Deformation-informed unsupervised reference-augmented synthesis for CT alignment from misaligned CBCT–CT pairs in head-and-neck radiotherapy

Registering cone-beam computed tomography (CBCT) to planning computed tomography (CT) in the head-and-neck region is challenging due to the reduced image quality of CBCT and posture-related anatomical deformation, particularly in the cervical spine. We propose a deformation-informed unsupervised reference-augmented synthesis framework that generates an aligned CT (aCT) directly in the CBCT coordinate space from misaligned CBCT–CT image pairs. Unlike conventional deformable registration approaches, the proposed method reformulates image alignment as deformation-informed synthesis. The model integrates a spatial transformer with a Deformation-Informed Efficient Attention (DEA) module that transfers CT features (“keys” and “values”) to spatially corresponding CBCT locations (“queries”). Memory-efficient attention from xFormers enables training within standard GPU memory constraints. Additionally, we introduce a multi-objective loss function and a modular CT style-transfer pre-conditioning stage that is practically essential for narrowing the severe CBCT–CT modality gap, thereby reducing the modality discrepancy before alignment. Experiments on the public SynthRAD2023 head-and-neck dataset demonstrate improved anatomical correspondence (NMI: 0.53 ± 0.12, GC: 0.82 ± 0.09) and dosimetric accuracy comparable to reference CT under surrogate planning conditions (Dose MAE: 0.002 ± 0.001, Gamma pass rate: 90.01 ± 18.2). These findings suggest that reference-guided synthesis can mitigate alignment difficulties in anatomically deformable regions. The implementation is publicly available at https://github.com/A-shazli/Reference_Augmented_Synthesis .