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.

翻译：本文解决了从异质解剖场景中生成可重复投影空间观测的计算问题，该场景中的组成部分可经历独立的空间变换。我们提出了一种变换驱动框架，用于从多模态解剖数据生成合成投影图像，并以下颌运动场景为例进行演示。与主要针对配准、投影真实性或渲染效率的传统数字重建放射影像（DRR）方法不同，本文提出的公式将投影成像视为对显式表示的解剖场景进行观测的过程。可独立变换的基于体积和曲面的解剖对象被嵌入到共享场景表示中，并通过显式变换直接传播到投影空间。投影几何、采集建模、材料解释和图像呈现保持显式分离，从而在保持生成投影之间的可重复性和直接可比性的同时，能够对方法学假设进行可控探索。特别强调了与颅面分析相关的变换驱动解剖场景，包括下颌运动和治疗性复位。通过使用由CT/CBCT体数据、分割结构、曲面模型以及辅助解剖或治疗对象组成的共享解剖参考场景，该框架能够在保持相同成像假设的同时，从多种解剖配置生成可直接比较的VirtualRTG投影。本方法并非旨在实现完全物理真实的放射学模拟，而是为研究解剖-投影关系、运动可观测性以及变换感知的成像工作流，提供了一个可控且可重复的方法学环境。