Medical Imaging Techniques and Applications

Últimos artículos

• A neural operator view on U-Nets for inverse imaging problems

  Alexander Auras, Martin Burger, Samira Kabri, Michael Moeller, Michael Schopf-Kuester · 7 de agosto de 2026

  Deep neural networks have shown great empirical success in the solution of a wide variety of ill-posed inverse problems in imaging. Yet, very few works have studied their behavior in the limit that turns the discretized ill-conditioned problems into truly ill-posed ones, i.e., for an increasing reso…

• Dual-domain U-Nets with embedded back projection operators for motion-resolved 4D CBCT reconstruction

  Ivo Herzig, Pascal Paysan, Daniel Barco, Marc Andr\'e Stadelmann, Frank-Peter Schilling, Igor Peterlik, Michal Walczak, Lijin Aryananda, Woo Sang Ahn, Rudolf Marcel F\"uchslin, Lukas Lichtensteiger · 5 de agosto de 2026

  Four-dimensional cone beam CT (4D CBCT) is important for image-guided radiation therapy of thoracic cancers, but its use is limited by long scan times, causing high patient dose and motion/sparse-sampling artifacts. We propose a deep learning method for motion-resolved 4D CBCT reconstruction from …

• Posterior Variance Is a Constraint Map, Not an Error Map: Closed-Form Uncertainty for Radiative Gaussian Splatting in Sparse-View CT

  Chulin Zhao, Yiran Xu, Shu Liu · 4 de agosto de 2026

  Radiative Gaussian splatting reconstructs sparse-view CT fast and accurately, and recent work attaches per-Gaussian posteriors to yield per-voxel uncertainty maps. We ask what such a map actually measures: posterior variance is a data-constraint map, not an error map -- its alarms are trustworthy, i…

• ELECTRIC: Evidential Learning-Enhanced CT Reconstruction via Iterative Correction

  Ge Wang · 4 de agosto de 2026

  Here we introduce ELECTRIC (Evidential Learning-Enhanced CT Reconstruction via Iterative Correction), a physics-guided Bayesian formulation. An evidential neural network provides an image proposal and an error-predictive epistemic-uncertainty surrogate. The latter is converted into an adaptive preci…

• CT-PrepAgent: Bounded Policy and Controlled Execution for Adaptive CT Data Preparation

  Xiaolin Fan, Yue Pei, Yingying Zhang, Haogang Zhu · 4 de agosto de 2026

  Heterogeneous computed tomography (CT) acquisitions and diverse downstream task requirements limit the transferability of fixed data preparation workflows across data sources and tasks. Existing approaches typically rely on manually designed or dataset-specific rules, making it difficult to accommod…

• SCMA: Structure-Conditioned and Metal-Aware Flow Matching for CT Metal Artifact Reduction

  Heran Wang, Jianing Sun, Xu Jiang, Genwei Ma, Xing Zhao, Jigang Duan · 3 de agosto de 2026

  In X-ray CT, metallic objects cause beam hardening, photon starvation, and scattering, leading to projection inconsistency, streaks, dark bands, and structural distortions that compromise clinical diagnosis and quantitative analysis. Existing metal artifact reduction (MAR) methods remain limited: op…

• Trustworthy Medical Segmentation: Uncertainty-Aware U-Net Evaluation Under Clinical Image Degradation

  Pranav Kaliaperumal, Manisha Kaliaperumal · 28 de julio de 2026

  Medical image segmentation models often report high benchmark accuracy under ideal imaging conditions, yet their failures under clinical degradation can be quiet: sensor noise, patient motion, low- resolution acquisition, and contrast variability may all alter model behavior without producing an obv…

• Reconstruction of SINR Maps from Sparse Measurements using Group Equivariant Non-Expansive Operators

  Lorenzo Mario Amorosa, Francesco Conti, Nicola Quercioli, Flavio Zabini, Tayebeh Lotfi Mahyari, Yiqun Ge, Patrizio Frosini · 28 de julio de 2026

  As sixth generation (6G) wireless networks evolve, accurate signal-to-interference-noise ratio (SINR) maps are becoming increasingly critical for effective resource management and optimization. However, acquiring such maps at high resolution is often cost-prohibitive, creating a severe data scarcity…

• Equivariant Conditional Diffusion Model for Head and Neck CT Image Synthesis from CBCT

