Document Type

Article

Publication Date

2025

DOI

10.32604/cmes.2025.066165

Publication Title

Computer Modeling in Engineering & Sciences

Volume

144

Issue

1

Pages

1091-1112

Abstract

3D medical image reconstruction has significantly enhanced diagnostic accuracy, yet the reliance on densely sampled projection data remains a major limitation in clinical practice. Sparse-angle X-ray imaging, though safer and faster, poses challenges for accurate volumetric reconstruction due to limited spatial information. This study proposes a 3D reconstruction neural network based on adaptive weight fusion (AdapFusionNet) to achieve high-quality 3D medical image reconstruction from sparse-angle X-ray images. To address the issue of spatial inconsistency in multi-angle image reconstruction, an innovative adaptive fusion module was designed to score initial reconstruction results during the inference stage and perform weighted fusion, thereby improving the final reconstruction quality. The reconstruction network is built on an autoencoder (AE) framework and uses orthogonal-angle X-ray images (frontal and lateral projections) as inputs. The encoder extracts 2D features, which the decoder maps into 3D space. This study utilizes a lung CT dataset to obtain complete three-dimensional volumetric data, from which digitally reconstructed radiographs (DRR) are generated at various angles to simulate X-ray images. Since real-world clinical X-ray images rarely come with perfectly corresponding 3D “ground truth,” using CT scans as the three-dimensional reference effectively supports the training and evaluation of deep networks for sparse-angle X-ray 3D reconstruction. Experiments conducted on the LIDC-IDRI dataset with simulated X-ray images (DRR images) as training data demonstrate the superior performance of AdapFusionNet compared to other fusion methods. Quantitative results show that AdapFusionNet achieves SSIM, PSNR, and MAE values of 0.332, 13.404, and 0.163, respectively, outperforming other methods (SingleViewNet: 0.289, 12.363, 0.182; AvgFusionNet: 0.306, 13.384, 0.159). Qualitative analysis further confirms that AdapFusionNet significantly enhances the reconstruction of lung and chest contours while effectively reducing noise during the reconstruction process. The findings demonstrate that AdapFusionNet offers significant advantages in 3D reconstruction of sparse-angle X-ray images.

Rights

© 2025 The Authors.

This work is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Data Availability

Article states: "The data that support the findings of this study are openly available in The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A completed reference database of lung nodules on CT scans at https://www.cancerimagingarchive.net/collection/lidc-idri/."

Original Publication Citation

Hong, S. Y., Yang, B., Chen, Y., Quan, H., Liu, S., Tang, M. Y., & Tian, J. W. (2025). Adaptive fusion neural networks for sparse-angle X-ray 3D reconstruction. Computer Modeling in Engineering & Sciences, 144(1), 1091-1112. https://doi.org/10.32604/cmes.2025.066165

ORCID

0000-0002-8040-0367 (Liu)

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