Advanced X-ray and CT Imaging

Neueste Paper

• Toward a Foundation Plug-and-Play Prior for Computed Tomography Reconstruction via a Multimodal Diffusion Model

  Haley Duba-Sullivan, Patxi Fernandez-Zelaia, Obaidullah Rahman, Amirkoushyar Ziabari · 25. September 2026

  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, …

• SpectralCTGaussians: Projection-Domain Reconstruction and Basis Material Decomposition for Spectral CT using 3D Gaussian Splatting

  Reinout Vos, Saptarshi Neil Sinha, Michael Weinmann · 25. September 2026

  Spectral computed tomography (CT) extends conventional CT by measuring attenuation across multiple energy channels, allowing improved modeling of physical X-ray interactions and energy-dependent material behavior and leading to richer scene understanding. We present a novel method for spectral CT re…

• JSolver: Joint Spectrum Estimation and Multi-Material Decomposition from Single-Energy CT Projections

  Qing Wu, Hongjiang Wei, Jingyi Yu, S. Kevin Zhou, Yuyao Zhang · 16. September 2026

  Multi-material decomposition (MMD) enables quantitative reconstruction of tissue compositions in the human body, supporting a wide range of clinical applications. However, traditional MMD typically requires spectral CT scanners and pre-measured X-ray energy spectra, significantly limiting clinical a…

• Shared-Structure 4D Spectral Gaussian Representation for Sparse-View Spectral CT Reconstruction

  Jiancheng Fang, Shaoyu Wang, Wenjun Xia, Yang Chen, Qiegen Liu · 18. August 2026

  Sparse-view spectral computed tomography (CT) reconstructs energy-resolved attenuation volumes from limited projection views, requiring simultaneous handling of angular undersampling and spectral coupling. We propose a SharedStructure 4D Spectral Gaussian Representation (4D-SG) that learns shared Ga…

• Splat-based Metal Artifact Reduction in Cone-Beam CT via Polychromatic Modeling

  Kiseok Choi, Inchul Kim, Jaemin Cho, Hyeongjun Cho, Min H. Kim · 14. August 2026

  Cone-beam computed tomography (CBCT) enables volumetric reconstruction from X-ray projections, but suffers from severe artifacts--especially beam hardening--when imaging materials with high attenuation such as metals. These artifacts arise from the polychromatic nature of X-rays and are not properly…

• RibAssist 3D: Biplanar Rib-Fracture Detection, Addressing, and Selective 3D Localization from CT-Derived Projections

  Kabila Haile Soboka · 10. August 2026

  Rib fractures are common and time-consuming to localize on computed tomography (CT). We ask whether fractures detected independently in two orthogonal CT-derived projections (anteroposterior and lateral) can be paired across views and triangulated into reliable 3D points at a controlled rate of fals…

• Lesion Detection in CT with Frozen Self-Distilled Features: SALT, a Spatially Adaptive Label-Guided Temperature

  Mahmut S. Gokmen, Evan W. Damron, Mitchell A. Klusty, Caroline N. Leach, Emily B. Collier, V. K. Cody Bumgardner · 6. August 2026

  Self-supervised pretraining objectives are spatially uniform: the teacher temperature and the per-patch loss weight are identical everywhere in the image, so a lesion a few patches wide contributes no more to the training signal than the surrounding parenchyma. Prior work biases the views toward ann…

• Splat-Based Metal Artifact Reduction in Cone-Beam CT via Compact Attenuation Modeling

  Kiseok Choi, Jaemin Cho, Inchul Kim, Min H. Kim · 6. August 2026

  X-ray computed tomography (CT) suffers from severe metal artifacts when high-attenuation objects such as dental fillings or orthopedic implants are present. These artifacts originate from the polychromatic nature of X-rays, where attenuation varies strongly with photon energy and material compositio…

• Inverse Bayesian Inference for Extracting Lesion Dynamics from Longitudinal Spectral CT

  Lukas Förner, Melina Wördehoff, Julian Steffens, Maximilian Schmutz, Rainer Claus, Josua Decker, Thomas Kröncke, Kartikay Tehlan, Thomas Wendler · 28. Juli 2026

  Longitudinal medical imaging captures temporal evolution of lesions, yet extracting the underlying dynamical parameters governing this evolution remains challenging. We propose an inverse Bayesian framework for inferring lesion dynamics from longitudinal spectral CT. We decompose spectral feature ($…