  Alzahra Altalib, Chunhui Li, Alessandro Perelli · 24 de julio de 2026

  Background: Cone-beam computed tomography CBCT is a commonly used modality for image guided radiotherapy. It offers real time anatomical visualization with low acquisition cost and dose. Nevertheless, photon scattering and beam hindrance lead CBCT images to suffer from several artifacts. These invol…

• GenDiff: A Dose and Anatomy Aware Diffusion Model with Structural Prior Refinement for Low-Dose CT Reconstruction and Generalization

  Md Imam Ahasan, Guangchao Yang, A F M Abdun Noor, Kah Ong Michael Goh, S. M. Hasan Mahmud, Md Mahfuzur Rahman · 15 de julio de 2026

  Computed tomography (CT) is a critical imaging modality for clinical diagnosis, but reducing radiation dose inevitably introduces severe noise and structured artifacts that degrade image quality. Existing deep learning-based low-dose CT (LDCT) reconstruction methods are typically optimized for fixed…

• SAVER: Stochastic Adaptive Variance-Driven Exploration and Reconstruction for Low-Dose Computed Tomography

  Shunta Nonaga, Koji Tabata, Junya Honda, Hiroyuki Kudo, Wataru Yashiro, Tamiki Komatsuzaki · 7 de julio de 2026

  Computed Tomography (CT) is indispensable in clinical diagnostics, yet minimizing radiation dose without compromising image quality remains a critical challenge. Conventional low-dose protocols often rely on fixed, uniform angular sampling, independent of the underlying structural complexity of orga…

• SFL-Net: Source-Factorized Latent Representation Learning for Multi-Contrast MRI to Tau-PET Synthesis

  Agamdeep S. Chopra, Caitlin Neher, Tianyi Ren, Juampablo E. Heras Rivera, Hesamoddin Jahanian, Mehmet Kurt · 7 de julio de 2026

  Tau positron emission tomography supports Alzheimer's disease staging but is difficult to scale because of tracer, scanner, and radiation constraints. Synthesis from structural MRI is therefore attractive, but it is a particularly difficult setting. T1-weighted and FLAIR MRI provide anatomy and dise…

• Enabling self-supervised learned primal dual with Noise2Inverse

  Antti S\"allinen, Siiri Rautio, Santeri Kaupinm\"aki, Andreas Hauptmann · 26 de junio de 2026

  X-ray computed tomography reconstruction is an ill-posed inverse problem, particularly in low-dose and sparse-angle settings where measurements are noisy and incomplete. While learned reconstruction methods such as the Learned Primal-Dual algorithm achieve strong performance, they typically rely on …

• MaRS: Robust Out-of-Distribution Detection via Mahalanobis Residual Scoring

  Francesco Di Salvo, Sebastian Doerrich, Christian Ledig · 23 de junio de 2026

  Foundation models provide highly descriptive representations for medical images, yet their reliability degrades under distribution shifts arising from changes in patients, devices, or acquisition conditions. Reliable out-of-distribution (OOD) detection is therefore essential for safe deployment. Rec…

• Feynman Kac Reweighted Schr\"odinger Bridge Matching for Surface-Based Tau PET Harmonization

  Jianwei Zhang, Xinyu Nie, Jiaxin Yue, Yonggang Shi · 17 de junio de 2026

  Tau PET imaging is central to tracking Alzheimer's disease progression, but systematic differences between scanners, protocols, and radiotracers across sites introduce nonbiological variability that inflates biomarker variance, reduces sensitivity to disease effects, and can bias downstream clinical…

• LUCID: Learned Undersampling-Adaptive Consistency-Guided Inference with Deterministic Flow Matching for Sparse-View CT Reconstruction

  Jigang Duan, Jiayi Wang, Heran Wang, Ping Yang, Genwei Ma, Xing Zhao · 16 de junio de 2026

  Sparse-view CT reduces radiation dose and scanning time by acquiring fewer projection views, but angular undersampling makes reconstruction severely ill-posed, causing streak artifacts, structural blurring, and loss of fine details. Existing supervised methods are often tied to specific sampling set…

• Dual-Domain Equivariant Generative Adversarial Network for Multimodal CT-PET Synthesis

