Medical Imaging Techniques and Applications

Neueste Paper

• Accurate 2D Reconstruction for PET Scanners based on the Analytical White Image Model

  Tomislav Matuli\'c, Damir Ser\v{s}i\'c · 18. Februar 2026

  In this paper, we provide a precise mathematical model of crystal-to-crystal response which is used to generate the white image - a necessary compensation model needed to overcome the physical limitations of the PET scanner. We present a closed-form solution, as well as several accurate approximatio…

• PLOT-CT: Pre-log Voronoi Decomposition Assisted Generation for Low-dose CT Reconstruction

  Bin Huang, Xun Yu, Yikun Zhang, Yi Zhang, Yang Chen, Qiegen Liu · 13. Februar 2026

  Low-dose computed tomography (LDCT) reconstruction is fundamentally challenged by severe noise and compromised data fidelity under reduced radiation exposure. Most existing methods operate either in the image or post-log projection domain, which fails to fully exploit the rich structural information…

• Accurate, provable and fast polychromatic tomographic reconstruction: A variational inequality approach

  Mengqi Lou, Kabir Aladin Verchand, Sara Fridovich-Keil, Ashwin Pananjady · 12. Februar 2026

  We consider the problem of signal reconstruction for computed tomography (CT) under a nonlinear forward model that accounts for exponential signal attenuation, a polychromatic X-ray source, general measurement noise (e.g., Poisson shot noise), and observations acquired over multiple wavelength windo…

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

  Johannes Thalhammer, Tina Dorosti, Sebastian Peterhansl, Daniela Pfeiffer, Franz Pfeiffer, Florian Schaff · 10. Februar 2026

  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 stre…

• Principled Confidence Estimation for Deep Computed Tomography

  Matteo G\"atzner, Johannes Kirschner · 6. Februar 2026

  We present a principled framework for confidence estimation in computed tomography (CT) reconstruction. Based on the sequential likelihood mixing framework (Kirschner et al., 2025), we establish confidence regions with theoretical coverage guarantees for deep-learning-based CT reconstructions. We co…

• NAB: Neural Adaptive Binning for Sparse-View CT reconstruction

  Wangduo Xie, Matthew B. Blaschko · 3. Februar 2026

  Computed Tomography (CT) plays a vital role in inspecting the internal structures of industrial objects. Furthermore, achieving high-quality CT reconstruction from sparse views is essential for reducing production costs. While classic implicit neural networks have shown promising results for sparse …

• Visible Singularities Guided Correlation Network for Limited-Angle CT Reconstruction

  Yiyang Wen, Liu Shi, Zekun Zhou, WenZhe Shan, Qiegen Liu · 3. Februar 2026

  Limited-angle computed tomography (LACT) offers the advantages of reduced radiation dose and shortened scanning time. Traditional reconstruction algorithms exhibit various inherent limitations in LACT. Currently, most deep learning-based LACT reconstruction methods focus on multi-domain fusion or th…

• Spatially-Adaptive Gradient Re-parameterization for 3D Large Kernel Optimization

  Ho Hin Lee, Quan Liu, Shunxing Bao, Yuankai Huo, Bennett A. Landman · 2. Februar 2026

  Large kernel convolutions offer a scalable alternative to vision transformers for high-resolution 3D volumetric analysis, yet naively increasing kernel size often leads to optimization instability. Motivated by the spatial bias inherent in effective receptive fields (ERFs), we theoretically demonstr…

• Feature Space Topology Control via Hopkins Loss

  Einari Vaaras, Manu Airaksinen · 2. Februar 2026

  Feature space topology refers to the organization of samples within the feature space. Modifying this topology can be beneficial in machine learning applications, including dimensionality reduction, generative modeling, transfer learning, and robustness to adversarial attacks. This paper introduces …

• Extendable Generalization Self-Supervised Diffusion for Low-Dose CT Reconstruction

  Guoquan Wei, Liu Shi, Zekun Zhou, Mohan Li, Cunfeng Wei, Wenzhe Shan, Qiegen Liu · 22. Januar 2026

