In medical image visualization, path tracing of volumetric medical data like CT scans produces lifelike three-dimensional visualizations. Immersive VR displays can further enhance the understanding of complex anatomies. Going beyond the diagnostic quality of traditional 2D slices, they enable interactive 3D evaluation of anatomies, supporting medical education and planning. Rendering high-quality visualizations in real-time, however, is computationally intensive and impractical for compute-constrained devices like mobile headsets. We propose a novel approach utilizing GS to create an efficient but static intermediate representation of CT scans. We introduce a layered GS representation, incrementally including different anatomical structures while minimizing overlap and extending the GS training to remove inactive Gaussians. We further compress the created model with clustering across layers. Our approach achieves interactive frame rates while preserving anatomical structures, with quality adjustable to the target hardware. Compared to standard GS, our representation retains some of the explorative qualities initially enabled by immersive path tracing. Selective activation and clipping of layers are possible at rendering time, adding a degree of interactivity to otherwise static GS models. This could enable scenarios where high computational demands would otherwise prohibit using path-traced medical volumes.
在医学图像可视化中，对CT扫描等体积医学数据进行路径追踪可以生成逼真的三维可视化。沉浸式VR显示进一步增强了对复杂解剖结构的理解，超越了传统二维切片的诊断质量，使得交互式三维解剖评估成为可能，支持医学教育和规划。然而，在实时渲染高质量的可视化效果时，计算量需求极高，对于移动头显等计算受限设备来说不切实际。 我们提出了一种利用高斯点云（GS）的新方法，用于创建高效但静态的CT扫描中间表示。我们引入了分层的GS表示，逐层增量地包含不同的解剖结构，同时最小化重叠，并通过扩展GS训练来移除不活跃的高斯。我们进一步通过层间聚类对模型进行压缩，以提升效率。 我们的方法在保留解剖结构的同时实现了交互帧率，并可根据目标硬件调整质量。与标准GS相比，我们的表示保留了沉浸式路径追踪初始提供的一些探索特性。渲染时可选择性地激活和裁剪各层，为静态GS模型增加了一定的交互性。这使得在高计算需求的场景下，原本无法使用路径追踪的医学体数据成为可能。