Aug 2026· IEEE journal of biomedical and health informatics· Vol PP, pp. 1-14· 0 citations
Medicine
TL;DR
MAC-DiffCT offers a low-radiation alternative to conventional CT, especially for vulnerable patients requiring repeated imaging and intraoperative scenarios where CT use is constrained, and consistently outperforms existing methods.
Abstract
Sparse-view CT reconstruction aims to synthesize volumetric CT images from a limited number of X-ray projections, reducing radiation dose while maintaining diagnostic quality. However, the substantial cross-modal gap between 2D X-rays and 3D CT volumes presents significant challenges, including uneven distribution of information across different views, artifact issues, and excessive computational costs. Therefore, in this paper, we propose MAC-DiffCT, a multi-scale adaptive conditional diffusion model designed for accurate and efficient CT reconstruction from biplanar X-rays. Our approach first extracts multi-scale 2D features from multi-view X-rays using a UNet-based encoder. A novel Bi-Directional Cross-Attention (BDC-Att) module adaptively fuses features by assigning spatially varying weights to each view. We then introduce a Multi-Scale Feature Sampling (MS-FS) module that projects 3D coordinates onto 2D planes, sample features across scales, and integrates them via a multi-layer perceptron to form a latent structural representation. This 3D structural feature serves as a condition for a latent-space conditional diffusion model, which reconstructs high-quality CT volumes with enhanced anatomical fidelity. An additional signed distance function (SDF) loss is applied to promote structural consistency in the 3D space. Experimental results on both public and private chest datasets demonstrate that MAC-DiffCT consistently outperforms existing methods, achieving the highest reconstruction accuracy with PSNR of 27.68 and 25.43 dB, SSIM of 0.8223 and 0.7598, and the lowest LPIPS of 0.0992 and 0.1322. Downstream evaluation via lung segmentation and an interpretability study further highlight the transparent, explainable, and anatomically grounded nature of our model. MAC-DiffCT offers a low-radiation alternative to conventional CT, especially for vulnerable patients requiring repeated imaging and intraoperative scenarios where CT use is constrained.
Cone-beam computed tomography (CBCT) with sparse projection views offers reduced radiation dose and faster scans but introduces severe streak artifacts and spatial coverage gaps. We address these challenges within a unified framework. First, we replace conventional UNet/ResNet encoders with TransUNet, a hybrid CNN–Transformer architecture that jointly models local details and long-range spatial context. It is adapted to CBCT reconstruction by concatenating multi-scale feature maps and introducing a lightweight attenuation-prediction head. Trans-CBCT outperforms the best baseline by 1.17 dB in PSNR and by 0.0163 in SSIM on LUNA16 with only six projection views. Second, we incorporate a neighbor-aware Point Transformer with explicit 3D positional encodings and a neighbor-aware attention module aggregating information from each point’s k-nearest spatial neighbors to enforce volumetric coherence. The resulting Trans2-CBCT achieves an additional 0.63 dB increase in PSNR and 0.0117 increase in SSIM over Trans-CBCT. In experiments with 6-10 views, Trans-CBCT and Trans2-CBCT consistently outperform all prior methods in both PSNR and SSIM on LUNA16. On the ToothFairy dataset, Trans2-CBCT leads in five of the six measurements, outperforming all baselines in PSNR. These results highlight the effectiveness of combining hybrid CNN–Transformer features with geometry-aware point-based reasoning for sparse-view CBCT reconstruction.
Minmin Yang, Yunhui Zhu, Huantao Ren et al.· Italian National Conference...· 0 citations
This paper proposes K-NeAS, a unified and scalable architecture for automated, multi-material surface reconstruction that replaces independent material networks with a shared latent backbone and introduces a fully differentiable $K$-material sequential soft selector to model an arbitrary number of overlapping tissues.
Daksh K. Shah, Emmanouil Nikolakakis, Razvan V. Marinescu· arXiv.org· 0 citations
Multi-energy CT (MECT) offers unique advantages in material decomposition, tissue characterization, and functional imaging, positioning it as a pivotal direction for next-generation CT. Currently, standardized scanning protocols for MECT have not yet been established. Considering growing public concern over X-ray radiation exposure, we propose a complementary sparse-view scanning protocol tailored for MECT, which reduces radiation dose while maximizing angular coverage. To reconstruct high-quality images from these sparse-view data and ensure algorithmic reliability in practical applications, we introduce an Online Adaptive Reconstruction (OA-Recon) framework that adapts robustly to varying acquisition settings through two designs. First, it adopts a Bayesian adaptation strategy for instance-specific optimization while preserving the learned prior. Second, it incorporates a Frequency-adaptive and Physics-informed Network (FaPiNet) for adaptive feature extraction and acquisition-conditioned feature modulation. In addition, it incorporates a spectral attention mechanism to fully exploit complementary information across energy channels. Experiments on simulated MECT and real mouse PCCT data show that FaPiNet-OA-Recon achieves better performance in suppressing streak artifacts, restoring image details, and maintaining CT-value accuracy. More importantly, OA-Recon demonstrates adaptability to changes in view, spectrum, and anatomy, providing a preliminarily feasible solution for clinical applications of MECT.
Xi Wang, Tong Lin, Jiashun Wang et al.· IEEE Transactions on Medical...· 0 citations
The experimental results demonstrate that MHSG-Model has a good performance on preserving detail information and removing artifacts, which show the potential to be applied in clinical sparse view spectral CT reconstruction.
Qi-Wei Li, Zaifeng Shi, Fanning Kong et al.· Journal of X-Ray Science and...· 0 citations
This work designs an implicit pixel-wise learnable step size to adapt to the spatial gradient heterogeneity of CT images and develops a cross-prompt guiding mechanism to enable inter-domain prompt interaction, which facilitates efficient prompt generation and enhances the convergence stability of the model.
Wenchao Du, Qiao Mu, Huanhuan Cui et al.· IEEE Transactions on Medical...· 0 citations
Three-dimensional (3D) CT reconstruction from dual-view X-ray data is a severely ill-posed inverse problem, due to the substantial loss of structural information caused by the sparsity of the projection data. To address this challenge, we propose a dual-view X-ray-to-CT reconstruction framework based on vector quantized variational autoencoders (VQ-VAEs) and Transformer-based latent code translation. Specifically, a 3D VQ-VAE model is pre-trained to learn discrete latent representations of CT volumes, while a 2D VQ-VAE encodes dual-view X-ray projections into discrete tokens. Two separate Transformers are then employed to transform the 2D discrete representations into top-level and bottom- level 3D latent codes, which are subsequently decoded by the pretrained 3D VAE decoder, yielding an end-to-end mapping from dual-view X-rays to 3D CT volumes. Furthermore, a genetic algorithm (GA) is introduced to optimize the predicted bottom-level 3D latent codes. The proposed method is evaluated on a synthetic paired dataset constructed from the LIDC-IDRI CT database and digital reconstructed radiographs (DRRs). The framework successfully synthesizes structurally plausible 3D CT volumes from only two X-ray views, achieving a structural similarity (SSIM) of 0.7352 and a mean squared error (MSE) of 0.0087. Latent code optimization with the GA further improves structural similarity and reduces reconstruction error. The results demonstrate the potential of the proposed approach for recovering bone structures such as ribs, scapulae, and the sternum.
Guangyu Xu, Juntao Zhang, Bo Yang et al.· International journal of pat...· 0 citations
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