Medical Imaging Techniques and Applications

Neueste Paper

• Generation of Heterogeneous PET Images from Uniform Organ Activity Maps Using a Pretrained Domain-Adapted Diffusion Model

  Suya Li, Kaushik Dutta, Debojyoti Pal, Jingqin Luo, Kooresh I. Shoghi · 22. Mai 2026

  Synthetic PET images are valuable for quantitative imaging workflow development, scalable virtual imaging trials, and deep learning model training, but conventional physics-based simulation approaches are computationally intensive, limited in anatomical variability, and often fail to capture heterog…

• EPC-3D-Diff: Equivariant Physics Consistent Conditional 3D Latent Diffusion for CBCT to CT Synthesis

  Alzahra Altalib, Chunhui Li, Haytham Al Ewaidat, Khaled Alawneh, Ahmad Qendel, Alessandro Perelli · 22. Mai 2026

  Cone-beam CT (CBCT) is routinely acquired during radiotherapy for patient setup, but its quantitative reliability is degraded by scatter, noise, and reconstruction artifacts, limiting Hounsfield Unit (HU) accuracy. We propose EPC-3D-Diff, a novel conditional 3D latent diffusion framework for volumet…

• PET-Adapter: Test-Time Domain Adaptation for Full and Limited-Angle PET Image Reconstruction

  R\"uveyda Yilmaz, Yuli Wu, Johannes Stegmaier, Volkmar Schulz · 11. Mai 2026

  Positron Emission Tomography (PET) image reconstruction is inherently challenged by Poisson noise and physical degradation factors, which are further exacerbated in limited-angle acquisitions. While deep learning methods demonstrate promising performance, their generalization to unseen clinical data…

• DuFal: Dual-Frequency-Aware Learning for High-Fidelity Extremely Sparse-view CBCT Reconstruction

  Cuong Tran Van, Trong-Thang Pham, Ngoc-Son Nguyen, Duy Minh Ho Nguyen, Ngan Le · 6. Mai 2026

  Sparse-view Cone-Beam Computed Tomography reconstruction from limited X-ray projections remains a challenging problem in medical imaging due to the inherent undersampling of fine-grained anatomical details, which correspond to high-frequency components. Conventional CNN-based methods often struggle …

• Unsupervised Denoising of Real Clinical Low Dose Liver CT with Perceptual Attention Networks

  Jingxi Pu, Tonghua Liu, Zhilin Guan, Siqiao Li, Yang Ming, Zheng Cong, Wei Zhang, Fangwei Li · 4. Mai 2026

  With the development of deep learning, medical image processing has been widely used to assist clinical research. This paper focuses on the denoising problem of low-dose computed tomography using deep learning. Although low-dose computed tomography reduces radiation exposure to patients, it also int…

• CT-Guided Spatially-varying Regularization for Voxel-Wise Deformable Whole-Body PET Registration

  Xiangcen Wu, Ruohua Chen, Sichun Li, Qianye Yang, Sheng Liu, Jianjun Liu, Zhaoheng Xie · 29. April 2026

  Whole-body Positron Emission Tomography (PET) registration is essential for multi-parametric tumor characterization and assessment of metastatic disease progression. In deep learning-based deformable registration, the dense displacement field (DDF) regularizer is crucial for stabilizing optimization…

• Conditional Diffusion Posterior Alignment for Sparse-View CT Reconstruction

  Luis Barba, Johannes Kirschner, Benjamin Bejar · 27. April 2026

  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 pro…

• PnP-CM: Consistency Models as Plug-and-Play Priors for Inverse Problems

  Merve G\"ulle, Junno Yun, Ya\c{s}ar Utku Al\c{c}alar, Mehmet Ak\c{c}akaya · 14. April 2026

  Diffusion models have found extensive use in solving inverse problems, by sampling from an approximate posterior distribution of data given the measurements. Recently, consistency models (CMs) have been proposed to directly predict the final output from any point on the diffusion ODE trajectory, ena…

• A Kernel Space-based Multidimensional Sparse Model for Dynamic PET Image Denoising

  Kuang Xiaodong, Li Bingxuan, Li Yuan, Rao Fan, Ma Gege, Xie Qingguo, Mok Greta S P, Liu Huafeng, Zhu Wentao · 24. März 2026