  Gabriel Steele, Alzahra Altalib, Alessandro Perelli · 12 de junio de 2026

  We present a Dual-Domain Equivariant Generative Adversarial Network (DDE-GAN) for multimodal CT-PET image synthesis. Traditional GAN-based approaches often operate solely in the spatial domain and ignore geometric consistency, resulting in limited structural fidelity. DDE-GAN addresses these challen…

• Deep Slice Interpolation for Reducing Through-Plane Anisotropy and Noise in Head CT

  Luis Cort\'es Ferre, Miguel A. Guti\'errez-Naranjo, Marcin Balcerzyk · 10 de junio de 2026

  Head computed tomography (CT) typically uses sub-millimeter in-plane resolution but 2-5 mm through-plane spacing, creating substantial anisotropy that degrades multiplanar reconstructions, volumetric measurements such as hematoma volume estimation, and downstream algorithms that assume near-isotropi…

• Sparse-View Lung Nodule Volumetry from Digitally Reconstructed Radiographs via AReT: Anatomy-Regularized TensoRF

  Spoorthi M, Suja Palaniswamy · 3 de junio de 2026

  We identify and resolve a previously unreported failure mode in TensoRF when applied to X-ray attenuation fields: the default density shift of -10, originally introduced for RGB scene reconstruction, suppresses density gradients and prevents sparse-view medical reconstruction regardless of learning …

• Pre-Deployment Robustness Stress Testing for CT Segmentation Systems Using Clinically Motivated Multi-Corruption Augmentation

  CholMin Kang, Jonghyun Chung, Amanpreet Kaurb, Nagesh Gulkotwarb, Arthi Sivasankaranb · 2 de junio de 2026

  Deep learning-based CT segmentation systems often achieve high accuracy on clean benchmark images, but their performance may degrade under heterogeneous clinical imaging conditions such as noise, resolution loss, contrast variation, intensity shift, and artifacts. This instability can limit reliable…

• Constructing efficient channels for ideal observers using the conjugate gradient method

  Weimin Zhou · 29 de mayo de 2026

  Task-based assessment of image quality (IQ) is critically important for the design and optimization of medical imaging systems. Ideal observers, including the Bayesian Ideal Observer (IO) and the ideal linear observer, i.e., the Hotelling observer (HO), provide objective figures of merit (FOMs) that…

• Gradient Step Plug-and-Play Model for Dental Cone-Beam CT Reconstruction

  Idris Tatachak (CREATIS), Luis Kabongo (MONC, IMB), Nicolas Papadakis (MONC, IMB), Xavier Ripoche (CREATIS), Simon Rit (CREATIS) · 28 de mayo de 2026

  The goal of this work is to reduce the effect of photon noise in dental cone-beam CT reconstruction. We consider an inverse problem formulation and develop a databased prior. To this end, we simulate fan-beam acquisitions and add photon noise to the projection data. The prior is obtained by training…

• An Empirical Study on Variance-based MC Dropout Uncertainty-Error Correlation in 2D Brain Tumor Segmentation

  Saumya B · 28 de mayo de 2026

  Accurate brain tumor segmentation from MRI is vital for diagnosis and treatment planning. Although Monte Carlo (MC) Dropout is widely used to estimate model uncertainty, the effectiveness of variance-based uncertainty - computed as pixel-wise variance across stochastic forward passes - in identifyin…

• Parameter-Efficient CT Reconstruction via Deep Graph Laplacian Regularization

  Veera Varuni Radhakrishnan, Chinthaka Dinesh, Qurat-ul-Ain Azim · 26 de mayo de 2026

  Low-dose computed tomography (LDCT) reconstruction faces a critical tradeoff between reconstruction quality and resource requirements. While recent deep learning methods achieve state-of-the-art performance, they typically rely on over 500,000 parameters trained on large-scale datasets exceeding 35,…

• Pointwise Metrics Mislead: An Evaluation Protocol for Multimodal Inverse Problems

  Mads H. Baattrup, J\"orn Bach, Laurids Jeppe, Finn Labe, Alexander Grohsjean, Christian Schwanenberger, Peer Stelldinger · 25 de mayo de 2026

  Evaluation in scientific reconstruction is dominated by pointwise metrics - RMSE, MAE, per-event resolution - under the implicit assumption that lower error means better reconstruction. We show that this assumption fails structurally for inverse problems with multimodal posteriors. By the law of tot…