Cone-Beam Computed Tomography

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37 papers in the last four weeks, up 147% on the four weeks before. 0.4% of all new papers.

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Latest papers 267

Mar 16, 2026cs.CV

ILV: Iterative Latent Volumes for Fast and Accurate Sparse-View CT Reconstruction

A long-term goal in CT imaging is to achieve fast and accurate 3D reconstruction from sparse-view projections, thereby reducing radiation exposure, lowering system cost, and enabling timely imaging in clinical workflows. Recent feed-forward approaches have shown strong potential toward this overarching goal, yet their results still suffer from artifacts and loss of fine details. In this work, we introduce Iterative Latent Volumes (ILV), a feed-forward framework that integrates data-driven priors with classical iterative reconstruction principles to overcome key limitations of prior feed-forward models in sparse-view CBCT reconstruction. At its core, ILV constructs an explicit 3D latent volume that is repeatedly updated by conditioning on multi-view X-ray features and the learned anatomical prior, enabling the recovery of fine structural details beyond the reach of prior feed-forward models. In addition, we develop and incorporate several key architectural components, including an X-ray feature volume, group cross-attention, efficient self-attention, and view-wise feature aggregation, that efficiently realize its core latent volume refinement concept. Extensive experiments on a large-scale dataset of approximately 14,000 CT volumes demonstrate that ILV significantly outperforms existing feed-forward and optimization-based methods in both reconstruction quality and speed. These results show that ILV enables fast and accurate sparse-view CBCT reconstruction suitable for clinical use. The project page is available at: https://sngryonglee.github.io/ILV/.

Seungryong Lee, Woojeong Baek, Joosang Lee +1

Sungkyunkwan University · Yonsei University

Cone-Beam Computed Tomography Image Reconstruction Sparse-View Feed-Forward 3D Reconstruction Latent Space

Mar 6, 2026cs.CV

Active View Selection with Perturbed Gaussian Ensemble for Tomographic Reconstruction

Sparse-view computed tomography (CT) is critical for reducing radiation exposure to patients. Recent advances in radiative 3D Gaussian Splatting (3DGS) have enabled fast and accurate sparse-view CT reconstruction. Despite these algorithmic advancements, practical reconstruction fidelity remains fundamentally bounded by the quality of the captured data, raising the crucial yet underexplored problem of X-ray active view selection. Existing active view selection methods are primarily designed for natural-light scenes and fail to capture the unique geometric ambiguities and physical attenuation properties inherent in X-ray imaging. In this paper, we present Perturbed Gaussian Ensemble, an active view selection framework that integrates uncertainty modeling with sequential decision-making, tailored for X-ray Gaussian Splatting. Specifically, we identify low-density Gaussian primitives that are likely to be uncertain and apply stochastic density scaling to construct an ensemble of plausible Gaussian density fields. For each candidate projection, we measure the structural variance of the ensemble predictions and select the one with the highest variance as the next best view. Extensive experimental results on arbitrary-trajectory CT benchmarks demonstrate that our density-guided perturbation strategy effectively eliminates geometric artifacts and consistently outperforms existing baselines in progressive tomographic reconstruction under unified view selection protocols.

Yulun Wu, Ruyi Zha, Wei Cao +3

University of Illinois Urbana-Champaign · Australian National University · Johns Hopkins University

Cone-Beam Computed Tomography Sparse-View Gaussian Splatting Gaussian Primitives

Mar 2, 2026cs.CV

CTForensics: A Comprehensive Dataset and Method for AI-Generated CT Image Detection

Recent advances in generative AI have made synthetic Computed Tomography (CT) images increasingly realistic, enabling promising applications in medical data augmentation while raising serious concerns about clinical safety and data trustworthiness. Detecting AI-generated CT images remains challenging for two key reasons: existing benchmarks cover only limited generation sources, and many detectors are adapted from natural-image forensics without explicitly modeling CT-specific imaging properties. In this paper, we introduce CTForensics, a dataset for detecting AI-generated CT images. CTForensics contains 75,990 2D CT images, including a dedicated test benchmark of 29,990 balanced authentic and generated samples from ten representative CT generative models spanning GAN-based and diffusion-based paradigms. We further propose the Enhanced Spatial-Frequency CT Forgery Detector (ESF-CTFD), a CT-oriented CNN framework built around a Wavelet-Enhanced Central Stem, Multi-Scale Spatial Aggregation, and a Frequency-Aware Prediction Block. The Wavelet-Enhanced Central Stem enhances local intensity correlations and high-frequency residuals, Multi-Scale Spatial Aggregation aligns anatomical features across resolutions with lightweight residual units, and the Frequency-Aware Prediction Block models global spectral artifacts. Extensive experiments on CTForensics show that ESF-CTFD achieves 96.01% mAcc and 99.96% mAP, outperforming existing methods and maintaining strong robustness under realistic perturbations with only a 0.99% average drop. Codes will be available at https://github.com/liyih/CTForensics.

Yiheng Li, Zichang Tan, Guoqing Xu +3

School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China · MAIS, Institute of Automation, Chinese Academy of Sciences, Beijing, China · Sangfor Technologies Inc., Shenzhen, China +1

Cone-Beam Computed Tomography Multimedia Forensics Face Forgery Detection Generative Adversarial Network

