In monitoring regeneration after hepatocellular carcinoma resection, addressing the challenge of quantifying the dynamic regeneration process of residual liver through image assessment, this paper designs a 3D bioprinted personalized repair scaffold based on CT/MRI multimodal fusion. Multi-temporal CT/MRI images of hepatocellular carcinoma patients were acquired, registered, and preprocessed using standardization. A dual-channel deep learning network was employed to achieve high-precision liver segmentation and multimodal fusion 3D reconstruction. Based on the deformation field of non-rigid registration, the dynamic changes in residual liver volume after surgery were accurately tracked, and the geometry of the resected cavity was extracted as the basis for scaffold design. Subsequently, the internal pore gradient was optimized by combining regeneration rate data. Using GelMA/HA composite bio-ink, a repair scaffold with a personalized shape and functional internal structure was manufactured using DLP photopolymerization 3D printing technology. Experiments showed that, in terms of structural fidelity, the GelMA/HA composite scaffold exhibited excellent performance in overall shape fidelity (94.82%) and key structural wall thickness deviation (12.3 μm). Regarding in vitro cell compatibility, the relative cell proliferation rate reached 0.85±0.04 under low serum conditions. In vivo biocompatibility showed that the inflammation score decreased to 0.4 8 weeks post-surgery, and neo-tissue infiltration depth reached 850.6 μm. This study effectively promoted host tissue infiltration and functional integration, providing a theoretical and technical pathway for precise repair after liver cancer surgery.