Development of an Automatic 3D Coregistration Technique of Brain PET and MR Images.
- Author:
Myung Chul LEE
;
June Key CHUNG
;
Dong Soo LEE
;
Cheol Eun KWARK
;
Jae Sung LEE
;
Kwang Suk PARK
- Publication Type:Original Article
- Keywords:
Image coregistration;
PET;
MR;
Surface matching
- MeSH:
Brain*;
Head;
Magnetic Resonance Imaging;
Positron-Emission Tomography
- From:Korean Journal of Nuclear Medicine
1998;32(5):414-424
- CountryRepublic of Korea
- Language:Korean
-
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.