Feb 27, 2026eess.IV

Efficient Flow Matching for Sparse-View CT Reconstruction

Generative models, particularly Diffusion Models (DM), have shown strong potential for Computed Tomography (CT) reconstruction serving as expressive priors for solving ill-posed inverse problems. However, diffusion-based reconstruction relies on Stochastic Differential Equations (SDEs) for forward diffusion and reverse denoising, where such stochasticity can interfere with repeated data consistency corrections in CT reconstruction. Since CT reconstruction is often time-critical in clinical and interventional scenarios, improving reconstruction efficiency is essential. In contrast, Flow Matching (FM) models sampling as a deterministic Ordinary Differential Equation (ODE), yielding smooth trajectories without stochastic noise injection. This deterministic formulation is naturally compatible with repeated data consistency operations. Furthermore, we observe that FM-predicted velocity fields exhibit strong correlations across adjacent steps. Motivated by this, we propose an FM-based CT reconstruction framework (FMCT) and an efficient variant (EFMCT) that reuses previously predicted velocity fields over consecutive steps to substantially reduce the number of Neural network Function Evaluations (NFEs), thereby improving inference efficiency. We provide theoretical analysis showing that the error introduced by velocity reuse is bounded when combined with data consistency operations. Extensive experiments demonstrate that FMCT/EFMCT achieve competitive reconstruction quality while significantly improving computational efficiency compared with diffusion-based methods. The codebase is open-sourced at https://github.com/EFMCT/EFMCT.

Jiayang Shi, Lincen Yang, Zhong Li +3

Centrum Wiskunde en Informatica · LIACS, Leiden University · Great Bay University +1

Cone-Beam Computed Tomography Conditional Flow Matching Velocity Field Ordinary Differential Equations

Feb 23, 2026cs.CV

Segmenting Low-Contrast XCTs of Concrete: An Unsupervised Approach

X-Ray Computed Tomography (XCT) is a compelling tool in experimental mechanics, capable of non-destructively extracting information pertaining to the internal morphology of materials. For materials with random heterogeneous morphology such as concrete, such information is of particular relevance since it allows for studies of morphology-related behaviour and for predictive modelling. Nevertheless, XCT images require semantic segmentation for practical usage. Here, concrete poses a unique challenge due to the similar X-ray attenuation coefficients of aggregates and mortar, which result in low contrast between the two phases in the ensuing XCT images. As such, purely intensity-dependent semantic segmentation tools remain unfeasible. While vision transformers (ViTs) and convolutional neural networks (CNNs) are proven techniques for semantic segmentation in such challenging cases, they typically require labelled training data, which is often unavailable for concrete or resource-intensive to obtain, thereby limiting their relevance. To address this challenge, a self-annotation technique is presented here that leverages superpixel algorithms to identify perceptually similar local regions in an image and relates them to the global context by utilizing the receptive field of a CNN-based model. This enables the model to learn a global-local relationship in the images and facilitates the identification of semantically similar structures. When evaluated against manually annotated ground truth on out-of-distribution data, the proposed methodology consistently outperformed direct greyscale thresholding across all pertinent metrics, demonstrating improved discernibility between aggregates and mortar, and providing the most favourable balance of sensitivity and precision for aggregate-phase identification.

Kaustav Das, Gaston Rauchs, Jan Sykora +1

Department of Mechanics, Faculty of Civil Engineering, Czech Technical University in Prague, Thákurova 7, 166 29 Prague 6, Czech Republic · Structural Composites Unit, Luxembourg Institute of Science and Technology (LIST), 5 avenue des Hauts-Fourneaux L-4362 Esch-sur-Alzette, Luxembourg

Multi Organ Computed Tomography Segmentation Semantic Segmentation Cone-Beam Computed Tomography High-Order Correlations

Feb 9, 2026cs.CV

Artifact Reduction in Undersampled 3D Cone-Beam CTs using a Hybrid 2D-3D CNN Framework

Undersampled CT volumes minimize acquisition time and radiation exposure but introduce artifacts degrading image quality and diagnostic utility. Reducing these artifacts is critical for high-quality imaging. We propose a computationally efficient hybrid deep-learning framework that combines the strengths of 2D and 3D models. First, a 2D U-Net operates on individual slices of undersampled CT volumes to extract feature maps. These slice-wise feature maps are then stacked across the volume and used as input to a 3D decoder, which utilizes contextual information across slices to predict an artifact-free 3D CT volume. The proposed two-stage approach balances the computational efficiency of 2D processing with the volumetric consistency provided by 3D modeling. The results show substantial improvements in inter-slice consistency in coronal and sagittal direction with low computational overhead. This hybrid framework presents a robust and efficient solution for high-quality 3D CT image post-processing. The code of this project can be found on github: https://github.com/J-3TO/2D-3DCNN_sparseview/.

Johannes Thalhammer, Tina Dorosti, Sebastian Peterhansl +3

Chair of Biomedical Physics, Department of Physics, School of Natural Sciences · Munich Institute of Biomedical Engineering · Institute for Diagnostic and Interventional Radiology, School of Medicine and Health, TUM Klinikum +2

Cone-Beam Computed Tomography U-Net

Feb 4, 2026quant-ph

Instance-optimal high-precision shadow tomography with few-copy measurements: A metrological approach

We study the sample complexity of shadow tomography in the high-precision regime under realistic measurement constraints. Given an unknown

d

-dimensional quantum state

ρ

and a known set of observables

\{O_i\}_{i=1}^m

, the goal is to estimate expectation values

\{\mathrm{tr}(O_iρ)\}_{i=1}^m

to accuracy

ε

in

L_p

-norm, using possibly adaptive measurements that act on

O(\mathrm{polylog}(d))

number of copies of

ρ

at a time. We focus on the regime where

ε

is below an instance-dependent threshold. Our main contribution is an instance-optimal characterization of the sample complexity as

\tildeΘ(Γ_p/ε^2)

, where

Γ_p

is a function of

\{O_i\}_{i=1}^m

defined via an optimization formula involving the inverse Fisher information matrix. Previously, tight bounds were known only in special cases, e.g. Pauli shadow tomography with

L_\infty

-norm error. Concretely, we first analyze a simpler oblivious variant where the goal is to estimate an observable of the form

\sum_{i=1}^m α_i O_i

with

\|α\|_q = 1

(where

q

is dual to

p

) revealed after the measurement. For single-copy measurements, we obtain a sample complexity of

Θ(Γ^{\mathrm{ob}}_p/ε^2)

. We then show

\tildeΘ(Γ_p/ε^2)

is necessary and sufficient for the original problem, with the lower bound applying to unbiased, bounded estimators. Our upper bounds rely on a two-step algorithm combining coarse tomography with local estimation. Notably,

Γ^{\mathrm{ob}}_\infty = Γ_\infty

. In both cases, allowing

c

-copy measurements improves the sample complexity by at most

Ω(1/c)

. Our results establish a quantitative correspondence between quantum learning and metrology, unifying asymptotic metrological limits with finite-sample learning guarantees.

