Advanced X-ray Imaging Techniques

Derniers papiers

• Image Reconstruction from Phase with Untrained Neural Priors

  Ene Meco, Ahmet Enis Cetin · 28 septembre 2026

  Fourier phase encodes important spatial image structure, but recovering an image without measured spectral magnitude requires additional constraints and leaves absolute intensity ambiguous. We propose a projection-based two-stage framework that combines Fourier-phase and spatial-support constraints …

• Adaptive Tiling for Least-Squares Phase Unwrapping: Runtime and Accuracy

  Antoine Moevus, Max Mignotte · 25 septembre 2026

  Phase unwrapping estimates the missing multiples of $2\pi$ in measured phase images. For large images, tiling limits the size of local reconstruction problems and enables parallel processing. Adaptive tiling could further reduce the number of local problems and boundaries by retaining large tiles wh…

• Differentiable Jitter Correction using Deep Learning-based Image Quality Metric for Phase-Contrast Micro-CT

  Junan Chen, Yiting Jia, Joscha Maier, Dominik John, Sami Wirtensohn, Imke Greving, Silja Flenner, Matthias Wieczorek, Julia Herzen · 11 septembre 2026

  This paper proposes a fully differentiable jitter correction method for X-ray phase-contrast micro computed tomography using a deep learning-based image quality metric that estimates and compensates per-projection rigid jitter directly from the acquired projection data, without a pre-scan motion-fre…

• LoRCA: LoRA Cycle Adaptation for Histology to HiP-CT Translation with DINOv3

  Yang Zhou, Edoardo Occhipinti, Banboye Kidzeru Elvis, Jishizhan Chen, Stathis Megas, Joseph Brunet, Joanna Purzycka, Theresa Urban, Hector Dejea, Sarah Amalia Teichmann, Menna R Clatworthy, Paul Tafforeau, Peter D Lee, Claire L Walsh · 10 août 2026

  Hierarchical Phase-Contrast Tomography (HiP-CT) is a synchrotron based X-ray imaging technique that enables non-destructive, volumetric imaging of intact organs with multi-resolutions bridging 20 $μm$/voxel for whole organs to near-cellular resolution ($\sim$0.8 $μm$/voxel) in local regions. This of…

• Contrast-invariant deep ptychography neural networks

  Albert Vong, Steven Henke, Oliver Hoidn, Hanna Ruth, Junjing Deng, Apurva Mehta, David Shapiro, Alexander Hexemer, Nicholas Schwarz · 5 août 2026

  Ptychography neural networks suffer from scaling inconsistencies when generalizing out of distribution, limiting their real world viability. We address this scaling mismatch using a factorization strategy which decouples the learned object texture from measurement scaling, enabling a single trained …

• Breaking the Weak Recovery Limit in Random Phase Retrieval with Learned Regularizers

  Stanislas Ducotterd, Zhiyuan Hu, Michael Unser, Jonathan Dong · 2 juillet 2026

  We seek to recover an unknown signal from nonlinear amplitude-only measurements, a challenging inverse problem. Strong theoretical guarantees have been established for idealized random measurements, defining the sampling ratio required for signal recovery. However, these results neglect signal prior…

• Unsupervised Deep Learning for Limited-Angle STEM-EDX Tomography -- Application to 3D Chemical Analysis of Phase-Change Memory Devices

  Daniel del Pozo Bueno, Serge Brosset, Theo Monniez, Gabriele Navarro, Philippe Ciuciu, Zineb Saghi · 10 juin 2026

  Energy Dispersive X-ray (EDX) tomography in Scanning Transmission Electron Microscopy (STEM) enables 3D compositional and elemental mapping at the nanoscale, but its use is limited by restricted tilt ranges and low-dose conditions required to avoid beam damage. Limited-angle acquisition introduces m…

• On the conditional equivalence of phase retrieval algorithms

  Jakob Schroeder, Andreas D\"opp · 8 juin 2026

  Phase retrieval - recovering a complex-valued field from intensity measurements - is typically solved using variants of the Gerchberg-Saxton (GS) algorithm, understood as alternating projections between measurement planes. Meanwhile, modern computational imaging increasingly relies on gradient-based…

• Position-Blind Ptychography: Viability of image reconstruction via data-driven variational inference

  Simon Welker, Lorenz Kuger, Tim Roith, Berthy Feng, Martin Burger, Timo Gerkmann, Henry Chapman · 1 juin 2026

