发表机构
Tampere University; Qatar University; Hamad Medical Corporation（坦佩雷大学; 卡塔尔大学; 哈马德医疗公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出多遍多视图混合学习框架，通过域自适应、DRR到CT变换、多视图细化及渐进迁移学习，从真实胸部X射线合成高保真体积CT，在LIDC-IDRI上PSNR提升14%，SSIM提升7.6%。
AI 中文摘要
从单张二维胸部X射线照片（CXR）重建体积计算机断层扫描（CT）是一个不适定的逆问题，且因配对CXR-CT训练数据的稀缺而更加复杂。先前的方法通过在数字重建放射影像（DRR）上训练来解决这一问题，DRR是从CT体积导出的合成投影。然而，DRR与真实CXR之间的域差距限制了泛化能力，在应用于临床图像时常常导致粗糙或解剖结构不一致的重建结果。为解决这一难题，本研究提出了一种多遍多视图混合学习框架，直接从真实胸部X射线（CXR）图像合成高保真体积CT。所提出的方法将合成任务逐步分解为两个不同且互补的学习阶段。第一阶段是无监督的CXR到DRR域自适应，第二阶段包括三个遍次，即（a）有监督的DRR到CT变换，（b）无监督的多视图切片细化，以及（c）渐进式迁移学习（PTL）。通过这种混合学习范式，所提出的方法减轻了合成到真实的域差距，同时增强了最终输出的结构完整性和解剖细节。在LIDC-IDRI数据集上，其中配对DRR-CT真值可用于定量评估，所提出的方法在PSNR上比先前方法提高了高达14%，在SSIM上提高了7.6%。该框架成功地从真实CXR生成了结构一致且解剖上真实的高保真CT体积，标志着从标准放射图像进行CT重建向临床可行性迈出了重要一步。
英文摘要
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.
Comments13 pages, 11 figures
