Medical Imaging Techniques and Applications

Neueste Paper

• Image Fidelity is Not Field Fidelity: Joint Thermodynamic Reconstruction and Error Localization in Neural Tomography

  Alan Hsu, Jenna Samra, Alin Razvan Paraschiv, Liam Connor · 25. September 2026

  Neural fields for scientific tomography are optimized from 2D images, but the actual quantity of interest is often a latent 3D physical field. Because the forward map is many-to-one, low 2D image error need not certify a correct 3D field. Moreover, the latent field is not directly supervised during …

• When Misalignment Becomes Supervision: Structured Label Noise in Supervised Synthetic CT Generation

  Valentin Boussot, Cedric Hemon, Caroline Lafond, Jean-Claude Nunes, Jean-Louis Dillenseger · 25. September 2026

  Supervised synthetic CT (sCT) generation is commonly trained and evaluated as voxel-wise regression against registered reference CT images. In practice, MRI-CT and CBCT-CT pairs are aligned through registration procedures that leave residual misalignments. These residuals are not independent intensi…

• PEEL: Physics-Enabled Evidential Learning for Identifiable Uncertainty in CT Imaging

  Ge Wang (Rensselaer Polytechnic Institute) · 25. September 2026

  Normal-inverse-gamma (NIG) regression is not uniquely identifiable from its marginal Student-t likelihood: the likelihood determines three combinations of four NIG parameters and is constant along a one-dimensional fiber. We identify that fiber using independent physical measurement. As an initial e…

• Field-of-View Extension in Dental Cone-Beam CT via Implicit Neural Representations and Diffusion Model-Based Refinement

  Susanne Schaub, Florentin Bieder, Matheus L. Oliveira, Yulan Wang, Buyanbileg Sodnom-ish, Dorothea Dagassan-Berndt, Michael M. Bornstein, Philippe C. Cattin · 24. September 2026

  Dental cone-beam computed tomography (CBCT) systems often employ detector configurations that provide a truncated field of view (FOV) that only captures a small part of the patient's anatomy. In this work, we aim to reconstruct an extended FOV using projections of truncated FOV scans. To this end, w…

• Geometry-anchored PET-aware multimodal pseudo-CT synthesis for whole-body attenuation correction: the BIC-MAC Challenge

  Xuan Loc Nguyen, Hoang-Loc Cao, Truong Thanh Hung Nguyen, Phuc Ho, Phuc Truong Loc Nguyen, Nguyen Truong Toan To, Hung Cao · 24. September 2026

  The BIC-MAC challenge targets whole-body pseudo-CT synthesis from NAC-PET, Dixon MRI, and a 2D topogram for CT-less PET attenuation correction. We propose GeoPACT, a geometry-anchored multimodal framework that uses NAC-PET as the spatial reference and incorporates topogram and MRI features through g…

• LINGO: Latent Initialization and Gradient Optimization for Sparse-view X-ray Novel View Synthesis and CT Reconstruction with 3D Gaussian Splatting

  Lifeng Xing, Dequan Jin, Kunpeng Bu, Peigeng He, Shihui Ying · 22. September 2026

  In novel view synthesis and Computed Tomography (CT) reconstruction with sparse-view X-ray imaging, insufficient angular coverage leads to structural ambiguity and accumulated noise. Integrating 3D Gaussian Splatting (3DGS) with X-ray absorption physics can achieve promising results, but it suffers …

• A deep dictionary network-based foundation model for ultra-low-dose CT denoising

  Baoshun Shi, Shuangyi Yang, Ke Jiang, Bin Zhu, Zhanli Hu, Huazhu Fu · 16. September 2026

  Ultra-low-dose computed tomography (ULDCT) reduces radiation exposure but suffers from severe noise that degrades diagnostic image quality. Existing deep learning-based denoising methods are typically trained in an organ-specific fashion, resulting in limited generalization across heterogeneous mult…

• Efficient 3D Whole-Body PET Image Denoising via Conditional Rectified Flow With Optimized Sampling Strategy

  Jiale Shen, Guolin Wang, Chenhao Wang, Xinhui Su, Wei Luo, Feng Yu · 16. September 2026

  Reducing radiation exposure in Positron Emission Tomography (PET) is important for patient safety; however, ultra-low-dose imaging suffers from severe noise, which may affect diagnostic interpretation without appropriate image enhancement. While current 3D deep generative models, particularly diffus…

