AI-Assisted segmentation and volumetric reconstruction of radiographs through multi-angular scintillation imaging.
Authors
Affiliations (14)
Affiliations (14)
- Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, USA.
- Department of Radiology, University of California San Francisco, San Francisco, USA.
- Department of Orthopedic Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
- Department of Clinical Research Design and Evaluation, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, Republic of Korea.
- Department of Radiation Oncology, Asan Medical Center, Seoul, Republic of Korea.
- Department of Chemical Engineering, Inha University, Incheon, Republic of Korea.
- Department of Chemical Engineering, Hanyang University, Seoul, Republic of Korea.
- Department of Materials Science and Engineering, University of Michigan, Ann Arbor, USA.
- Department of Materials Science and Engineering, University of Virginia, Charlottesville, USA.
- Department of Chemical Engineering, Hanyang University, Seoul, Republic of Korea. [email protected].
- Division of System Semiconductor, Dongguk University, Seoul, Republic of Korea. [email protected].
- Department of Chemical Engineering, Inha University, Incheon, Republic of Korea. [email protected].
- Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, USA. [email protected].
- Department of Materials Science and Engineering, University of Virginia, Charlottesville, USA. [email protected].
Abstract
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.