  Current methods based on deep learning for self-supervised low-dose CT (LDCT) reconstruction, while reducing the dependence on paired data, face the problem of significantly decreased generalization when training with single-dose data and extending to other doses. To enable dose-extensive generaliza…

• 3D Wavelet-Based Structural Priors for Controlled Diffusion in Whole-Body Low-Dose PET Denoising

  Peiyuan Jing, Yue Tang, Chun-Wun Cheng, Zhenxuan Zhang, Liutao Yang, Thiago V. Lima, Klaus Strobel, Antoine Leimgruber, Angelica Aviles-Rivero, Guang Yang, Javier Montoya · 13. Januar 2026

  Low-dose Positron Emission Tomography (PET) imaging reduces patient radiation exposure but suffers from increased noise that degrades image quality and diagnostic reliability. Although diffusion models have demonstrated strong denoising capability, their stochastic nature makes it challenging to enf…

• Meta-information Guided Cross-domain Synergistic Diffusion Model for Low-dose PET Reconstruction

  Mengxiao Geng, Ran Hong, Xiaoling Xu, Bingxuan Li, Qiegen Liu · 30. Dezember 2025

  Low-dose PET imaging is crucial for reducing patient radiation exposure but faces challenges like noise interference, reduced contrast, and difficulty in preserving physiological details. Existing methods often neglect both projection-domain physics knowledge and patient-specific meta-information, w…

• Dynamic PET Image Prediction Using a Network Combining Reversible and Irreversible Modules

  Jie Sun, Junyan Zhang, Qian Xia, Chuanfu Sun, Yumei Chen, Yunjie Yang, Huafeng Liu, Wentao Zhu, Qiegen Liu · 22. Dezember 2025

  Dynamic positron emission tomography (PET) images can reveal the distribution of tracers in the organism and the dynamic processes involved in biochemical reactions, and it is widely used in clinical practice. Despite the high effectiveness of dynamic PET imaging in studying the kinetics and metabol…

• Template-Guided Reconstruction of Pulmonary Segments with Neural Implicit Functions

  Kangxian Xie, Yufei Zhu, Kaiming Kuang, Li Zhang, Hongwei Bran Li, Mingchen Gao, Jiancheng Yang · 16. Dezember 2025

  High-quality 3D reconstruction of pulmonary segments plays a crucial role in segmentectomy and surgical planning for the treatment of lung cancer. Due to the resolution requirement of the target reconstruction, conventional deep learning-based methods often suffer from computational resource constra…

• Robust Simultaneous Multislice MRI Reconstruction Using Slice-Wise Learned Generative Diffusion Priors

  Shoujin Huang, Guanxiong Luo, Yunlin Zhao, Yilong Liu, Yuwan Wang, Kexin Yang, Jingzhe Liu, Hua Guo, Min Wang, Lingyan Zhang, Mengye Lyu · 16. Dezember 2025

  Simultaneous multislice (SMS) imaging is a powerful technique for accelerating magnetic resonance imaging (MRI) acquisitions. However, SMS reconstruction remains challenging due to complex signal interactions between and within the excited slices. In this study, we introduce ROGER, a robust SMS MRI …

• Quantifying Cross-Attention Interaction in Transformers for Interpreting TCR-pMHC Binding

  Jiarui Li, Zixiang Yin, Haley Smith, Zhengming Ding, Samuel J. Landry, Ramgopal R. Mettu · 11. Dezember 2025

  CD8+ "killer" T cells and CD4+ "helper" T cells play a central role in the adaptive immune system by recognizing antigens presented by Major Histocompatibility Complex (pMHC) molecules via T Cell Receptors (TCRs). Modeling binding between T cells and the pMHC complex is fundamental to understanding …

• Advancing Limited-Angle CT Reconstruction Through Diffusion-Based Sinogram Completion

  Jiaqi Guo, Santiago Lopez-Tapia, Aggelos K. Katsaggelos · 26. November 2025

  Limited Angle Computed Tomography (LACT) often faces significant challenges due to missing angular information. Unlike previous methods that operate in the image domain, we propose a new method that focuses on sinogram inpainting. We leverage MR-SDEs, a variant of diffusion models that characterize …

