AI-Assisted segmentation and volumetric reconstruction of radiographs through multi-angular scintillation imaging

X-ray imaging serves as a fundamental tool for non-destructive inspection. Although conventional radiography is well suited for two-dimensional imaging, it cannot provide volumetric structure. Computed tomography provides three-dimensional reconstruction but remains constrained by bulky instrumentation, high radiation exposure, and cost. Here we demonstrate a patch-type scintillator integrated with multi-stage neural network that segments and reconstructs three-dimensional volumes from sparse angular two-dimensional radiographs. The scintillator is fabricated by electrospraying cellulose nanocrystals onto a bulk cellulose matrix, followed by dip-coating of perovskite, yielding a composite with enhanced radioluminescence under X-ray excitation. This flexible film conforms to complex geometries, enabling distortion-free and multi-angle imaging. Neural networks are trained on synthetic datasets and validated on experimentally acquired avian tibiotarsus radiographs, accurately reconstructing volumetric bone structures. This approach serves as a proof-of-concept for low-dose, accessible artificial intelligence-enabled three-dimensional X-ray imaging, demonstrating the feasibility of recovering macroscopic three-dimensional morphology from as few as three sparse projections. Kim et al. report a hardware and software co-designed approach that employs a flexible perovskite scintillator for multi-angle X-ray image acquisition and feed to multi-stage neural networks for segmentation and reconstruction of high-fidelity 3D volumetric X-ray imaging from a limited set of 2D X-ray projections.