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Clinical Significance of Creative 3D-Image Fusion Across [CT + MR] Modalities Based on Approach of Characteristic Co-Registration

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In order to detect findings of multiple clues from both CT and MR for early diagnostics, this study addresses to investigate a registration scheme for two-dimensional (2D) hybrid based on characteristic localization to achieve 3D-fusion from the images of CT and MR as a whole. A cubic oriented proposal of “9-point and 3-plane” for coregistration designs were verified to be geometrically practical. Human internal-feature points were sorted to combine with preselected external-feature points for matching process through 3D-reconstruction and virtual-dissection. By following the procedures of feature-extraction and image-mapping, the processes of “picking points to form plane” and “picking planes for segment” were executed. Ultimately, image-fusions were implemented at the real-time workstation Mimics based on auto-fuse techniques so called “information exchange” and “Signal Overlaying.” A complementary 3D-image across [CT + MR] modalities, which simultaneously presents anatomic structures of hard-tissue and soft-tissue, was created with a detectable-rate of 70%, this is equivalent to detectable-rate of [PET + CT] or [PET + MR] with no statistically significant difference. Our approach of “9-point and 3-plane” offers a fresh idea for integration of digital imaging in mathematic consideration, and it facilitates a 3D vision that isn't functional yet for 2D hybrid imaging. This exploration is practical to those small hospitals that are unable to afford expensive hybrid equipment.
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Keywords: CHARACTERISTIC REGISTRATION; CROSS-MODALITY IMAGE FUSION; CT; HYBRID RADIODETECTOR; IMAGE SEGMENTATION; MRI; PET

Document Type: Research Article

Publication date: 2016-02-01

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  • Journal of Medical Imaging and Health Informatics (JMIHI) is a medium to disseminate novel experimental and theoretical research results in the field of biomedicine, biology, clinical, rehabilitation engineering, medical image processing, bio-computing, D2H2, and other health related areas.
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