원저 : 뇌 PET 과 MR 영상의 자동화된 3차원적 합성기법 개발 ( Development of an Automatic 3D Coregistration Technique of Brain PET and MR Images ) () |
Author |
이재성(Jae Sung Lee),곽철은(Cheol Eun Kwark),이동수(Dong Soo Lee),이명철(Myung Chul Lee),박광석(Kwang Suk Park), |
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Affiliation |
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Abstract |
Purpose: Cross-modality coregistration of positron
emission tomography (PET) and magnetic resonance
imaging (MR) could enhance the clinical information. In
this study we propose a refined technique to improve
the robustness of registration, and to implement more
realistic visualization of the coregistered images.
Materials and Methods: Using the sinogram of PET
emission scan, we extracted the robust head boundary
and used boundary-enhanced PET to coregister PET with
MR. The pixels having 10% of maximum pixel value were
considered as the boundary of sinogram. Boundary pixel
values were exchanged with maximum value of sinogram.
One hundred eighty boundary points were extracted at
intervals of about 2 degree using simple threshold
method from each slice of MR images. Best affined
transformation between the two point sets was performed
using least square fitting which should minimize the
sum of Euclidean distance between the point sets. We
reduced calculation time using pre-defined distance
map. Finally we developed an automatic coregistration
program using this boundary detection and surface
matching technique. We designed a new weighted
normalization technique to display the coregistered PET
and MR images simultaneously. Results: Using our newly
developed method, robust extraction of head boundary
was possible and spatial regishation was successfully
performed. Mean displacement error was less than 2.0mm.
In visualization of coregistered images using weighted
normalization method, structures shown in MR image
could be realistically represented. Conclusion: Our
refined technique could practically enhance the
performance of automated three dimensional
coregistration. |
Keyword |
Image coregistration, PET, MR, Surface matching |
Full text Article |
28502206.pdf
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