PSCT-Net: Geometry-Aware Pediatric Skull CT Reconstruction via Differentiable Back-Projection and Attention-Guided Refinement
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/.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Boosting Skull-Stripping Performance for Pediatric Brain Images
Skull-stripping is the removal of background and non-brain anatomical features from brain images. While many skull-stripping tools exist, few target pediatric populations. With the emergence of multi-institutional pediat…
Skull StrippingMRI-to-CT Synthesis With Cranial Suture Segmentations Using A Variational Autoencoder Framework
Quantifying normative pediatric cranial development and suture ossification is crucial for diagnosing and treating growth-related cephalic disorders. Computed tomography (CT) is widely used to evaluate cranial and sutura…
CraNeXt: Automatic Reconstruction of Skull Implants With Skull Categorization Technique
Automatic cranial implant design aims to design a patient-specific implant where various machine-learning-based skull reconstruction techniques have been introduced to predict the implant. Despite the significant progres…
Probabilistic Joint Face-Skull Modelling for Facial Reconstruction
We present a novel method for co-registration of two independent statistical shape models. We solve the problem of aligning a face model to a skull model with stochastic optimization based on Markov Chain Monte Carlo (MC…
Face ModelStochastic OptimizationSkull-to-Face: Anatomy-Guided 3D Facial Reconstruction and Editing
Deducing the 3D face from a skull is a challenging task in forensic science and archaeology. This paper proposes an end-to-end 3D face reconstruction pipeline and an exploration method that can conveniently create textur…
3D Face ReconstructionAnatomyFace ModelFace Reconstruction