Senrui Chen, Weiyuan Gong, Sisi Zhou

IQIM, California Institute of Technology · SEAS, Harvard University · Perimeter Institute

Optimal Sample Complexity Sample Complexity Quantum Learning Qubit Measures Finite Time Analysis+1

Jan 27, 2026physics.geo-ph

Machine learning enhanced data assimilation framework for multiscale carbonate rock characterization

Carbonate reservoirs offer significant capacity for subsurface carbon storage, oil production, and underground hydrogen storage. X-ray computed tomography (X-ray CT) coupled with numerical simulations is commonly used to investigate the multiphase flow behaviors in carbonate rocks. Carbonates exhibit pore size distribution across scales, hindering the comprehensive investigation with conventional X-ray CT images. Imaging samples at both macro and micro-scales (multi-scale imaging) proved to be a viable option in this context. However, multi-scale imaging faces two key limitations: the trade-off between field of view and voxel size necessitates resource-intensive imaging, while multi-scale multi-physics numerical simulations on resulting digital models incur prohibitive computational costs. To address these challenges, we propose a machine learning-enhanced data assimilation framework that leverages experimental drainage relative permeability measurements to achieve efficient characterization of micro-scale structures, delivering a data-driven solution toward a high-fidelity multiscale digital rock modeling. We train a dense neural network (DNN) as a proxy to a multi-scale pore network simulator and couple it with an ensemble smoother with multiple data assimilation (ESMDA) algorithm. DNN-ESMDA framework simultaneously infers the CO2-brine drainage relative permeability of microporosity phases with associated uncertainty estimation, revealing the relative importance of each rock phase and guiding future characterization. Our DNN-ESMDA framework achieves a computational speedup, reducing inference time from thousands of hours to seconds compared with the usage of conventional multiscale numerical simulation. Given this computational efficiency and applicability, the machine learning-enhanced ESMDA framework presents a generalizable approach for characterizing multiscale carbonate rocks.

Zhenkai Bo, Ahmed H. Elsheikh, Hannah P. Menke +5

Institute of GeoEnergy Engineering, Heriot-Watt University, Edinburgh, EH14 4AS, United Kingdom · Department of Geoscience & Engineering, Technical University of Delft, Delft, The Netherlands · PETRONAS Research Sdn Bhd, Bangi, Selangor, Malaysia

Porous Media Lithology Classification Cone-Beam Computed Tomography Deep Learning Framework Data Assimilation

Jan 21, 2026cs.CV

Tracing 3D Anatomy in 2D Strokes: A Multi-Stage Projection Driven Approach to Cervical Spine Fracture Identification

Cervical spine fractures require rapid and accurate diagnosis, yet automatic CT interpretation remains challenging as subtle injuries must be assessed across large 3D volumes. We ask whether full 3D vertebra segmentation is necessary for automated fracture recognition, or whether vertebra masks approximated from 2D projections can preserve sufficient diagnostic context. We propose an end-to-end pipeline that localizes the cervical spine, estimates C1-C7 vertebra masks from optimized 2D projections, and uses the resulting vertebra-level volumes for downstream fracture classification. A YOLOv8 detector first localizes spine regions of interest from multi-view variance projections, achieving a 3D mean Intersection over Union of 94.45%. Multi-label vertebra segmentation is then performed with a DenseNet121-Unet on energy-based sagittal and coronal projections, attaining a mean Dice score of 87.86%. The predicted 2D masks are back-projected and fused into approximate 3D masks for each vertebra to extract volumes of interest from the original CT. These volumes are analyzed by an ensemble of 2.5D spatio-sequential CNN-Transformer models, yielding vertebra-level and patient-level F1 scores of 68.15 and 82.26, area under the receiver operating characteristic curve of 91.62 and 90.95, and area under the precision-recall curve of 75.60 and 92.00, respectively. The projection-derived volumes achieved fracture-recognition performance comparable to a full 3D-segmentation baseline, while shifting the vertebra segmentation stage into a lower-dimensional domain. Saliency-based explainability and interobserver variability analysis further examine interpretability and reliability. Overall, the results indicate that projection-based mask approximation is a viable proxy for full 3D vertebra segmentation in cervical fracture recognition.

Fabi Nahian Madhurja, Rusab Sarmun, Muhammad E. H. Chowdhury +3

Department of Computer Science and Engineering, BRAC University, Dhaka 1212, Bangladesh · Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka 1000, Bangladesh · Department of Electrical Engineering, Qatar University, Doha 2713, Qatar +1

Multi Organ Computed Tomography Segmentation Cone-Beam Computed Tomography Anatomy Volume

Jan 1, 2026cs.CV

TotalFM: An Organ-Separated 3D-CT Foundation Model Leveraging Large-Scale Routine Clinical Radiology Data

