Fast GPU-based CT reconstruction applied in ablation treatment for hepatocellular carcinoma

Abstract Objective: To develop an image visualization system based on graphic processing unit (GPU) hardware acceleration for clinical use in hepatocellular carcinoma (HCC) interventional planning. Methods: We developed a liver tumor planning tool to assist the physician in providing patient-specific analysis and visualization. We employed a spatial distance computation algorithm to determine the spatial location of tumors and their relation to the main hepatic vessels. GPU hardware acceleration was implemented for rapid calculation of the spatial distance from the tumor surface to the surrounding vascular territories. Results: The algorithm for spatial distance provided an accurate minimum value for the distance from the tumor surface to the surrounding duct system as well as the region of interest (ROI). Analyzing the data (mean CPU time = 43.14 ± 29.34; mean GPU time = 0.41 ± 0.38) using an independent samples t-test, the result showed a remarkable difference (p < 0.001). Thus, GPU hardware acceleration performed the distance arithmetic at higher rates than conventional CPUs. Conclusions: The visual assistance tool performs as an intuitive and objective module in clinical cases, and is expected to help physicians achieve a more reliable treatment in liver tumor patients. As such, we believe it represents an improvement in image guided preoperative planning.