mlsanityData to 14 Oct 2026

PSCT-Net: Geometry-Aware Pediatric Skull CT Reconstruction via Differentiable Back-Projection and Attention-Guided Refinement

arXiv 2606.19867 ↗First posted 18 Jun 2026cs.CV cs.AIcited by 0

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Abstract (arXiv)

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. Project page and code are available at https://dydevelop.github.io/PSCT-Net/.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 33 references that are in the map. First posted on arXiv 18 Jun 2026.

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19 of 33 references.

References · 19

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
  3. 2020Denoising Diffusion Implicit Modelscited by 9,781
  4. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,226
  5. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,718
  6. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
  7. 2020MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformerscited by 1,463
  8. 2016Least Squares Generative Adversarial Networkscited by 1,001
  9. 2017Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learningcited by 950
  10. 2024U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentationcited by 419
  11. 2024VM-UNet: Vision Mamba UNet for Medical Image Segmentationcited by 287
  12. 2019X2CT-GAN: Reconstructing CT from Biplanar X-Rays with Generative Adversarial Networkscited by 85
7 more
  1. 2024Transformers to SSMs: Distilling Quadratic Knowledge to Subquadratic Modelscited by 67
  2. 2021CTSpine1K: A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed Tomographycited by 46
  3. 2020Deep Learning to Segment Pelvic Bones: Large-scale CT Datasets and Baseline Modelscited by 45
  4. 2024DiffuX2CT: Diffusion Learning to Reconstruct CT Images from Biplanar X-Rayscited by 9
  5. 2020XraySyn: Realistic View Synthesis From a Single Radiograph Through CT Priorscited by 8
  6. 2025DVG-Diffusion: Dual-View Guided Diffusion Model for CT Reconstruction from X-Rayscited by 4
  7. 2024XctDiff: Reconstruction of CT Images with Consistent Anatomical Structures from a Single Radiographic Projection Imagecited by 3

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