Reconstructing 3D Computed Tomography (CT) images from a few X-ray projections is a highly ill-posed inverse problem due to the loss of volumetric information. We propose PhyDiCT, a training-free framework that integrates a differentiable Physics-based forward model, grounded in the Beer-Lambert law, with a text-conditioned Diffusion as a strong prior to reconstruct 3D lung CT images. We refer to our approach as training-free since the prior model is used without fine-tuning, and our goal is to steer the denoising procedure to generate samples consistent with X-ray observations. We guide the diffusion generation using Split Gibbs sampling to jointly optimize for projection fidelity (reward) and consistency with prior knowledge. Also, we introduce a test-time refinement step that enhances image realism and anatomical coherence. We extensively evaluate our method on publicly available 3D CT datasets using both perceptual and semantic metrics, demonstrating that it surpasses existing plug-and-play diffusion and fully trained reconstruction approaches. Our findings highlight that combining a strong generative prior with the underlying physics of image formation substantially improves reconstruction quality, e.g., 7.5% improvement on SSIM compared to full training methods. Code will be released at https://github.com/batmanlab/PhyDiCT.
Figures & tables
Figure 1: (a): Denoiser stage 1. The text prompt c (findings and impression tokens with <CLS> and <SEP> tokens) conditions the iterative generation of image (x0)0 , which is later used for stage 2. (b): Differentiable X-ray rendering. Real-world setup where multiple X-rays (each ω starting at the same s , ending at multiple p on detector) are used to acquire one entire y on the receiver plane. (c): Overview of reward-guided reconstruction. The shadow illustrates distribution envelope of (x0)t , which shrinks as it approaches p(x∣y) . A denoiser Ft is first applied to move the noisy sample xt (lying outside the manifold) toward the clean data distribution p(x∣c) . Subsequent likelihood step shifts the predicted (x0)t to a new variable zt (maybe lying outside) guided by the reward function to drive it closer to the posterior space p(x∣y) .
Figure 2: Comparison of reconstructed CT based on DiffVox. The first row shows raw slices, and following rows are zoomed-in regions. Arrows on first row show minor artifacts, further suppressed with EC. Second row ( bounding box ) highlights that our method accurately reconstructs subtle pathology, while avoiding overemphasis of disease compared to Prior. Third row ( arrows ) highlights the anatomical reconstruction of fissure lines.
Computed tomography (CT) provides rich 3D anatomical detail but is often constrained by high radiation exposure, substantial costs, and limited availability. Standard chest X-rays are cost-effective and widely accessible, but provide only 2D projections with limited pathological information. Reconstructing 3D CT volumes from 2D X-rays could markedly increase diagnostic accessibility, yet existing methods rely predominantly on synthetic X-ray projections, limiting clinical generalization. We propose AXON, a multi-stage diffusion-based framework that reconstructs 3D CT volumes directly from real X-rays with substantially improved fidelity over existing approaches. AXON follows a coarse-to-fine paradigm: a Brownian Bridge diffusion model first captures global anatomical structure, and a ControlNet-guided refinement stage then enhances local intensity detail and anatomical realism. To alleviate the depth ambiguity inherent in 2D-to-3D reconstruction, AXON incorporates bi-planar X-ray views, enabling more accurate spatial reasoning and structural recovery. A dedicated super-resolution module further increases the spatial resolution of the generated volumes. Experiments on public and external datasets show that AXON consistently surpasses state-of-the-art approaches while generalizing across diverse clinical distributions. At our highest-resolution bi-planar setting, AXON achieves an 11.9% improvement in PSNR and an 11.0% increase in SSIM over the strongest baseline evaluated at that resolution. In the 1283 single-planar setting, it maintains a lead of 7.8% in PSNR on LIDC-IDRI, with larger margins on the external clinical dataset of 8.0% in PSNR and 16.9% in SSIM. Our code is available at https://github.com/ai-med/AXON/.
Martin Rath, Morteza Ghahremani, Yitong Li +3
Technical University of Munich (TUM), Germany · Munich Center for Machine Learning (MCML), Germany · University of Western Australia (UWA), Australia
Computed tomography (CT) throughput is limited by scan time, which grows with both the number of projections acquired and the detector integration time for each projection. Reconstructing high-quality volumes from sparse-view or low-dose measurements therefore depends on using an informative prior, typically a neural network trained for one specific scan setting and retrained whenever the modality, geometry, or material changes. We investigate whether a single diffusion model trained across several imaging domains can instead serve as a reusable prior for heterogeneous CT reconstruction problems. We evaluate the proposed method using the same diffusion visual transformer model and normalized denoising strength on three datasets that differ in modality, beam geometry, material, and degradation type, spanning additively manufactured metal parts and concrete microstructure imaged with cone-bean X-ray CT and parallel-beam neutron CT respectively. The proposed method improves upon analytic reconstructions in all three cases, demonstrating transferability across the evaluated problems and providing a step toward a reusable foundation prior for heterogeneous CT reconstruction.
Computed Tomography (CT) is a widely used imaging modality in medical and industrial applications. To limit radiation exposure and measurement time, there is a growing interest in sparse-view CT, where the number of projection views is significantly reduced. Deep neural networks have shown great promise in improving reconstruction quality in sparse-view CT, especially generative diffusion models. However, these methods struggle to scale to large 3D volumes due to several reasons: (i) the high memory and computational requirements of 3D models, (ii) the lack of large 3D training datasets, and (iii) the inconsistencies across slices when using 2D models independently on each slice. We overcome these limitations and scale diffusion-based sparse-view CT reconstruction to large 3D volumes by combining conditional diffusion with explicit data consistency. We propose Conditional Diffusion Posterior Alignment (CDPA) to enable scalable 3D sparse-view CT reconstruction. A 2D U-Net diffusion model is conditioned on an initial 3D reconstruction to improve inter-slice consistency, combined with data-consistency alignment to match measured projections. Experiments on synthetic and real Cone Beam CT (CBCT) data show state-of-the-art performance, with ablations that confirm the synergistic effects of the proposed pipeline. Finally, we show that the same principles also strengthen fast denoising U-Nets, yielding near-diffusion quality at a fraction of the computational cost.
Luis Barba, Johannes Kirschner, Benjamin Bejar
Swiss Data Science Center (SDSC) in Paul Scherrer Institute (PSI), Villigen, Switzerland. · Swiss Data Science Center (SDSC) and ETH Zurich, Switzerland. · Swiss Data Science Center (SDSC) and Paul Scherrer Institute (PSI), Villigen, Switzerland.