Quality-Gated CycleGAN with Frequency-Split Composition for Structure-Constrained Contrast-Like Enhancement of Non-DICOM Chest CT: External Feasibility and Implications for Pulmonary Embolism AI

CT pulmonary angiography (CTPA) is the reference test for pulmonary embolism (PE), whereas AI datasets often contain display-domain (non-DICOM) non-contrast CT (NCT). We present a three-stage pipeline: a representation-consistency gate calibrated on DICOM-verified images (Stage 1), unpaired CycleGAN translation (Stage 2), and a frequency-split composition keeping the acquired finest spatial band plus support-guided residual refinement (Stage 3). Of 208 patients, 30 were held out. The final output reduced mediastinal distribution distance (14.92 to 7.19) and contrast-gap error (11.00 to 4.25; Holm-adjusted p < 0.001). Distribution metrics did not differ significantly from CUT, whereas three edge-based metrics favored the proposed method in all 30 patients. On 24 external NCT scans from three manufacturers (full-pipeline feasibility analysis without paired CTPA), distribution distances decreased and edge metrics again favored the method. The gate flagged all degraded external CTPA images (blur, down-sampling, heavy compression) in every calibration resample. An external 3D CTPA-trained PE model labeled unopacified arteries as embolus on NCT; enhancement reduced this volume in normal patients (12.0 to 1.7 mL), but detection stayed at chance, as on the original NCT (AUROC 0.47; original 0.42; CTPA 0.85). The pipeline harmonizes display-domain NCT with limited structural alteration for dataset preparation rather than CTPA-based detection.