While foundation models in radiology are expected to be applied to various clinical tasks, computational cost constraints remain a major challenge when training on 3D-CT volumetric data. In this study, we propose TotalFM, a radiological foundation model that efficiently learns the correspondence between 3D-CT images and linguistic expressions based on the concept of organ separation, utilizing a large-scale dataset of 140,000 series. By automating the creation of organ volume and finding-sentence pairs through segmentation techniques and Large Language Model (LLM)-based radiology report processing, and by combining self-supervised pre-training via VideoMAE with contrastive learning using volume-text pairs, we aimed to balance computational efficiency and representation capability. In zero-shot organ-wise lesion classification tasks, the proposed model achieved higher F1 scores in 83% (5/6) of organs compared to CT-CLIP and 64% (9/14) of organs compared to Merlin. These results suggest that the proposed model exhibits high generalization performance in a clinical evaluation setting using actual radiology report sentences. Furthermore, in zero-shot finding-wise lesion classification tasks, our model achieved a higher AUROC in 83% (25/30) of finding categories compared to Merlin. We also confirmed performance comparable to existing Vision-Language Models (VLMs) in radiology report generation tasks. Our results demonstrate that the organ-separated learning framework can serve as a realistic and effective design guideline for the practical implementation of 3D-CT foundation models. The source code and pretrained models are publicly available at https://github.com/jichi-labo/TotalFM.

Kohei Yamamoto, Tomohiro Kikuchi

Department of Radiology, Jichi Medical University · Data Science Center, Jichi Medical University

3D Foundation Models Cone-Beam Computed Tomography Radiology Report Generation Lesion Foundation Model Volume

Nov 18, 2025cs.CV

Iterative Diffusion-Refined Neural Attenuation Fields for Multi-Source Stationary CT Reconstruction: NAF Meets Diffusion Model

Multi-source stationary computed tomography (CT) has recently attracted attention for its ability to achieve rapid image reconstruction, making it suitable for time-sensitive clinical and industrial applications. However, practical systems are often constrained by ultra-sparse-view sampling, which significantly degrades reconstruction quality. Traditional methods struggle under ultra-sparse-view settings, where interpolation becomes inaccurate and the resulting reconstructions are unsatisfactory. To address this challenge, this study proposes Diffusion-Refined Neural Attenuation Fields (Diff-NAF), an iterative framework tailored for multi-source stationary CT under ultra-sparse-view conditions. Diff-NAF combines a Neural Attenuation Field representation with a dual-branch conditional diffusion model. The process begins by training an initial NAF using ultra-sparse-view projections. New projections are then generated through an Angle-Prior Guided Projection Synthesis strategy that exploits inter view priors, and are subsequently refined by a Diffusion-driven Reuse Projection Refinement Module. The refined projections are incorporated as pseudo-labels into the training set for the next iteration. Through iterative refinement, Diff-NAF progressively enhances projection completeness and reconstruction fidelity under ultra-sparse-view conditions, ultimately yielding high-quality CT reconstructions. Experimental results on multiple simulated 3D CT volumes and real projection data demonstrate that Diff-NAF achieves the best performance under ultra-sparse-view conditions.

Jiancheng Fang, Shaoyu Wang, Junlin Wang +3

School of Information Engineering, Nanchang University, Nanchang 330031, China

Image Reconstruction Cone-Beam Computed Tomography Iterative

Oct 28, 2025cs.IT

Robustness to Sparse Adversarial Corruption in Arbitrary Linear Measurements: Beyond Exact Recovery

Recovery from linear measurements under sparse adversarial corruption is typically formulated as an exact-recovery problem: one seeks structural conditions on

\mathbf{A}

(e.g., restricted isometry property) guaranteeing unique recovery of

\mathbf{x}^\star

from

\mathbf{y} = \mathbf{A}\mathbf{x}^\star + \mathbf{e}

with

\|\mathbf{e}\|_0 \leq q

. However, these guarantees provide no guidance once exact recovery fails. This limitation obscures simple robustness phenomena -- for instance, repeated rows in

\mathbf{A}

can preserve nontrivial information about

\mathbf{x}^\star

under sparse corruption. In this paper, we study what information about

\mathbf{x}^\star

can be \emph{uniformly} recovered from

\mathbf{y} = \mathbf{A}\mathbf{x}^\star + \mathbf{e}

for arbitrary

\mathbf{A}\in\mathbb{R}^{m\times n}

and \emph{any}

q

-sparse

\mathbf{e}

. We show that the robust information is precisely

\mathbf{x}^\star + \ker(\mathbf{U})

, where

\mathbf{U}

is the orthogonal projection onto the intersection of rowspaces of all submatrices of

\mathbf{A}

obtained by deleting

2q

rows. This clarifies how the row structure of

\mathbf{A}

governs whether a

q

-sparse corruption allows exact, partial, or only trivial recovery. We further prove every

\mathbf{x}

minimizing

\|\mathbf{y} - \mathbf{A} \mathbf{x}\|_0

belongs to

\mathbf{x}^\star + \ker(\mathbf{U})

, yielding a constructive approach to recover this set. For i.i.d. Gaussian matrices, we establish a sharp phase transition between exact and trivial recovery. We sketch two applications: robust network tomography and signal reconstruction from oversampled DCT.

Vishal Halder, Alexandre Reiffers-Masson, Abdeldjalil Aïssa-El-Bey +1

IMT Atlantique, Lab-STICC, CNRS UMR 6285, Brest, France · Department of Computer Science and Automation, Indian Institute of Science, Bengaluru, India

Tensor Completion Sparsity Reconstruction Error Adversarial Corruption Gaussian Primitives Cone-Beam Computed Tomography

Oct 12, 2025cs.CV

Structured Spectral Graph Representation Learning for Multi-label Abnormality Analysis from 3D CT Scans

With the growing volume of CT examinations, there is an increasing demand for automated tools such as organ segmentation, abnormality detection, and report generation to support radiologists in managing their clinical workload. Multi-label classification of 3D Chest CT scans remains a critical yet challenging problem due to the complex spatial relationships inherent in volumetric data and the wide variability of abnormalities. Existing methods based on 3D convolutional neural networks struggle to capture long-range dependencies, while Vision Transformers often require extensive pre-training on large-scale, domain-specific datasets to perform competitively. In this work, we propose a 2.5D alternative by introducing a new graph-based framework that represents 3D CT volumes as structured graphs, where axial slice triplets serve as nodes processed through spectral graph convolution, enabling the model to reason over inter-slice dependencies while maintaining complexity compatible with clinical deployment. Our method, trained and evaluated on 3 datasets from independent institutions, achieves strong cross-dataset generalization, and shows competitive performance compared to state-of-the-art visual encoders. We further conduct comprehensive ablation studies to evaluate the impact of various aggregation strategies, edge-weighting schemes, and graph connectivity patterns. Additionally, we demonstrate the broader applicability of our approach through transfer experiments on automated radiology report generation and abdominal CT data.

