Accurate automated 3D lumbar spine reconstruction from biplanar X-rays using multi-task deep learning and anatomy-aware optimization.

BACKGROUND: Accurate 3D assessment of the weight-bearing lumbar spine is crucial for diagnosing various spinal pathologies. However, existing biplanar X-ray reconstruction methods struggle with complex pathologies and low-contrast structures. PURPOSE: This study aims to propose a fully automated framework for high-accuracy 3D lumbar spine reconstruction from biplanar X-ray images. METHODS: Statistical shape models (SSMs) of L1-L5 vertebrae were constructed from 270 lumbar CT scans. A multi-task deep learning network was designed to simultaneously isolate vertebral signals from biplanar X-rays and detect anatomical landmarks. Landmark predictions were used for pose initialization, followed by SSM-based 2D-3D registration using an anatomy-aware weighted optimization strategy that emphasized the transverse and spinous processes. The method was validated against patient-specific CT segmentations registered to the biplanar imaging geometry. The method was evaluated in subjects without significant osseous abnormalities and in pathological patients from an independent center. RESULTS: The proposed network achieved Dice coefficients of 0.991 and 0.989 for anteroposterior and lateral vertebral signal isolation, respectively. The overall 3D reconstruction accuracy was 0.85 ± 0.24 mm. Reconstruction accuracy was 0.80 ± 0.15 mm in Center 1 and 1.32 ± 0.46 mm in the pathological cohort from Center 2. The method maintained stable performance under additive Gaussian noise and across different lumbar postures. Ablation studies confirmed the contributions of signal isolation, landmark-based initialization, and anatomy-aware optimization. CONCLUSION: This robust, high-accuracy automated method precisely reconstructs complex lumbar structures from biplanar X-rays, even for severely pathological vertebrae. Its ability to maintain accuracy under extreme movement and improve reconstruction quality in complex regions highlights its significant potential for clinical diagnosis and surgical planning.