  Achieving high image quality for temporal frames in dynamic positron emission tomography (PET) is challenging due to the limited statistic especially for the short frames. Recent studies have shown that deep learning (DL) is useful in a wide range of medical image denoising tasks. In this paper, we …

• Scalable Learning from Probability Measures with Mean Measure Quantization

  Erell Gachon, Elsa Cazelles, J\'er\'emie Bigot · 24. März 2026

  We consider statistical learning problems in which data are observed as a set of probability measures. Optimal transport (OT) is a popular tool to compare and manipulate such objects, but its computational cost becomes prohibitive when the measures have large support. We study a quantization-based a…

• Unsupervised Learning for Inverse Problems in Computed Tomography

  Laura Hellwege, Johann Christopher Engster, Moritz Schaar, Thorsten M. Buzug, Maik Stille · 20. März 2026

  Assume you encounter an inverse problem that shall be solved for a large number of data, but no ground-truth data is available. To emulate this encounter, in this study, we assume it is unknown how to solve the imaging problem of Computed Tomography (CT). An unsupervised deep learning approach is in…

• Projection Guided Personalized Federated Learning for Low Dose CT Denoising

  Anas Zafar, Muhammad Waqas, Amgad Muneer, Rukhmini Bandyopadhyay, Jia Wu · 17. März 2026

  Low-dose CT (LDCT) reduces radiation exposure but introduces protocol-dependent noise and artifacts that vary across institutions. While federated learning enables collaborative training without centralizing patient data, existing methods personalize in image space, making it difficult to separate s…

• TrackFormers Part 2: Enhanced Transformer-Based Models for High-Energy Physics Track Reconstruction

  Sascha Caron, Nadezhda Dobreva, Maarten Kimpel, Uraz Odyurt, Slav Pshenov, Roberto Ruiz de Austri Bazan, Eugene Shalugin, Zef Wolffs, Yue Zhao · 17. März 2026

  High-Energy Physics experiments are rapidly escalating in generated data volume, a trend that will intensify with the upcoming High-Luminosity LHC upgrade. This surge in data necessitates critical revisions across the data processing pipeline, with particle track reconstruction being a prime candida…

• Unsupervised Adaptation from FDG to PSMA PET/CT for 3D Lesion Detection under Label Shift

  Xiaofeng Liu, Menghua Xia, Yanis Chemli, Georges El Fakhri, Chi Liu, Jinsong Ouyang · 17. März 2026

  In this work, we propose an unsupervised domain adaptation (UDA) framework for 3D volumetric lesion detection that adapts a detector trained on labeled FDG PET/CT to unlabeled PSMA PET/CT. Beyond covariate shift, cross tracer adaptation also exhibits label shift in both lesion size composition and t…

• PatchDenoiser: Parameter-efficient multi-scale patch learning and fusion denoiser for Low-dose CT imaging

  Jitindra Fartiyal, Pedro Freire, Sergei K. Turitsyn, Sergei G. Solovski · 12. März 2026

  Low-dose CT images are essential for reducing radiation exposure in cancer screening, pediatric imaging, and longitudinal monitoring protocols, but their quality is often degraded by noise from low-dose acquisition, patient motion, or scanner limitations, affecting both clinical interpretation and d…

• 3D CBCT Artefact Removal Using Perpendicular Score-Based Diffusion Models

  Susanne Schaub, Florentin Bieder, Matheus L. Oliveira, Yulan Wang, Dorothea Dagassan-Berndt, Michael M. Bornstein, Philippe C. Cattin · 9. März 2026

  Cone-beam computed tomography (CBCT) is a widely used 3D imaging technique in dentistry, offering high-resolution images while minimising radiation exposure for patients. However, CBCT is highly susceptible to artefacts arising from high-density objects such as dental implants, which can compromise …

• Benchmarking Self-Supervised Learning Methods for Accelerated MRI Reconstruction

  Andrew Wang, Steven McDonagh, Mike Davies · 3. März 2026

  Reconstructing MRI from highly undersampled measurements is crucial for accelerating medical imaging, but is challenging due to the ill-posedness of the inverse problem. While supervised deep learning (DL) approaches have shown remarkable success, they traditionally rely on fully-sampled ground trut…

