SIA-CT:State-Structured Item Alignment for Open-Vocabulary 3D Chest CT Representation Learning

Radiology reports describe both findings and the states that determine their meaning: where a finding occurs, whether it is present, and how severe or certain it is. Learning from these reports requires supervision that preserves these distinctions. We introduce SIA-CT, a framework for state-structured item alignment in 3D chest CT. Its central principle is that cross-patient item alignment requires agreement in observed states, whereas within-examination item separation requires evidence of distinct findings. We instantiate this principle through coordinated item separation, cross-patient alignment, and false-negative masking. A complementary regional objective aligns organ-supported visual features with report descriptions through shared transformations. Together, these objectives train a representation queried by natural language using only CT and text at inference. On 750 report-derived state questions, SIA-CT reaches 66.8% four-axis macro accuracy, exceeding a matched itemized baseline by 3.5 percentage points. Global and textconditioned score fusion yields AUROCs of 83.3% on CT-RATE and 75.3% on RadChestCT, while text-conditioned scoring gives stronger aggregate state discrimination. These results establish observed-state relations as a useful source of supervision and show why finding recognition and state discrimination should be evaluated together.