• Task-Based CT Protocol Optimization Using Reinforcement Learning and Virtual Imaging Trials

  Jiaqi Zou, David Fenwick, Vahid Tarokh, Nicholas Felice, Jayasai Rajagopal, Anuj Kapadia, Ehsan Samei, Navid NaderiAlizadeh, Ehsan Abadi · 15. September 2026

  Protocol optimization in computed tomography (CT) aims to improve diagnostic image quality while reducing radiation dose, but the interdependence of acquisition and reconstruction parameters makes exhaustive testing impractical. We propose a virtual imaging trial framework with reinforcement learnin…

• 3D CT-to-PET Translation via Latent Brownian Bridge Diffusion

  Sarita Mourya, Francesco Di Feola, Pierangelo Veltri, Paolo Soda · 14. September 2026

  Computed tomography (CT) and positron emission tomography (PET) provide complementary anatomical and functional information for cancer diagnosis and treatment planning. However, the widespread use of PET is limited by high radiation exposure, elevated costs, and restricted availability. To address t…

• Confidence-Calibrating Regularization for Robust Brain MRI Segmentation Under Domain Shift

  Behraj Khan, Tahir Qasim Syed, Syed Ahmad Chan Bukhari · 11. September 2026

  The Segment Anything Model (SAM) exhibits strong zero-shot performance on natural images but suffers from domain shift and overconfidence when applied to medical volumes. We propose \textbf{CalSAM}, a lightweight adaptation framework that (i) reduces encoder sensitivity to domain shift via a \emph{F…

• Multi-Pass, Multi-View Blended Learning for High-Fidelity Volumetric CT Synthesis from Chest X-Rays

  Ozer Can Devecioglu, Serkan Kiranyaz, Rashid Mazhar, Tahir Hamid, Muhammad Chowdhury, Moncef Gabbouj · 10. September 2026

  Reconstructing volumetric Computed Tomography (CT) from a single 2D chest radiograph (CXR) is an ill-posed inverse problem, further complicated by the scarcity of paired CXR-CT training data. Prior approaches address this by training on Digitally Reconstructed Radiographs (DRRs), which are synthetic…

• Advanced Brain Tissue Imaging with Data-Consistent Diffusion Priors in Laminographic X-Ray Nanoimaging

  Wenxuan Fang, Abraham L. Levitan, Ana Diaz, Carles Bosch, Adrian Wanner, Andreas T. Schaefer, Mirko Holler, Tomas Aidukas, Nicholas W. Phillips, Yuxin Zhang, Alexandra Pacureanu, Manuel Guizar-Sicairos, Luis Barba · 10. September 2026

  Nanoscale imaging of mammalian brains is critical for connectomics. X-ray laminography enables high-throughput imaging of extended, plate-like biological specimens. However, the tilted acquisition geometry leads to incomplete Fourier-space coverage, giving rise to a missing-cone of information. Conv…

• Shape-guided Gaussian Splatting for Sparse-View X-ray 3D Reconstruction

  Pranav Poudel, Florence Dell'Aniello Picard, Nairouz Shehata, Fr\'ed\'eric Lavoie, Herve Lombaert · 10. September 2026

  Sparse-view X-ray 3D reconstruction is essential for reducing radiation exposure, but recovering a density field from a handful of X-ray projections is severely ill-posed. Recently, 3D Gaussian Splatting has achieved state-of-the-art performance in sparse-view reconstruction by representing the volu…

• Physics-Guided Flow Matching for CT Image Reconstruction

  Davide Evangelista · 31. August 2026

  Deep generative models have recently emerged as powerful priors for solving ill-posed inverse problems in CT, with diffusion-based approaches achieving state-of-the-art reconstruction performance. However, diffusion models typically rely on stochastic sampling procedures, long inference trajectories…

• Three-Phase Scribble-Adaptive Curriculum Learning for autoPETV Grand Challenge

  Libo Zhang · 25. August 2026

  This report describes Libo Zhang's algorithmic solution to autoPETV Grand Challenge on interactive lesion segmentation in whole-body PET/CT. Interaction is encoded as two additional input channels that rasterize the accumulated foreground and background scribbles, and a residual-encoder U-Net of abo…

• Region-Weighted Losses and Model Fusion for Cross-Modal PET Attenuation Correction

