用一张全景牙片生成3D牙科影像，精准还原牙齿和骨结构。
X-Splat: Gaussian Splatting for 3D CBCT Generation from Single Panoramic Radiograph
- 基于X射线路径初始化高斯点，结合几何约束生成3D体积
- 恢复出下颌神经管等关键解剖结构，优于现有方法
- 适合牙科影像重建、低剂量成像研究者使用
从单张全景牙片（PXR）生成3D牙科体积可提供低辐射替代方案，但问题高度欠定：全景成像将3D衰减沿弯曲射线路径投影至2D图像，导致深度信息缺失。现有隐式与生成式方法常产生过度平滑的几何或解剖不一致的幻觉，缺乏几何驱动监督且依赖光滑表示，难以精确定位锐利解剖边界。我们提出X-Splat，首个用于从单张PXR生成类CBCT 3D牙科体积的高斯点渲染框架。X-Splat利用已知的全景成像几何作为生成骨架：可学习的各向异性高斯原型沿形成输入图像的射线路径初始化，并在一次前向传播中调整，受啤酒-朗伯重投影和多视角放射影像训练监督约束。轻量级残差修正器引入数据集级解剖先验，不覆盖高斯已解析的几何结构。我们在合成的PXR-CBCT配对数据上训练，实现无需真实配对扫描的直接体素监督。我们进一步提出基于分割的几何感知评估指标，首次实现对颌面解剖生成质量的量化评估。X-Splat优于NeRF与GAN基线，成功恢复单个牙齿、皮质边界及牙槽结构，包括此前方法无法重建的下颌神经管。代码将发布于https://github.com/tomek1911/X-Splat。
原文摘要 · Abstract (English)
Generating a 3D dental volume from a single panoramic radiograph (PXR) could provide a low-radiation alternative to Cone-Beam Computed Tomography (CBCT), but the problem is highly underdetermined: panoramic acquisition integrates 3D attenuation along curved X-ray paths into a 2D image, leaving depth-resolved anatomy unobserved. Existing implicit and generative approaches often produce oversmoothed geometry or anatomically inconsistent hallucinations, lacking geometry-driven supervision and relying on smooth representations unable to precisely localize sharp anatomical boundaries. We propose X-Splat, the first Gaussian Splatting framework for generating CBCT-like 3D dental volumes from a single PXR. X-Splat uses the known panoramic acquisition geometry as a generation scaffold: learnable anisotropic Gaussian primitives are initialized along the X-ray paths that formed the input image and adjusted in a single feed-forward pass, constrained by Beer-Lambert reprojection and multi-view radiographic training supervision. A lightweight residual refiner adds dataset-level anatomical priors without overriding the geometry already resolved by the Gaussians. We train on synthetic PXR-CBCT pairs, enabling direct volumetric supervision without paired real scans. We further introduce segmentation-based geometry-aware metrics, providing the first evaluation of PXR-based generation over maxillofacial anatomy. X-Splat outperforms NeRF- and GAN-based baselines, recovering individual teeth, cortical boundaries, and alveolar structure, including the mandibular canal which prior methods fail to reconstruct. Code will be available at https://github.com/tomek1911/X-Splat
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。