• Prompting Lipschitz-constrained network for multiple-in-one sparse-view CT reconstruction

  Baoshun Shi, Ke Jiang, Qiusheng Lian, Xinran Yu, Huazhu Fu · 26. November 2025

  Despite significant advancements in deep learning-based sparse-view computed tomography (SVCT) reconstruction algorithms, these methods still encounter two primary limitations: (i) It is challenging to explicitly prove that the prior networks of deep unfolding algorithms satisfy Lipschitz constraint…

• ReBrain: Brain MRI Reconstruction from Sparse CT Slice via Retrieval-Augmented Diffusion

  Junming Liu, Yifei Sun, Weihua Cheng, Yujin Kang, Yirong Chen, Ding Wang, Guosun Zeng · 24. November 2025

  Magnetic Resonance Imaging (MRI) plays a crucial role in brain disease diagnosis, but it is not always feasible for certain patients due to physical or clinical constraints. Recent studies attempt to synthesize MRI from Computed Tomography (CT) scans; however, low-dose protocols often result in high…

• Diffusion-based Sinogram Interpolation for Limited Angle PET

  R\"uveyda Yilmaz, Julian Thull, Johannes Stegmaier, Volkmar Schulz · 13. November 2025

  Accurate PET imaging increasingly requires methods that support unconstrained detector layouts from walk-through designs to long-axial rings where gaps and open sides lead to severely undersampled sinograms. Instead of constraining the hardware to form complete cylinders, we propose treating the mis…

• An update to PYRO-NN: A Python Library for Differentiable CT Operators

  Linda-Sophie Schneider, Yipeng Sun, Chengze Ye, Markus Michen, Andreas Maier · 12. November 2025

  Deep learning has brought significant advancements to X-ray Computed Tomography (CT) reconstruction, offering solutions to challenges arising from modern imaging technologies. These developments benefit from methods that combine classical reconstruction techniques with data-driven approaches. Differ…

• Diffusion Posterior Sampling is Computationally Intractable

  Shivam Gupta, Ajil Jalal, Aditya Parulekar, Eric Price, Zhiyang Xun · 11. November 2025

  Diffusion models are a remarkably effective way of learning and sampling from a distribution $p(x)$. In posterior sampling, one is also given a measurement model $p(y \mid x)$ and a measurement $y$, and would like to sample from $p(x \mid y)$. Posterior sampling is useful for tasks such as inpaintin…

• Anatomy-Aware Lymphoma Lesion Detection in Whole-Body PET/CT

  Simone Bendazzoli, Antonios Tzortzakakis, Andreas Abrahamsson, Bj\"orn Engelbrekt Wahlin, \"Orjan Smedby, Maria Holstensson, Rodrigo Moreno · 11. November 2025

  Early cancer detection is crucial for improving patient outcomes, and 18F FDG PET/CT imaging plays a vital role by combining metabolic and anatomical information. Accurate lesion detection remains challenging due to the need to identify multiple lesions of varying sizes. In this study, we investigat…

• AI-driven software for automated quantification of skeletal metastases and treatment response evaluation using Whole-Body Diffusion-Weighted MRI (WB-DWI) in Advanced Prostate Cancer

  Antonio Candito, Matthew D Blackledge, Richard Holbrey, Nuria Porta, Ana Ribeiro, Fabio Zugni, Luca D'Erme, Francesca Castagnoli, Alina Dragan, Ricardo Donners, Christina Messiou, Nina Tunariu, Dow-Mu Koh · 5. November 2025

  Quantitative assessment of treatment response in Advanced Prostate Cancer (APC) with bone metastases remains an unmet clinical need. Whole-Body Diffusion-Weighted MRI (WB-DWI) provides two response biomarkers: Total Diffusion Volume (TDV) and global Apparent Diffusion Coefficient (gADC). However, tr…

• In Defence of Post-hoc Explainability

  Nick Oh · 31. Oktober 2025

  This position paper defends post-hoc explainability methods as legitimate tools for scientific knowledge production in machine learning. Addressing criticism of these methods' reliability and epistemic status, we develop a philosophical framework grounded in mediated understanding and bounded factiv…