Theo Di Piazza, Carole Lazarus, Olivier Nempont +1

INSA Lyon, University of Lyon, CNRS, INSERM, CREATIS UMR 5220, U1294, Villeurbanne, France · Hospices Civil de Lyon, Lyon, France · Philips Clinical Informatics, Innovation Paris, France

Cone-Beam Computed Tomography Cnn-Transformer Tradeoff Radiology Report Generation Spectral

Aug 25, 2025cs.CV

InSituRes: A Physics-Informed Same-Grid Model for Enhanced Dynamic X-ray Micro-CT Reconstructions

X-ray micro-computed tomography (micro-CT) provides non-destructive three-dimensional (3D) imaging of porous material microstructures. In situ experiments, including mechanical loading and reactive transport, increasingly require dynamic four-dimensional (4D) imaging with volumes repeatedly acquired during experiments. However, rapid acquisition typically requires fewer projections, shorter exposures, or reduced fields of view, producing reconstructions with noise, blur, and artifacts that obscure pores, microcracks, and interfaces. To address this challenge, this study introduces InSituRes, a physics-informed same-grid volumetric enhancement framework for fast dynamic X-ray micro-CT imaging of temporally evolving materials. InSituRes maps fast-acquisition volumes to higher-quality long-acquisition reconstructions using paired scans of the same specimens. The model integrates 3D convolutional feature extraction with slice-wise transformer attention to capture local and broader in-plane context. A learnable forward degradation model approximates rapid acquisition effects, including spatial blurring, intensity scaling differences, and signal-dependent noise. During training, reconstructed volumes should match high-quality reference scans and reproduce observed fast acquisition data after propagation through the forward model, imposing a physics-guided consistency constraint. Experiments on unseen micro-CT datasets demonstrate improved reconstruction fidelity and enhanced visibility of fine microstructural features relative to conventional interpolation and learning-based enhancement approaches. The framework supports quantitative interpretation of fast 4D X-ray micro-CT scans of evolving materials.

Qinyi Tian, Andrea Bisciotti, Soniya Tiwari +2

Department of Civil & Environmental Engineering, Duke University, Durham, North Carolina, 27708, USA · Department of Physics and Earth Sciences, University of Ferrara, Via Saragat 1, Ferrara 44141, Italy · Department of Mechanical Engineering & Materials Science, Duke University, Durham, North Carolina, 27708, USA

X-Ray Cone-Beam Computed Tomography Porous Media Reconstruction Fidelity Physics-Informed Learning House

Aug 24, 2025cs.CV

FoundDiff: Foundational Diffusion Model for Generalizable Low-Dose CT Denoising

Low-dose computed tomography (CT) denoising is crucial for reduced radiation exposure while ensuring diagnostically acceptable image quality. Despite significant advancements driven by deep learning (DL) in recent years, existing DL-based methods, typically trained on a specific dose level and anatomical region, struggle to handle diverse noise characteristics and anatomical heterogeneity during varied scanning conditions, limiting their generalizability and robustness in clinical scenarios. In this paper, we propose FoundDiff, a foundational diffusion model for unified and generalizable LDCT denoising across various dose levels and anatomical regions. FoundDiff employs a two-stage strategy: (i) dose-anatomy perception and (ii) adaptive denoising. First, we develop a dose- and anatomy-aware contrastive language-image pre-training model (DA-CLIP) to achieve robust dose and anatomy perception by leveraging specialized contrastive learning strategies to learn continuous representations that quantify ordinal dose variations and identify salient anatomical regions. Second, we design a dose- and anatomy-aware diffusion model (DA-Diff) to perform adaptive and generalizable denoising by synergistically integrating the learned dose and anatomy embeddings from DA-CLIP into diffusion process via a novel dose and anatomy conditional block (DACB) based on Mamba. Extensive experiments on a large simulated multi-dose CT dataset spanning three anatomical regions, together with cross-dataset evaluations on Mayo-2016, CQ500, and piglet datasets, demonstrate superior denoising performance and strong generalization to unseen dose levels and anatomical regions. The codes and models are available at https: //github.com/hao1635/FoundDiff.

Zhihao Chen, Qi Gao, Zilong Li +4

Institute of Science and Technology for Brain-inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China · Shanghai Key Lab of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai 200433, China · School of Cyber Science and Engineering, Sichuan University, Chengdu, Sichuan 610065, China +1

Positron Emission Tomography Cone-Beam Computed Tomography Iterative Denoising Methods Diffusion Models Fetch

Jul 15, 2025cs.CV

Trexplorer Super: Topologically Correct Centerline Tree Tracking of Tubular Objects in CT Volumes

Tubular tree structures, such as blood vessels and airways, are essential in human anatomy and accurately tracking them while preserving their topology is crucial for various downstream tasks. Trexplorer is a recurrent model designed for centerline tracking in 3D medical images but it struggles with predicting duplicate branches and terminating tracking prematurely. To address these issues, we present Trexplorer Super, an enhanced version that notably improves performance through novel advancements. However, evaluating centerline tracking models is challenging due to the lack of public datasets. To enable thorough evaluation, we develop three centerline datasets, one synthetic and two real, each with increasing difficulty. Using these datasets, we conduct a comprehensive evaluation of existing state-of-the-art (SOTA) models and compare them with our approach. Trexplorer Super outperforms previous SOTA models on every dataset. Our results also highlight that strong performance on synthetic data does not necessarily translate to real datasets. The code and datasets are available at https://github.com/RomStriker/Trexplorer-Super.

