CT2X: An Empirical Study of Physics-Grounded World Models for Radiographic Navigation

CT-grounded virtual views can appear plausible despite incorrect three-dimensional alignment, leaving both their geometric reliability and the source of downstream gains unclear. We introduce CT2X, an empirical framework that makes this information flow explicit: patient-specific CT is a persistent 3D world, a source radiograph updates an explicit rigid pose, a known imaging action analytically propagates that pose, and a task renderer produces the target view. CT2X holds the estimated state fixed to compare direct state use with a virtual-landmark round trip, and matches renderer budgets to compare additional computation with an additional real radiograph. We combine controlled experiments on an 80-case CT cohort with evaluations on four public real-image benchmarks, using learned state correction, intensity-based registration, and learned 2D–3D correspondence under dataset-specific evaluation settings. On 224 view pairs from seven held-out patients, state correction reduces mean future translation error from 47.42 to 7.89 mm; direct use of the estimated pose and recovery from virtual landmarks yield numerically equivalent results across three seeds. For this geometric task, the gain is explained by state correction. In a six-object DeepFluoro comparison, adding a second real view reduces one failed object's mean target registration error from 25.91 to 3.05 mm under a matched maximum budget of 3,000 renderer evaluations; additional single-view optimization leaves that failure unresolved. Gains vary across objects and evaluation settings, and action perturbations cause large target errors even from reference source poses. Evaluations on public datasets use reference-derived actions and assess retrospective geometric reliability. Overall, the results show that virtual navigation depends on accurate state estimation and action fidelity; additional computation cannot always substitute for a new observation.