Counterfactual Support Fields for Task-Preserving Medical Image Reconstruction

Medical image reconstruction is commonly optimised for whole-image fidelity, although errors of similar magnitude can have different diagnostic consequences depending on their location. Under aggressive radial MR undersampling and sparse-view CT, lesion structures may remain degraded despite high global image quality. We propose CSF-Recon, a diagnosis-oriented framework that uses a counterfactual support field (CSF) to coordinate spatially targeted reconstruction and diagnostic evidence aggregation. Our analysis bounds diagnostic-margin degradation by reconstruction error weighted by spatial decision sensitivity, motivating preferential restoration of diagnostically relevant regions. Counterfactual alignment trains a reference teacher on fully sampled images to discourage reliance on decision-changing evidence outside annotated lesions. Its lesion-restricted class responses supervise a student field that gates microstructure restoration, local refinement, and local diagnostic pooling while retaining global context. The student predicts the field from a physics-driven coarse reconstruction, enabling spatial guidance directly from incomplete measurements. The teacher and lesion annotations are required only during training. On fastMRI+ and LIDC-IDRI at 16-fold acceleration, CSF-Recon achieves the highest mean accuracy and AUC among evaluated methods without maximising whole-image PSNR or SSIM. These results support spatially selective restoration as a practical approach to preserving diagnostic information under incomplete measurements.