Beyond respiratory models: a physics-enhanced synthetic data generation method for 2D-3D deformable registration

Deformable image registration is crucial in aligning medical images for various clinical applications, yet enhancing its efficiency and robustness remains a challenge. Deep Learning methods have shown very promising results for addressing the registration process, however, acquiring sufficient and diverse data for training remains a hurdle. Synthetic data generation strategies have emerged as a solution, yet existing methods often lack versatility and often do not represent well certain types of deformation. This work focuses on X-ray to CT 2D-3D deformable image registration for abdominal interventions, where tissue deformation can arise from multiple sources. Due to the scarcity of real-world data for this task, synthetic data generation is unavoidable. Unlike previous approaches relying on statistical models extracted from 4DCT images, our method leverages a single 3D CT image and physically corrected randomized Displacement Vector Fields (DVF) to enable 2D-3D registration for a variety of clinical scenarios. We believe that our approach represents a significant step towards overcoming data scarcity challenges and enhancing the effectiveness of DL-based DIR in a variety of clinical settings.