• MAP-Diff: Multi-Anchor Guided Diffusion for Progressive 3D Whole-Body Low-Dose PET Denoising

  Peiyuan Jing, Chun-Wun Cheng, Liutao Yang, Zhenxuan Zhang, Thiago V. Lima, Klaus Strobel, Antoine Leimgruber, Angelica Aviles-Rivero, Guang Yang, Javier A. Montoya-Zegarra · 3. März 2026

  Low-dose Positron Emission Tomography (PET) reduces radiation exposure but suffers from severe noise and quantitative degradation. Diffusion-based denoising models achieve strong final reconstructions, yet their reverse trajectories are typically unconstrained and not aligned with the progressive na…

• Efficient Flow Matching for Sparse-View CT Reconstruction

  Jiayang Shi, Lincen Yang, Zhong Li, Tristan Van Leeuwen, Daniel M. Pelt, K. Joost Batenburg · 3. März 2026

  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 d…

• SALIENT: Frequency-Aware Paired Diffusion for Controllable Long-Tail CT Detection

  Yifan Li, Mehrdad Salimitari, Taiyu Zhang, Guang Li, David Dreizin · 2. März 2026

  Detection of rare lesions in whole-body CT is fundamentally limited by extreme class imbalance and low target-to-volume ratios, producing precision collapse despite high AUROC. Synthetic augmentation with diffusion models offers promise, yet pixel-space diffusion is computationally expensive, and ex…

• DisQ-HNet: A Disentangled Quantized Half-UNet for Interpretable Multimodal Image Synthesis Applications to Tau-PET Synthesis from T1 and FLAIR MRI

  Agamdeep S. Chopra, Caitlin Neher, Tianyi Ren, Juampablo E. Heras Rivera, Mehmet Kurt · 27. Februar 2026

  Tau positron emission tomography (tau-PET) provides an in vivo marker of Alzheimer's disease pathology, but cost and limited availability motivate MRI-based alternatives. We introduce DisQ-HNet (DQH), a framework that synthesizes tau-PET from paired T1-weighted and FLAIR MRI while exposing how each …

• PatchDenoiser: Parameter-efficient multi-scale patch learning and fusion denoiser for medical images

  Jitindra Fartiyal, Pedro Freire, Sergei K. Turitsyn, Sergei G. Solovski · 26. Februar 2026

  Medical images are essential for diagnosis, treatment planning, and research, but their quality is often degraded by noise from low-dose acquisition, patient motion, or scanner limitations, affecting both clinical interpretation and downstream analysis. Traditional filtering approaches often over-sm…

• DM4CT: Benchmarking Diffusion Models for Computed Tomography Reconstruction

  Jiayang Shi, Daniel M. Pelt, K. Joost Batenburg · 24. Februar 2026

  Diffusion models have recently emerged as powerful priors for solving inverse problems. While computed tomography (CT) is theoretically a linear inverse problem, it poses many practical challenges. These include correlated noise, artifact structures, reliance on system geometry, and misaligned value…

• TopoGate: Quality-Aware Topology-Stabilized Gated Fusion for Longitudinal Low-Dose CT New-Lesion Prediction

  Seungik Cho · 23. Februar 2026

  Longitudinal low-dose CT follow-ups vary in noise, reconstruction kernels, and registration quality. These differences destabilize subtraction images and can trigger false new lesion alarms. We present TopoGate, a lightweight model that combines the follow-up appearance view with the subtraction vie…

• Filter2Noise: A Framework for Interpretable and Zero-Shot Low-Dose CT Image Denoising

  Yipeng Sun, Linda-Sophie Schneider, Siyuan Mei, Jinhua Wang, Ge Hu, Mingxuan Gu, Chengze Ye, Fabian Wagner, Lan Song, Siming Bayer, Andreas Maier · 19. Februar 2026

  Noise in low-dose computed tomography (LDCT) can obscure important diagnostic details. While deep learning offers powerful denoising, supervised methods require impractical paired data, and self-supervised alternatives often use opaque, parameter-heavy networks that limit clinical trust. We propose …