Roman Naeem, David Hagerman, Jennifer Alvén +2

Chalmers University of Technology, 412 96 Gothenburg, Sweden

Cone-Beam Computed Tomography Spatiotemporal Tube Recurrent Model

Jul 9, 2025eess.IV

Fast Equivariant Imaging: Accelerating Unsupervised Learning and Model Adaptation via Inexact Splitting

In this work, we propose Fast Equivariant Imaging (FEI), a novel unsupervised learning framework to rapidly and efficiently train deep imaging networks without ground-truth data. FEI reformulates the EI objective through an inexact variable-splitting scheme, decoupling network training from an auxiliary restoration step implemented with a plug-and-play denoiser, this novel unsupervised scheme shows superior efficiency and performance compared to the standard Equivariant Imaging paradigm. In particular, our FEI schemes achieve an order-of-magnitude (10x) acceleration over standard EI on training U-Net for X-ray CT reconstruction and image inpainting, with improved generalization performance. Beyond offline training, the proposed scheme also enables efficient test-time adaptation of a pretrained model to individual samples, to secure further performance improvements. Extensive experiments show that the proposed approach provides a noticeable efficiency and performance gain over existing unsupervised methods and model adaptation techniques.

Guixian Xu, Jinglai Li, Junqi Tang

School of Mathematics, University of Birmingham, Birmingham B15 2TT, United Kingdom

U-Net Image Reconstruction Imagenet Parameter-Efficient Adaptation Unsupervised Cone-Beam Computed Tomography

Jun 29, 2025eess.IV

Region-Aware Multimodal Large Language Model via SlowFast Tokenization and Pseudo-Mask Guidance for 3D CT Report Generation

Current CT report generation frameworks predominantly rely on global feature representations, often failing to capture region-specific details and potentially missing certain abnormalities. To overcome this limitation, we propose MedRegion-CT, a region-focused multimodal large language model framework featuring three key innovations. First, we revisit the SlowFast strategy to jointly model global and fine-grained information and adapt it to the medical domain via a Region-based SlowFast Tokenizer that extracts tokens guided by clinically meaningful regions. Second, generated pseudo-masks guide the model to attend to diagnostically important anatomical regions, facilitating a systematic understanding of the overall scan context. Third, quantitative lesion information, including size, diameter, and spatial location, is encoded as structured textual prompts, enabling context-aware and clinically informed report generation. To enable rigorous evaluation, we validate our framework on multi-institutional structured report generation benchmarks. Experimental results demonstrate that MedRegion-CT achieves state-of-the-art performance, outperforming existing approaches in both linguistic quality and clinical accuracy. All code is publicly available at: https://github.com/babbu3682/MedRegion-CT.

Sunggu Kyung, Jinyoung Seo, Hyunseok Lim +7

Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea · University of Ulsan College of Medicine, Seoul, Republic of Korea · Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea

Radiology Report Generation Cone-Beam Computed Tomography Multimodal Clinical Data Multimodal Large Language Models Semantic Region

May 31, 2025cs.CV

From Alignment to Synthesis: Contrastive Volumetric Grounding for Text-to-CT Generation

Generating semantically controllable 3D CT volumes from radiology reports requires more than a rich text encoder, it requires vision-language alignment grounded in volumetric space. Existing Text-to-CT approaches condition generation on encoders pretrained with language only or 2D vision-language objectives, providing conditioning signals that are linguistically expressive but volumetrically blind. We argue this is a structural limitation: the quality of 3D vision-language alignment, not the richness of the text encoder, is the primary bottleneck for semantic controllability in volumetric diffusion models. To address this, we propose a generation-oriented 3D-CLIP encoder trained with structured hard negatives that operate exclusively at the text level. This design increases contrastive difficulty without any additional 3D memory cost, overcoming the small-batch constraints inherent to volumetric encoders. The resulting encoder conditions a fully end-to-end latent diffusion model that operates directly in 3D latent space, eliminating the spatial artifacts and cross-slice inconsistencies introduced by super-resolution pipelines. Through systematic ablations, we establish a clear empirical link between grounding quality and downstream generative controllability. Evaluated on CT-RATE across 18 pathological conditions, our method achieves state-of-the-art performance on both image fidelity and factual correctness, while requiring less inference time and GPU memory than all competing methods. Code is at https://github.com/danielemolino/Text2CT.

Daniele Molino, Camillo Maria Caruso, Filippo Ruffini +2

Unit of Artificial Intelligence and Computer Systems, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy · Department of Diagnostics and Intervention, Biomedical Engineering and Radiation Physics, Umeå University, Umeå, Sweden · UniCamillus – Saint Camillus International University of Health Sciences, Rome, Italy

Cone-Beam Computed Tomography Volume Identity-Conditioned Latent Diffusion Models Grounding Synthesis

May 22, 2025cs.CV

Render-FM: Feedforward Model for Real-time Photorealistic Volumetric Rendering

Photorealistic volumetric rendering of CT scans greatly benefits clinical workflows, yet neural approaches such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) require prohibitive per-scan optimization (hours for NeRF, about 30 minutes for 3DGS), making them impractical in clinical settings. We propose Render-FM, a feedforward model that eliminates this bottleneck by directly regressing 6D Gaussian Splatting (6DGS) parameters from a CT volume in a single 2.8-second forward pass, a 500x speedup over per-scan optimization. To bridge the domain gap between natural scene reconstruction and medical volumetric rendering, we introduce Anatomy-Guided Priming (AGP), which incorporates segmentation masks and transfer functions as structural and appearance priors, information that existing Gaussian splatting methods overlook. Built on an nnU-Net-inspired 3D U-Net trained on diverse CT scans, Render-FM predicts per-voxel 6DGS parameters and supports immediate real-time rendering. Unlike per-scan methods, it generalizes to unseen anatomies, novel transfer functions, and enables compositional organ visualization with zero additional preparation time. Optional 89-second fine-tuning further improves quality, surpassing per-scan optimized baselines. Project page: https://gaozhongpai.github.io/renderfm/.

