AIM: To provide a tool to plan neurosurgical intervention which demonstrate multimodal brain images together in two and three dimensions respectively. MATERIAL and METHODS: On the brain CT-MRI image sequences, a unique multilevel registration technique is used in place of the traditional image registration strategy, which has drawbacks. The multi-resolution registration technique is used for level one registration, and the Bspline Deformable Registration Technique is used for level-2 registration. The results of the multilevel registration procedure are then used to execute feature-based fusion. This is followed by 3-D volume rendering on the fused sequence of CT and registered MRI sequence. RESULTS: The results of fusion are evaluated between the resultants of both level-1 and level-2 registration. The Q4 index and the correlation coefficient (CC) are two potential measures for estimating fusion results. Both level-1 and 2 registration approaches yield fusion results. For level-1 and 2 fusions, the average CC measured across all image pairs is 0.71 and 0.85, respectively, while Q4 measured 0.21 and 0.46 for level-1 and 2, respectively. At level-2 registration, both CC and Q4 exhibit an improvement in fusion. Using VTK angle and distance widgets for measuring distances and angles on the 3-D model improves path planning capabilities. CONCLUSION: The proposed research creates a computer-aided platform for better neurosurgical planning. The multilevel registration method produced promising fusion results and laid the groundwork for enhanced 3-D viewing of fused CT-MRI sequences using depth peeling. Distance and angle measurements improve surgical planning capability.
All Science Journal Classification (ASJC) codes
- Clinical Neurology