  Khoa Tuan Nguyen, Joris Vankerschaver, Wesley De Neve · 25. August 2026

  We describe our approach to the Big Cross-Modal Attenuation Correction (BIC-MAC) challenge, which asks for a pseudo-CT in Hounsfield Units to be synthesized from Non-Attenuation-Corrected PET (NAC-PET), DIXON MRI and a topogram, and scores both the pseudo-CT and the Attenuation-Corrected PET (AC-PET…

• Flow Matching-Based PET Image Reconstruction

  Fumio Hashimoto, Ziqian Huang, Tatsuya Yokota, Kuang Gong · 21. August 2026

  Generative models have shown strong potential for positron emission tomography (PET) image reconstruction. Although diffusion model-based reconstruction methods have demonstrated promising performance, they often require many reverse sampling steps with data-consistency updates incorporated into the…

• MUST-PET: MUltimodal Self-supervised learning across Tracers for whole-body PET/CT-based lesion segmentation

  Bashirul Azam Biswas, Amartya Bhattacharya, Biratal Raj Wagle, Matthew E. Maeder, James B. Yu, Indrani Bhattacharya · 21. August 2026

  Deep learning-based whole-body PET-CT lesion segmentation can support cancer staging, treatment planning, and response assessment, but generalization is limited by scarce annotations and domain shifts. Self-supervised learning (SSL) can address these challenges but remains underexplored in pan-cance…

• Learning Where and What to Lift for Bi-planar X-ray-to-CT Reconstruction

  Yifei Wu, Yicheng Wu, Qiang Ma, Qi Chen, Renyang Gu, Xinyu Liu, Yongsheng Pan, Yong Xia · 19. August 2026

  X-ray imaging can be approximately modeled as the projection of an underlying volumetric attenuation field, with each measurement recording the accumulated attenuation along a corresponding ray path. Reconstructing a CT volume from only a few X-ray views is therefore severely ill-posed, as the proje…

• Anatomical and Physical Supervision for CT-less PET Attenuation Correction: BIC-MAC 2026 Challenge

  Petros Chatzitoulousis, George K. Matsopoulos · 18. August 2026

  This report describes our submission to the Big Cross-Modal Attenuation Correction (BIC-MAC) 2026 Challenge for CT-less PET attenuation correction through multimodal pseudo-CT synthesis. We build upon a standard nnU-Net architecture and combine anatomical and physical supervision to improve both pse…

• Feed-Forward Hierarchical Gaussian Diffusion for Extreme CT Reconstruction

  Yuezhe Yang, Li Cheng · 18. August 2026

  Reconstructing three-dimensional computed tomography (CT) from severely constrained projections is highly ill-posed. Sparse angular sampling, restricted angular coverage, and low photon counts can occur individually or jointly, obscuring global anatomy and local tissue detail. Many learned CT recons…

• CG-GLORE: A Conjugate Gradient-Based Global-Local Regularization Network for Sparse-View CT Reconstruction

  Tran Xuan Hieu Le, Doanh C. Bui, Vu Trung Duong Le, Hoai Luan Pham, Khang Nguyen, Mai K. Nguyen, Tu Bao Ho, Yasuhiko Nakashima · 18. August 2026

  Sparse-view computed tomography (CT) reduces radiation dose by acquiring fewer projection views, but the resulting inverse problem is highly ill-posed and often produces severe streak artifacts. Existing deep reconstruction methods have achieved promising performance, yet many rely on first-order up…

• VLM- and LLM-Driven Multi-Agent System for PET Image Denoising

  Boxiao Yu, Savas Ozdemir, Yang Xing, Fumio Hashimoto, Jiong Wu, Yizhou Chen, Axel Rominger, Ruogu Fang, Kuangyu Shi, Tinsu Pan, Kuang Gong · 17. August 2026

  Positron emission tomography (PET) imaging suffers from limited spatial resolution and low signal-to-noise ratio, which can compromise quantitative accuracy and lesion detectability. Deep learning-based denoising methods have demonstrated strong potential for improving PET image quality. However, th…

• Knowledge-Guided 3D CT Generation: A Conditioning-Centric Taxonomy

  Francesca Pia Panaccione, Eugenio Lomurno, Matteo Matteucci · 12. August 2026

  Controllable generation guided by external knowledge is a key requirement in modern generative deep learning applications, enabling the synthesis of samples with explicit constraints on semantic content, structural properties, and variability. In 3D Computed Tomography (CT), such control is essentia…