Research On Key Technologies In Peri-Interventional Navigation And Ultrasonic Monitoring For Thermal Tumor Ablation

Thermal tumor ablation therapy(microwave ablation and radiofrequency ablation) involves inserting an ablation needle(microwave antenna or radiofrequency electrode) into the tumor as the heating source under the guidance of medical imaging. The tumor was heated to a high temperature(over 60 °C) within minutes to induce complete coagulation necrosis instantly, so as to kill the tumor in situ. Peri-interventional navigation of ablation needles and real-time evaluation of treatment efficacy are key issues in urgent need of solution with respect to thermal tumor ablation therapy. This dissertation focused on key technologies in peri-interventional multi-modality image navigation and ultrasonic monitoring for thermal tumor ablation. In this dissertation, starting with three key aspects including ultrasound and CT(Computed Tomography) image segmentation, ultrasonic tissue characterization and computer-assisted ablation needle trajectory planning, the semi-automatic breast tumor ultrasound image segmentation method, three-dimensional(3D) semi-automatic liver CT segmentation method, real-time ultrasonic monitoring method for radiofrequency ablation as well as computer-assisted ablation needle trajectory planning technique were studied. The main research contents are as follows.(1) Research on semi-automatic breast tumor ultrasound image segmentation method and its application. A new method was proposed for semi-automatic tumor segmentation on breast ultrasound images using Gaussian filtering, histogram equalization, mean shift, and graph cuts. A breast ultrasound image segmentation software was developed. The only interaction required for the proposed method was to select two diagonal points to determine a region of interest(ROI) on an input image. The ROI image was then shrunken by factor 2 using bicubic interpolation to reduce computation time. The shrunken image was smoothed by a Gaussian filter and then contrast-enhanced by histogram equalization. Next, the enhanced image was filtered by pyramid mean shift to improve homogeneity. The object and background seeds for graph cuts were automatically generated on the filtered image. Using these seeds, the filtered image was then segmented by graph cuts into a binary image containing the object and background. Finally, the binary image was expanded by factor 2 using bicubic interpolation and the expanded image was processed by morphological opening and closing to refine the tumor contour. The method was implemented with C++ language, Open CV and Visual Studio 2010 and tested for 38 breast ultrasound images with benign tumors and 31 breast ultrasound images with malignant tumors from different ultrasound scanners. Experimental results showed that our method had a true positive rate of 91.7%, a false positive rate of 11.9%, and a similarity rate of 85.6%. The mean run time on Intel Core 2.66 GHz CPU and 4 GB RAM was 0.49 ± 0.36 s. The experimental results indicate that the proposed method may be useful in BUS image segmentation.(2) Research on 3D semi-automatic liver CT segmentation method and its application. A semi-automatic and fast method for 3D liver segmentation from CT volumetric images was proposed using SLIC(Simple Linear Iterative Clustering) supervoxels and 3D graph cuts. A 3D semi-automatic liver CT segmentation software was developed. The state-of-the-art automatic and semi-automatic liver CT segmentation methods had high computational cost and low segmentation speed. To this end, a fast semi-automatic liver CT segmentation algorithm was proposed. First, the CT volumetric image was down-sampled to reduce the computational cost. Then, an appropriate window leveling(window width and window center) was set for the CT volumetric image to covert 16 bit to 8 bit image and to enhance the contrast between the liver and adjacent tissues. The SLIC supervoxel algorithm was applied to the 8-bit CT image to generate a series of supervoxels. These supervoxels were used to construct the nodes in the graph for graph cuts. The foreground and background seeds for graph cuts were selected interactively. Subsequently, the max-flow / min-cut algorithm was employed to segment out the foreground and background in the graph. Finally, the segmentation result underwent up-sampling, 3D binary opening and 3D median filtering to obtain the ultimate liver object. The proposed algorithm was implemented using C++ language, VC++ 2008, ITK, VTK and Open CV. CT image segmentation experiments were conducted on 30 volumetric images provided by MICCAI(Medical Image Computing and Computer Aided Intervention) SLIVER07. The experimental results demonstrated that the proposed method could get a satisfying segmentation performance and have a segmentation speed that is faster than the majority of existing 3D liver CT segmentation methods.(3) Research on real-time ultrasonic monitoring method for radiofrequency ablation(RFA) based on probability statistical model of ultrasound backscatter signals(i.e., Nakagami model) and its application. A novel algorithmic scheme based on the frequency and temporal compounding of Nakagami imaging for enhanced ablation zone visualization was proposed. The proposed algorithm was integrated into a clinical scanner to develop a real-time Nakagami imaging system for monitoring RFA using C++ language, Open CV and Visual Studio 2010. The applicability of Nakagami imaging to various types of tissues was investigated. The performance of the real-time Nakagami imaging system in visualizing RFA-induced ablation zones was validated by measuring porcine liver(n = 18) and muscle tissues(n = 6) in vitro. The experimental results showed that the proposed algorithm can operate on a standard clinical ultrasound scanner to monitor RFA in real time. The Nakagami imaging system effectively monitors RFA-induced ablation zones in liver tissues. However, because tissue properties differ, the system cannot visualize ablation zones in muscle fibers.(4) Research on computer-assisted ablation needle trajectory(path) planning. Ablation needle intervention trajectory(puncture path) consists of the entry position on the skin(entry point) and the target position(tumor target). Based on the patient’s preoperative CT sequence images, the constraint of ablation needle trajectory length(puncture depth) and the constraint of distance between ablation needle trajectory and risk structures were designed using the Me Vis Lab software. Based on 3D reconstruction and visualization of the patient’s structures including liver, hepatic vessels and hepatic tumor, a manual planning system for ablation needle trajectory was realized using the Fit Me open-source toolkit, which was implemented using the Medical Imaging Interaction Toolkit(MITK).