High-resolution Computed Tomography (CT) is the gold standard medical imaging technique for bone assessment. However, its clinical use is limited by high radiation dose (8.8 mSv; biplanar X-rays 1.4 mSv), cost, and reduced accessibility. These barriers are particularly significant for patients requiring frequent imaging. This study introduces a novel hybrid framework combining statistical intensity modeling with Deep Learning to reconstruct 3D tibial CT volumes including internal density distributions from biplanar radiographs. The method employs principal component analysis (PCA) to capture intensity variations in a compact latent space and trains a convolutional neural network (CNN) to regress PCA coefficients directly from radiographs. The framework was developed and validated using 60 subjects from the publicly available Korea Institute of Science and Technology Information (KISTI) database. Compared to ground truth CT, it achieved a mean absolute error of 127.17 ± 12.08 Hounsfield Units (HU), a structural similarity index of 0.8558 ± 0.0215, and a peak signal-to-noise ratio of 21.40 ± 0.78 dB. The method has the potential to achieve substantial radiation dose reduction compared to conventional CT while preserving sufficient anatomical detail for potential clinical tasks such as patient-specific implant planning and bone quality triage. However, the actual dose reduction depends on clinical imaging protocols and requires validation through protocol-matched dosimetry on actual radiographs. Moreover, it produces interpretable outputs that reflect anatomical intensity variations (e.g., cortical vs. trabecular regions), demonstrating feasibility for hybrid statistical-Deep Learning bone reconstruction. The proposed pipeline establishes a foundation for reduced-dose 3D bone imaging and offers a pathway toward clinical translation pending validation on real-world radiographic data.