用可微反投影和注意力机制，从低剂量二维片重建儿童头骨三维CT。
PSCT-Net: Geometry-Aware Pediatric Skull CT Reconstruction via Differentiable Back-Projection and Attention-Guided Refinement
- 通过可微反投影建立空间精准的三维先验，解决深度模糊问题。
- 注意力模块学习二维与三维体素的非线性对应关系，提升骨骼边界清晰度。
- 专为儿童头骨设计，适合医学影像低剂量重建与儿科临床应用。
计算机断层扫描（CT）对诊断儿童颅面异常至关重要，但对发育中的组织存在辐射风险。从稀疏双平面X光片重建3D CT是一种低剂量替代方案，但问题严重病态。现有方法采用无几何意识的特征提升，盲目将2D特征投影至3D，缺乏显式空间建模，导致深度模糊和骨性边界退化。我们提出PSCT-Net，一种具有可微反投影的几何感知框架。可微反投影建立空间忠实的体素先验，缓解深度模糊。注意力引导投影（AGP-3D）模块学习2D区域与3D位置间的非线性体素级对应关系。双向梅巴（BiM-3D）模块以线性复杂度捕捉长程体素依赖性。我们还构建了私有机构儿童头骨CT数据集PedSkull-CT，包含正常与病理病例，用于内部评估，填补了现有成人中心、躯干聚焦数据集的空白。
原文摘要 · Abstract (English)
Computed Tomography (CT) is essential for diagnosing pediatric craniofacial abnormalities, yet poses radiation risks to developing anatomies. Reconstructing 3D CT from sparse bi-planar X-rays offers a low-dose alternative but is severely ill-posed. Existing methods employ geometry-agnostic feature lifting, naively projecting 2D features into 3D without explicit spatial modeling, causing depth ambiguity and degraded osseous boundaries. We present PSCT-Net, a geometry-aware framework with differentiable back-projection. Differentiable back-projection establishes a spatially faithful volumetric prior, alleviating depth ambiguity. An Attention-Guided Projection (AGP-3D) module then learns non-linear voxel-wise correspondences between 2D regions and 3D locations. A Bidirectional Mamba (BiM-3D) module captures long-range volumetric dependencies with linear complexity. We further curate a private institutional pediatric skull CT cohort, PedSkull-CT, comprising normal and pathological cases for internal evaluation, addressing the gap in adult-centric, trunk-focused datasets.
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