Characterize registration-induced bias in supervised synthetic CT evaluation

Characterize how structured or systematic residual registration errors affect voxel-wise regression, anatomical fidelity, benchmark rankings, and the interpretation of reference-based metrics in supervised synthetic CT generation.

Background

Supervised synthetic CT methods are commonly evaluated with intensity-based metrics computed against registered CT references. Because residual multimodal misregistration can alter anatomical structures and intensity correspondences, these metrics may reward adaptation to a registration convention rather than preservation of patient-specific anatomy.

The paper identifies this limitation as insufficiently characterized in the literature. Resolving it requires determining how registration-induced geometric discrepancies influence training behavior, metric sensitivity, benchmark rankings, and the relationship between voxel-wise agreement and anatomical fidelity.

References

This limitation remains insufficiently characterized in the recent literature , where evaluation protocols predominantly emphasize intensity-based similarity rather than anatomical faithfulness.

— When Misalignment Becomes Supervision: Structured Label Noise in Supervised Synthetic CT Generation  (2609.29387 - Boussot et al., 24 Sep 2026) in Section 2, paragraph “Consequences for synthetic CT evaluation”