925X-7-6
URI
https://scholar.gist.ac.kr/handle/local/17491
Publisher
BioMed Central
Citation
BioMedical Engineering Online, v.7
ISSN
1475-925X
Appears in Collections:
Department of Electrical Engineering and Computer Science > 1. Journal Articles
Graduate School of AI Policy and Strategy > 1. Journal Articles
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