• Unsupervised Metal Artifact Reduction in Dental CBCT using Fine-tuned Cycle-Consistent Adversarial Networks

  G. L. T. Chamika, S. N. A. Dhanapala, P. H. S. V. Nimalaweera, Maheshi B. Dissanayake, Ruwan D. Jayasinghe · 24. Juli 2026

  Metal artifacts generated by dental implants significantly degrade cone-beam computed tomography (CBCT) volumes, obscuring critical anatomical structures and compromising diagnostic precision. To address this, an unsupervised deep learning framework has been proposed for Metal Artifact Reduction (MA…

• From Reconstruction to Interpretation: Zero-Setup Multi-Phase Segmentation of X-ray Tomography Data

  Pradyumna Elavarthi, Arun J. Bhattacharjee, Harrison Lisabeth, Anca Ralescu, Petrus H. Zwart, Dilworth Parkinson, Elizabeth G. Clark · 20. Juli 2026

  X-ray tomography enables nondestructive characterization of material microstructures, while advances in micro-CT imaging have accelerated volumetric data acquisition and reconstruction. However, rapid interpretation remains limited by image segmentation, which often requires manual thresholding, use…

• Quantum Compressed Sensing CT Reconstruction Algorithm Based on Penalized Weighted Least Squares and Guided Total Variation

  Yuwen Zhang, Yujie Liu, Ao Wang, Yikuang Yuluo, Shuangyang Zhong, Haijun Yu, Yixing Huang · 14. Juli 2026

  Objective. Existing quadratic unconstrained binary optimization (QUBO)-based sparse-view computed tomography (CT) reconstruction neglects photon-counting statistics and anatomical heterogeneity. We address both limitations within the QUBO framework.Approach. We propose a quantum compressed-sensing C…

• Decoupled Single-Mask Annotation Noise Detection via Cross-Sectional Patch Self-Consistency

  Yinheng Zhu, Xiaowei Xu · 8. Juli 2026

  Vascular computed tomography datasets are commonly annotated only once per scan, yielding the pervasive yet under addressed problem of single mask annotation noise. Existing solutions either require costly multirater fusion or are coupled with network training, preventing explicit auditing of where …

• A Dual-domain Refinement Network with FBP-based Jacobian Learning for Sparse-view Dual-Energy CT Material Decomposition

  Qian Liu, Xiaohong Fan, Ke Chen, Chong Chen, Shuaikang Wang, Jianping Zhang · 30. Juni 2026

  Dual-energy CT (DECT) exploits attenuation differences across different X-ray spectra to provide richer material information and has been widely used in medical imaging. While sparse-view acquisition can lower radiation exposure, it makes DECT material decomposition even more challenging, as the pro…

• Adaptive Beam Selection for Efficient Scanning Probe Tomography

  San Dinh, Zichao Wendy Di, Matt Menickelly · 23. Juni 2026

  In X-ray tomography, reconstruction quality generally improves with larger numbers of projections. However, more projections increase experiment costs, acquisition time and the radiation dose imparted to the sample. One mitigation to these trade-offs is to adopt a sequential design of experiments, i…

• Photon: Federated LLM Pre-Training

  Lorenzo Sani, Alex Iacob, Zeyu Cao, Royson Lee, Bill Marino, Yan Gao, Dongqi Cai, Zexi Li, Wanru Zhao, Xinchi Qiu, Nicholas D. Lane · 16. Juni 2026

  Scaling large language models (LLMs) demands extensive data and computing resources, which are traditionally constrained to data centers by the high-bandwidth requirements of distributed training. Low-bandwidth methods like federated learning (FL) could enable collaborative training of larger models…

• A Multi-Center Benchmark for Abdominal Disease Diagnosis and Report Generation from Non-Contrast CT

  Mariam Elbakry, Aliaa Sayed Sheha, Salma Hassan Tantawy, Aya Yassin, Concetto Spampinato, Karim Lekadir, Xiaomeng Li, Marawan Elbatel · 16. Juni 2026

  Multiphasic contrast-enhanced CT (CECT) is widely used for abdominal lesion characterization, yet it carries inherent risks of contrast-induced nephropathy, escalates acquisition burden, and heavily contributes to radiologist workload. To address these challenges, we introduce a novel multi-center b…

• A unified deeplearning framework for contrast-phase-specific virtual monochromatic imaging