Zhongpai Gao, Benjamin Planche, Meng Zheng +4

United Imaging Intelligence, Boston, MA, USA

Volume Cone-Beam Computed Tomography Gaussian Splatting Real-Time Systems Feed-Forward Twitter-Fmnerg Benchmarks+1

Mar 26, 2025cs.CV

Reconstructing Rational Functions on Finite Abelian Groups with Higher Autocorrelations

The higher-order autocorrelations of integer-valued or rational-valued functions on finite Abelian groups appear naturally in X-ray crystallography, and have applications in computer vision systems, correlation tomography, correlation spectroscopy, and pattern recognition. In this paper, we consider the problem of reconstructing a rational-valued function on finite Abelian groups from its higher-order autocorrelations. We describe an explicit reconstruction algorithm, and prove that the autocorrelations up to order

3r+3

are always sufficient to determine the data up to translation, where

r

is the rank of the group. We also provide examples of rational-valued functions on finite Abelian group which are not determined by their autocorrelations up to order

3r+2

. In particular, we provide a sharp upper bound on the separating degree of the regular representation of a finite Abelian group in terms of its rank.

W. Riley Casper, Bobby Orozco

Finite Groups Autocorrelations Cone-Beam Computed Tomography

Mar 17, 2025cs.CV

UniReg: Conditional Unified Model for Medical Image Registration

Learning-based medical image registration has matched the accuracy of conventional methods while offering superior computational efficiency. However, existing approaches suffer from poor generalization across diverse clinical scenarios, requiring the laborious development of multiple isolated networks for specific registration tasks, \emph{e.g.}, inter-/intra-subject registration or anatomical region-specific alignment, leading to cumbersome development pipelines. To overcome this limitation, we propose \textbf{UniReg}, the first conditional unified model for multi-scenario medical image registration, which combines the precision advantages of task-specific learning methods with the generalization of traditional optimization methods. Our key innovation is a unified registration framework that adaptively estimates deformation fields conditioned on: (1) anatomical structure priors, (2) registration type constraints (inter/intra-subject), and (3) instance-specific features, enabling effective alignment across heterogeneous CT and MR registration scenarios within a single model. Through comprehensive experiments on multiple CT/MR registration datasets, UniReg achieves superior average registration accuracy compared with current state-of-the-art learning-based methods while exhibiting strong cross-scenario generalization. Moreover, by replacing multiple isolated task-specific models with a compact unified model, UniReg substantially reduces the overall training burden in terms of total training cost and model redundancy.

Zi Li, Jianpeng Zhang, Tai Ma +7

Department of Electrical and Computer Engineering, The University of Hong Kong, Hong Kong SAR, China · DAMO Academy, Alibaba Group · The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China +1

Image Registration U-Net Anatomy Cone-Beam Computed Tomography

Date pendingcs.CV

MCSeg: Pre-training and Fine-tuning Volumetric Pyramid Transformer for Multi-modal Cardiac Image Segmentation

Automatic cardiac image segmentation is pivotal for diagnosing and treating cardiac diseases. In this work, we introduce MCSeg, a volumetric transformer-based network tailored for multi-modal cardiac segmentation. To overcome the architectural mismatch inherent in existing hybrid networks, we propose a novel Scaling Feature Pyramid (SFP). Unlike conventional skip connections, the SFP effectively bridges the single-scale 3D Vision Transformer (ViT) encoder and the multi-scale CNN decoder by transforming the ViT's output into a hierarchical feature pyramid, ensuring that global contextual information is effectively leveraged. For the training paradigm, the ViT encoder first undergoes self-supervised pre-training via masked image modeling. Subsequently, the network is fine-tuned on downstream tasks, during which a regional mutual information (RMI) loss is integrated to improve boundary segmentation accuracy. In experiments, MCSeg consistently outperforms eleven SOTA methods on CT dataset ImageCHD, multi-modal dataset MM-WHS, MRI dataset HVSMR-2.0 and MSD Heart, highlighting the effectiveness of our MCSeg for multi-modal cardiac segmentation tasks. Furthermore, MCSeg's superior performance in few-shot experiment showcases its significant potential in adapting to limited data scenarios. Codes and pre-trained ViT-B weights are open-sourced at https://openi.pcl.ac.cn/OpenMedIA/MCSeg

Zhiyu Ye, Hairong Zheng, Tong Zhang

State Key Laboratory of Biomedical Imaging Science and System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China · Pengcheng Laboratory, Shenzhen, China · University of Chinese Academy of Sciences, Beijing, China

Echocardiography Vision Transformer Cone-Beam Computed Tomography Model Fine-Tuning

Date pendingeess.IV

Subcortical Masks Generation in CT Images via Ensemble-Based Cross-Domain Label Transfer

Subcortical segmentation in neuroimages plays an important role in understanding brain anatomy and facilitating computer-aided diagnosis of traumatic brain injuries and neurodegenerative disorders. However, training accurate automatic models requires large amounts of labelled data. Despite the availability of publicly available subcortical segmentation datasets for Magnetic Resonance Imaging (MRI), a significant gap exists for Computed Tomography (CT). This paper proposes an automatic ensemble framework to generate high-quality subcortical segmentation labels for CT scans by leveraging existing MRI-based models. We introduce a robust ensembling pipeline to integrate them and apply it to unannotated paired MRI-CT data, resulting in a comprehensive CT subcortical segmentation dataset. Extensive experiments on multiple public datasets demonstrate the superior performance of our proposed framework. Furthermore, using our generated CT dataset, we train segmentation models that achieve improved performance on related segmentation tasks. To facilitate future research, we make our source code, generated dataset, and trained models publicly available at https://github.com/alxw0671/CT_Subcortical_Segmentation, marking the first open-source release for CT subcortical segmentation to the best of our knowledge.

