Enhancing the depth perception of DSA images with 2D-3D registration

Objective

Today, cerebrovascular disease has become an important health hazard. Therefore, it is necessary to perform a more accurate and less time-consuming registration of preoperative three-dimensional (3D) images and intraoperative two-dimensional (2D) projection images which is very important for conducting cerebrovascular disease interventions. The 2D-3D registration method proposed in this study is designed to solve the problems of long registration time and large registration errors in 3D computed tomography angiography (CTA) images and 2D digital subtraction angiography (DSA) images.

Methods

To make a more comprehensive and active diagnosis, treatment and surgery plan for patients with cerebrovascular diseases, we propose a weighted similarity measure function, the normalized mutual information-gradient difference (NMG), which can evaluate the 2D-3D registration results. Then, using a multi-resolution fusion optimization strategy, the multi-resolution fused regular step gradient descent optimization (MR-RSGD) method is presented to attain the optimal value of the registration results in the process of the optimization algorithm.

Result

In this study, we adopt two datasets of the brain vessels to validate and obtain similarity metric values which are 0.0037 and 0.0003, respectively. Using the registration method proposed in this study, the time taken for the experiment was calculated to be 56.55s and 50.8070s, respectively, for the two sets of data. The results show that the registration methods proposed in this study are both better than the Normalized Mutual (NM) and Normalized Mutual Information (NMI).

Conclusion

The experimental results in this study show that in the 2D-3D registration process, to evaluate the registration results more accurately, we can use the similarity metric function containing the image gray information and spatial information. To improve the efficiency of the registration process, we can choose the algorithm with gradient optimization strategy. Our method has great potential to be applied in practical interventional treatment for intuitive 3D navigation.

Research on Panoramic Stitching Algorithm of Lateral Cranial Sequence Images in Dental Multifunctional Cone Beam Computed Tomography

In the design of dental multifunctional Cone Beam Computed Tomography, the linear scanning strategy not only saves equipment cost, but also avoids the demand for patients to be repositioned when acquiring lateral cranial sequence images. In order to obtain panoramic images, we propose a local normalized cross-correlation stitching algorithm based on Gaussian Mixture Model. Firstly, the Block-Matching and 3D filtering algorithm is used to remove quantum and impulse noises according to the characteristics of X-ray images; Then, the segmentation of the irrelevant region and the extraction of the region of interest are performed by Gaussian Mixture Model; The locally normalized cross-relation is used to complete the registration with the multi-resolution strategy based on wavelet transform and Particle Swarm Optimization algorithm; Finally, image fusion is achieved by the weighted smoothing fusion algorithm. The experimental results show that the panoramic image obtained by this method has significant performance in both subjective vision and objective quality evaluation and can be applied to preoperative diagnosis of clinical dental deformity and postoperative effect evaluation.