  Antony Jerald, Hemant K Aggarwal, Brian Nett, Avinash Gopal, Phaneendra K Yalavarthy, Bipul Das, Rajesh Langoju · 29. Mai 2026

  Dual-energy CT (DECT) enables virtual monochromatic imaging (VMI) and improved contrast resolution, but its clinical adoption is limited by hardware complexity and cost. In this work, we propose a unified deep learning framework that synthesizes contrast-phase-specific virtual monochromatic 50 keV i…

• Optical Quantum Mixed-State Reconstruction With Multiple Deep Learning Approaches

  Nhan Trong Luu, Tuyen Quang Nguyen, Duong Trung Luu, Thang Cong Truong · 22. Mai 2026

  Quantum state tomography is a crucial technique for characterizing the state of a quantum system, which is essential for many applications in quantum technologies. In recent years, there has been growing interest in leveraging neural networks to enhance the efficiency and accuracy of quantum state t…

• Disentangling Sampling from Training Budget in Class-Imbalanced CT Body Composition Segmentation

  Iason Skylitsis, Dimitrios Karkalousos, Ivana I\v{s}gum · 22. Mai 2026

  Class imbalance is a fundamental challenge in medical image segmentation, where frequent classes typically dominate training at the expense of rare classes. Loss-based approaches mitigate imbalance by reweighting the per-pixel loss within the batch, while sampling strategies control which images ent…

• GraphMAR: Geometry-Aware Graph Learning Framework for Spatially Adaptive CT Metal Artifact Reduction

  Zilong Li, Chenglong Ma, Yiming Lei, Yuanlin Li, Jing Han, Jiannan Liu, Huidong Xie, Junping Zhang, Yi Zhang, Hongming Shan · 19. Mai 2026

  Computed tomography (CT) metal artifact reduction (MAR) aims to reduce the severe streaking artifacts induced by metallic implants and other high-density objects. Effective MAR generally requires both accurate artifact localization and artifact removal. Sinogram-domain methods can exploit explicit g…

• CT-DegradBench: A Physics-Informed Benchmark for CT Degradation Detection and Severity Estimation

  Yousra Nabila Taifour, Marouane Tliba, Zuheng Ming, Marie Luong, Nour Aburaed, Aladine Chetouani, Gorkem Durak, Alessandro Bruno, Faouzi Alaya Cheikh, Habib Zaidi, Ulas Bagci, Azeddine Beghdadi · 18. Mai 2026

  Computed tomography (CT) images are frequently degraded by acquisition artifacts, including noise, blur, streaking, aliasing, and metal artifacts. Yet CT enhancement is still largely evaluated using image quality metrics with limited perceptual and clinical validity, while existing datasets remain f…

• H3D-MarNet: Wavelet-Guided Dual-Path Learning for Metal Artifact Suppression and CT Modality Transformation for Radiotherapy Workflows

  Mubashara Rehman, Niki Martinel, Michele Avanzo, Riccardo Spizzo, Christian Micheloni · 13. Mai 2026

  Metal artifacts in computed tomography (CT) severely degrade image quality, compromising diagnostic accuracy and radiotherapy planning, especially in cancer patients with high-density implants. We propose H3D-MarNet, a two-stage framework for artifact-aware CT domain transformation from kilo-voltage…

• 3D Ultrasound-Derived Pseudo-CT Synthesis Using a Transformer-Augmented Residual Network for Real-Time Operator Guidance

  Sapna Sachan, Amulya Kumar Mahto · 7. Mai 2026

  Computed tomography (CT) is indispensable for clinical diagnosis and image-guided interventions but exposes patients to ionizing radiation, motivating the development of safer imaging alternatives. Ultrasound (US) is non-ionizing and widely accessible; however, it is highly operator dependent and la…

• Distilling Photon-Counting CT into Routine Chest CT through Clinically Validated Degradation Modeling

  Junqi Liu, Xinze Zhou, Wenxuan Li, Scott Ye, Arkadiusz Sitek, Xiaofeng Yang, Yucheng Tang, Daguang Xu, Kai Ding, Kang Wang, Yang Yang, Alan L. Yuille, Zongwei Zhou · 9. April 2026

  Photon-counting CT (PCCT) provides superior image quality with higher spatial resolution and lower noise compared to conventional energy-integrating CT (EICT), but its limited clinical availability restricts large-scale research and clinical deployment. To bridge this gap, we propose SUMI, a simulat…