Open Access

ARTICLE

Adaptive Fusion Neural Networks for Sparse-Angle X-Ray 3D Reconstruction

Shaoyong Hong¹, Bo Yang², Yan Chen², Hao Quan³, Shan Liu⁴, Minyi Tang^5,*, Jiawei Tian^6,*

1 School of Artificial Intelligence, Guangzhou Huashang College, Guangzhou, 511300, China
2 School of Automation, University of Electronic Science and Technology of China, Chengdu, 610054, China
3 Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Via Ponzio 34/5, Milan, 20133, Italy
4 Department of Modelling, Simulation, and Visualization Engineering, Old Dominion University, Norfolk, VA 23529, USA
5 Graduate School of Engineering, ESIGELEC, Av. Galilée, St Etienne du Rouvray, 76801, France
6 Department of Computer Science and Engineering, Hanyang University, Ansan, 15577, Republic of Korea

* Corresponding Authors: Minyi Tang. Email: ; Jiawei Tian. Email:

(This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications)

Computer Modeling in Engineering & Sciences 2025, 144(1), 1091-1112. https://doi.org/10.32604/cmes.2025.066165

Received 31 March 2025; Accepted 19 June 2025; Issue published 31 July 2025

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.

---

Keywords

---