计算机科学
渲染(计算机图形)
体绘制
可视化
路径跟踪
光线追踪(物理)
并行渲染
计算机视觉
蒙特卡罗方法
人工智能
计算机图形学(图像)
统计
物理
数学
量子力学
作者
Elena Denisova,Leonardo Manetti,Leonardo Bocchi,Ernesto Iadanza
标识
DOI:10.1007/978-3-031-43987-2_34
摘要
Three-dimensional (3D) rendering of biomedical volumes can be used to illustrate the diagnosis to patients, train inexperienced clinicians, or facilitate surgery planning for experts. The most realistic visualization can be achieved by the Monte-Carlo path tracing (MCPT) rendering technique which is based on the physical transport of light. However, this technique applied to biomedical volumes has received relatively little attention, because, naively implemented, it does not allow to interact with the data. In this paper, we present our application of MCPT to the biomedical volume rendering–Advanced Realistic Rendering Technique (AR2T), in an attempt to achieve more realism and increase the level of detail in data representation. The main result of our research is a practical framework that includes different visualization techniques: iso-surface rendering, direct volume rendering (DVR) combined with local and global illumination, maximum intensity projection (MIP), and AR2T. The framework allows interaction with the data in high quality for the deterministic algorithms, and in low quality for the stochastic AR2T. A high-quality AR2T image can be generated on user request; the quality improves in real-time, and the process is stopped automatically on the algorithm convergence, or by user, when the desired quality is achieved. The framework enables direct comparison of different rendering algorithms, i.e., utilizing the same view/light position and transfer functions. It therefore can be used by medical experts for immediate one-to-one visual comparison between different data representations in order to collect feedback about the usefulness of the realistic 3D visualization in clinical environment.
科研通智能强力驱动
Strongly Powered by AbleSci AI