Augustine X. W. Lee, Pak-Hei Yeung, Jagath C. Rajapakse

College of Computing and Data Science, Nanyang Technological University, Singapore

Multi Organ Computed Tomography Segmentation Cone-Beam Computed Tomography

Date pendingcs.CV

Differentiable Jitter Correction using Deep Learning-based Image Quality Metric for Phase-Contrast Micro-CT

This paper proposes a fully differentiable jitter correction method for X-ray phase-contrast micro computed tomography using a deep learning-based image quality metric that estimates and compensates per-projection rigid jitter directly from the acquired projection data, without a pre-scan motion-free reference. The approach builds on a gradient-based auto-focus strategy adapted to parallel-beam geometry. A set of candidate objective functions is benchmarked in a controlled study, and the sensitivity of the visual information fidelity (VIF) metric to the jitter artifact is verified with the target phase-contrast data. To operate without a clean reference, a compact 3D convolutional neural network is trained to predict the VIF score from a single corrupted volume. A spatially selective total variation penalty applied exclusively to the image background is introduced to penalize spurious high-frequency structures that otherwise emerge during optimization. Experiments on biological specimens acquired at different synchrotron beamlines are conducted. Evaluation uses jitter motion applied to simulated and experimentally acquired projection data. The result confirms that the integrated pipeline reliably recovers fine structural detail lost due to jitter, with generalization demonstrated across morphologically distinct samples.

Junan Chen, Yiting Jia, Joscha Maier +6

ImFusion GmbH, Ridlerstr. 57, 80339 Munich, Germany. · Research Group Biomedical Imaging Physics, Department of Physics, TUM School of Natural Sciences and Munich Institute of Biomedical Engineering, Technical University of Munich, James-Franck-Str. 1, 85748 Garching, Germany. · Centre for X-Ray and Nano Science CXNS, Deutsches Elektronen-Synchrotron (DESY), Notkestr. 85, 22067 Hamburg, Germany. +1

Cone-Beam Computed Tomography Image Registration Image Quality Assessment Projection

Date pendingcs.RO

Multi-Robot Scanner for Automated Full-Body Dermoscopic Imaging

This paper outlines the specifications and design approach used to construct a full body imaging scanner capable of capturing skin lesions at a dermatoscopic level using cameras mounted on the end-effectors of four UR10 manipulators. The system possesses a view-planning algorithm capable of appropriately selecting the best camera position to acquire images of moles, a high-level controller to allow the manipulators to work simultaneously and a collision-detector that halts the manipulators when they make contact with an object or a person. We evaluate the system through real-patient full-body scans, comparing acquired images against contact dermoscopy and an existing total-body photography system (Vectra) across clinically relevant lesion features, and quantify true optical resolving power using a USAF 1951 resolution target, yielding a smallest resolvable feature size of 22.1 microns for our scanner compared to 8.8 microns for contact dermoscopy. Results show the scanner consistently outperforms Vectra across most clinically relevant features and achieves comparable performance to contact dermoscopy for the majority of features assessed. By acquiring dermatoscopic-quality images automatically and without contact, and without requiring a separate manual dermoscopic examination, the scanner closes part of the gap between total-body photography and handheld dermoscopy, suggesting potential for future integration into screening workflows.

Valerio Franchi, Rafael Garcia, Nuno Gracias +7

University of Girona, Girona, Spain · Coronis Computing S.L., Girona, Spain · Optotune Switzerland AG, Dietikon, Switzerland +1

Skin Lesion Classification Microscopy Lesion Cone-Beam Computed Tomography Camera Multi-Robot Systems

Date pendingcs.CV

PSCT-Net: Geometry-Aware Pediatric Skull CT Reconstruction via Differentiable Back-Projection and Attention-Guided Refinement

Computed Tomography (CT) is essential for diagnosing pediatric craniofacial abnormalities, yet poses radiation risks to developing anatomies. Reconstructing 3D CT from sparse bi-planar X-rays offers a low-dose alternative but is severely ill-posed. Existing methods employ geometry-agnostic feature lifting, naively projecting 2D features into 3D without explicit spatial modeling, causing depth ambiguity and degraded osseous boundaries. We present PSCT-Net, a geometry-aware framework with differentiable back-projection. Differentiable back-projection establishes a spatially faithful volumetric prior, alleviating depth ambiguity. An Attention-Guided Projection (AGP-3D) module then learns non-linear voxel-wise correspondences between 2D regions and 3D locations. A Bidirectional Mamba (BiM-3D) module captures long-range volumetric dependencies with linear complexity. We further curate a private institutional pediatric skull CT cohort, PedSkull-CT, comprising normal and pathological cases for internal evaluation, addressing the gap in adult-centric, trunk-focused datasets. Project page and code are available at https://dydevelop.github.io/PSCT-Net/.

Dong Yeong Kim, Jaewon Choi, Youmin Shin +5

Interdisciplinary Program in Bioengineering, Seoul National University · Department of Transdisciplinary Medicine, Seoul National University Hospital · Department of Artificial Intelligence, Yonsei University +2

Cone-Beam Computed Tomography X-Ray