Transformation-driven generation of comparable projection images from multimodal anatomical scenes

2026-06-15

AI Summary

This work addresses the challenge of generating comparable projection images from multimodal, heterogeneous anatomical scenes under independent spatial transformations. To this end, the authors propose a transformation-driven synthetic projection imaging framework that explicitly models anatomical structures as independently transformable volumetric data and surface representations. For the first time, projection imaging is formulated as an observation process grounded in an explicit anatomical scene, decoupling projection geometry, acquisition modeling, material interpretation, and image rendering. By integrating CT/CBCT data, segmented anatomical structures, and virtual radiographic (RTG) projection techniques, the framework produces imaging-consistent, comparable projections for craniofacial analysis tasks such as mandibular motion studies, thereby enabling controlled and reproducible investigation of anatomy–projection relationships and motion observability.

Abstract

This work addresses the computational problem of generating reproducible projection-space observations from heterogeneous anatomical scenes whose components may undergo independent spatial transformations. We propose a transformation-driven framework for synthetic projection imaging from multimodal anatomical data and demonstrate it on mandibular-motion scenarios. In contrast to conventional Digitally Reconstructed Radiograph (DRR) approaches primarily designed for registration, projection realism, or rendering efficiency, the proposed formulation treats projection imaging as an observation process operating on an explicitly represented anatomical scene. Independently transformable volumetric and surface-based anatomical objects are embedded within a shared scene representation and propagated directly into projection space through explicit transformations. Projection geometry, acquisition modelling, material interpretation, and image presentation remain explicitly separated, enabling controlled exploration of methodological assumptions while preserving reproducibility and direct comparability between generated projections. Particular emphasis is placed on transformation-driven anatomical scenarios relevant to craniofacial analysis, including mandibular motion and therapeutic repositioning. Using a shared anatomical reference scene composed of CT/CBCT volumes, segmented structures, surface models, and auxiliary anatomical or therapeutic objects, the framework enables generation of directly comparable VirtualRTG projections from multiple anatomical configurations while preserving identical imaging assumptions. Rather than aiming at fully physically faithful radiographic simulation, the proposed approach provides a controllable and reproducible methodological environment for studying anatomy--projection relationships, motion observability, and transformation-aware imaging workflows.

Problem

• multimodal anatomical scenes
• spatial transformations
• projection imaging
• comparable projections
• anatomical motion

Innovation

• transformation-driven imaging
• multimodal anatomical scene
• comparable projection generation
• VirtualRTG
• anatomical motion modeling