Eliminating registration bias in synthetic CT generation using a physics-based simulation framework for pelvic anatomy
Objective. Supervised synthetic computed tomography (sCT) generation from cone-beam computed tomography (CBCT) requires spatially registered training pairs, yet perfect registration between separately acquired scans is unattainable. This registration bias propagates into trained models and corrupts intensity-based eval...