多层高斯散射用于沉浸式解剖可视化
Multi-Layer Gaussian Splatting for Immersive Anatomy Visualization
AI总结：
本文提出多层高斯散射方法，用于高效且静态地表示CT扫描，实现沉浸式解剖可视化，支持交互式三维评估与医学教育。
摘要：
在医学图像可视化中，对体积医学数据（如CT扫描）进行路径追踪可以产生逼真的三维可视化。沉浸式VR显示器可以进一步增强对复杂解剖结构的理解。超越传统2D切片的诊断质量，它们使解剖结构的交互式三维评估成为可能，支持医学教育和规划。然而，实时渲染高质量可视化在计算上是密集的，对于计算受限的设备如移动头显来说是不现实的。我们提出了一种利用GS创建CT扫描高效但静态中间表示的新方法。我们引入了一种分层的GS表示，逐步包含不同的解剖结构，同时最小化重叠，并扩展GS训练以移除不活跃的高斯分布。我们进一步通过跨层聚类压缩所创建的模型。我们的方法在保持解剖结构的同时实现了交互式帧率，质量可根据目标硬件进行调整。与标准GS相比，我们的表示保留了最初由沉浸式路径追踪启用的一些探索特性。在渲染时可以选择性地激活和裁剪层，为原本静态的GS模型增加了一定的交互性。这可能使在高计算需求情况下原本无法使用路径追踪医学体积的场景成为可能。
英文摘要：
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.
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