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Addressing the ill-posed inverse problem of reconstructing 3D CT volumes from single 2D radiographs without paired clinical data, this work presents a two-stage blended learning framework that bridges the synthetic-to-real domain gap. The approach couples unsupervised CXR-to-DRR domain adaptation with multi-pass volumetric synthesis, multi-view slice refinement, and progressive transfer learning. Evaluated on LIDC-IDRI, the method outperforms prior baselines by up to 14% in PSNR and 7.6% in SSIM while yielding anatomically coherent volumes from real clinical X-rays.
Clinical 2D X-rays can now yield high-fidelity 3D volumetric CTs without paired real-world training data, beating prior 2D-to-3D synthesis baselines by up to 14% in PSNR through multi-view slice refinement and progressive transfer learning.
Reconstructing volumetric Computed Tomography (CT) from a single 2D chest radiograph (CXR) is an ill-posed inverse problem, further complicated by the scarcity of paired CXR-CT training data. Prior approaches address this by training on Digitally Reconstructed Radiographs (DRRs), which are synthetic projections derived from CT volumes. However, the domain gap between DRRs and real CXRs limits generalization, often resulting in coarse or anatomically inconsistent reconstructions when applied to clinical images. To address this challenging problem, this study introduces a Multi-Pass Multi-View Blended Learning framework for synthesizing high-fidelity volumetric CT directly from real chest X-ray (CXR) images. The proposed approach progressively decomposes the synthesis task into two distinct, complementary learning stages. Stage 1 is an unsupervised CXR-to-DRR Domain Adaptation, while Stage 2 includes three passes, namely, (a) supervised DRR-to-CT Transformation, (b) unsupervised Multi-View Slice Refinement, followed by (c) Progressive Transfer Learning (PTL). With such a blended learning paradigm, the proposed approach mitigates the synthetic-to-real domain gap while enhancing both the structural integrity and anatomical detail of the final output. On the LIDC-IDRI dataset, where paired DRR-CT ground truth is available for quantitative evaluation, the proposed method improves upon prior methods by up to 14% in PSNR and 7.6% in SSIM. The framework successfully generates structurally consistent and anatomically realistic high-fidelity CT volumes from real CXRs, marking a significant advancement toward clinical viability of CT reconstruction from standard radiographic images.
