HICT: High-precision 3D CBCT reconstruction from a single X-ray

2026-04-01

AI Summary

This study addresses the limitations of conventional cone-beam computed tomography (CBCT)—notably high radiation exposure and cost—and the inability of single panoramic X-rays to yield geometrically consistent, high-fidelity 3D dental reconstructions. To overcome these challenges, the authors propose HiCT, a two-stage framework that first leverages a video diffusion model to synthesize geometrically consistent multi-view projections from a single panoramic X-ray, followed by high-fidelity CBCT reconstruction via a ray-based dynamic attention network integrated with an X-ray sampling strategy. This work pioneers the integration of video diffusion models with ray-wise dynamic attention mechanisms and introduces XCT, a large-scale paired dataset enabling robust training and validation. Experimental results demonstrate state-of-the-art performance on clinically relevant metrics, achieving accurate, geometrically consistent CBCT reconstructions with strong potential for clinical translation.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Accurate 3D dental imaging is vital for diagnosis and treatment planning, yet CBCT's high radiation dose and cost limit its accessibility. Reconstructing 3D volumes from a single low-dose panoramic X-ray is a promising alternative but remains challenging due to geometric inconsistencies and limited accuracy. We propose HiCT, a two-stage framework that first generates geometrically consistent multi-view projections from a single panoramic image using a video diffusion model, and then reconstructs high-fidelity CBCT from the projections using a ray-based dynamic attention network and an X-ray sampling strategy. To support this, we built XCT, a large-scale dataset combining public CBCT data with 500 paired PX-CBCT cases. Extensive experiments show that HiCT achieves state-of-the-art performance, delivering accurate and geometrically consistent reconstructions for clinical use.

Problem

• 3D CBCT reconstruction
• single X-ray
• geometric inconsistency
• low-dose imaging
• dental imaging

Innovation

• HiCT
• video diffusion model
• ray-based dynamic attention
• single-view CBCT reconstruction
• XCT dataset