OBJECTIVE: To investigate a registration approach for 2-dimension (2D) based on characteristic localization to achieve 3-dimension (3D) fusion from images of PET, CT and MR one by one. METHOD: A cubic oriented scheme of"9-point & 3-plane" for co-registration design was verified to be geometrically practical. After acquisiting DICOM data of PET/CT/MR (directed by radiotracer 18F-FDG etc.), through 3D reconstruction and virtual dissection, human internal feature points were sorted to combine with preselected external feature points for matching process. By following the procedure of feature extraction and image mapping, "picking points to form planes" and "picking planes for segmentation" were executed. Eventually, image fusion was implemented at real-time workstation mimics based on auto-fuse techniques so called "information exchange" and "signal overlay". RESULT: The 2D and 3D images fused across modalities of [CT+MR], [PET+MR], [PET+CT] and [PET+CT+MR] were tested on data of patients suffered from tumors. Complementary 2D/3D images simultaneously presenting metabolic activities and anatomic structures were created with detectable-rate of 70%, 56%, 54% (or 98%) and 44% with no significant difference for each in statistics. CONCLUSION: Currently, based on the condition that there is no complete hybrid detector integrated of triple-module [PET+CT+MR] internationally, this sort of multiple modality fusion is doubtlessly an essential complement for the existing function of single modality imaging. Crown
OBJECTIVE: To investigate a registration approach for 2-dimension (2D) based on characteristic localization to achieve 3-dimension (3D) fusion from images of PET, CT and MR one by one. METHOD: A cubic oriented scheme of"9-point & 3-plane" for co-registration design was verified to be geometrically practical. After acquisiting DICOM data of PET/CT/MR (directed by radiotracer 18F-FDG etc.), through 3D reconstruction and virtual dissection, human internal feature points were sorted to combine with preselected external feature points for matching process. By following the procedure of feature extraction and image mapping, "picking points to form planes" and "picking planes for segmentation" were executed. Eventually, image fusion was implemented at real-time workstation mimics based on auto-fuse techniques so called "information exchange" and "signal overlay". RESULT: The 2D and 3D images fused across modalities of [CT+MR], [PET+MR], [PET+CT] and [PET+CT+MR] were tested on data of patients suffered from tumors. Complementary 2D/3D images simultaneously presenting metabolic activities and anatomic structures were created with detectable-rate of 70%, 56%, 54% (or 98%) and 44% with no significant difference for each in statistics. CONCLUSION: Currently, based on the condition that there is no complete hybrid detector integrated of triple-module [PET+CT+MR] internationally, this sort of multiple modality fusion is doubtlessly an essential complement for the existing function of single modality imaging. Crown