  In this work, we present and investigate the novel blind inverse problem of position-blind ptychography, i.e., ptychographic phase retrieval without any knowledge of scan positions, which then must be recovered jointly with the image. The motivation for this problem comes from single-particle diffra…

• A Fully Convolutional Approach to Denoising Structural Dynamics Data from X-Ray Photon Correlation Spectroscopy

  Nisar Nellikunnummel, Andi Barbour, Lutz Wiegart, Tatiana Konstantinova, Anthony DeGennaro · 29 mai 2026

  We present a fully convolutional denoising autoencoder (FC-DAE) for denoising two-time intensity-intensity correlation functions ($C_2$) in X-ray photon correlation spectroscopy (XPCS). Unlike conventional denoising autoencoders that are typically restricted to fixed input sizes, the FC-DAE accepts …

• Beyond MMSE: Enhancing PnP Restoration with ProxiMAP

  Kenta Vert, Giacomo Meanti, Scott Pesme, Michael Arbel, Julien Mairal · 19 mai 2026

  Plug-and-Play (PnP) methods have become standard tools for solving imaging inverse problems by replacing the intractable maximum a posteriori (MAP) denoiser with the MMSE one. While this mismatch has been widely treated as unavoidable, recent works have sought to close this gap by targeting the MAP …

• Neighbor2Inverse: Self-Supervised Denoising for Low-Dose Region-of-Interest Phase Contrast CT

  Johannes B. Thalhammer, Lorenzo D'Amico, Lucy Costello, Sebastian Peterhansl, Daniel Frey, Tina Dorosti, Florian Schaff, Jannis Ahlers, Ronan Smith, Marcus Kitchen, Franz Pfeiffer, Martin Donnelley, Daniela Pfeiffer, Kaye S. Morgan · 5 mai 2026

  Propagation-based X-ray phase-contrast imaging (PBI) enables high-contrast visualization of lung structures and holds strong medical potential. However, safe translation to the clinic will require a substantial radiation dose reduction, which inevitably increases image noise. Supervised convolutiona…

• Machine Learning-Augmented Acceleration of Iterative Ptychographic Reconstruction

  Bowen Zheng, Katayun Kamdin, David Shapiro, Alexander Ditter, Dayne Sasaki, Emma Bernard, Roopali Kukreja, Petrus H. Zwart, Slavom\'ir Nem\v{s}\'ak, Apurva Mehta, Nicholas Schwarz, Alexander Hexemer, Tanny Chavez · 5 mai 2026

  Iterative ptychographic reconstruction algorithms are widely used for coherent diffractive imaging but can exhibit slow convergence under realistic experimental conditions. We propose a machine learning-augmented approach that accelerates iterative ptychographic reconstruction by introducing a learn…

• Circular Phase Representation and Geometry-Aware Optimization for Ptychographic Image Reconstruction

  Carson Yu Liu, Jun Cheng, Chien-Chun Chen, Steve F. Shu · 30 avril 2026

  Traditional iterative reconstruction methods are accurate but computationally expensive, limiting their use in high-throughput and real-time ptychography. Recent deep learning approaches improve speed, but often predict phase as a Euclidean scalar despite its $2π$ periodicity, which can introduce wr…

• Autonomous Algorithm Discovery for Ptychography via Evolutionary LLM Reasoning

  Xiangyu Yin, Ming Du, Junjing Deng, Zhi Yang, Yimo Han, Yi Jiang · 9 mars 2026

  Ptychography is a computational imaging technique widely used for high-resolution materials characterization, but high-quality reconstructions often require the use of regularization functions that largely remain manually designed. We introduce Ptychi-Evolve, an autonomous framework that uses large …

• Towards single-shot coherent imaging via overlap-free ptychography

  Oliver Hoidn, Aashwin Mishra, Steven Henke, Albert Vong, Matthew Seaberg · 26 février 2026

  Ptychographic imaging at synchrotron and XFEL sources requires dense overlapping scans, limiting throughput and increasing dose. Extending coherent diffractive imaging to overlap-free operation on extended samples remains an open problem. Here, we extend PtychoPINN (O. Hoidn \emph{et al.}, \emph{Sci…

• Tractable Gaussian Phase Retrieval with Heavy Tails and Adversarial Corruption with Near-Linear Sample Complexity

  Santanu Das, Jatin Batra · 12 février 2026

  Phase retrieval is the classical problem of recovering a signal $x^* \in \mathbb{R}^n$ from its noisy phaseless measurements $y_i = \langle a_i, x^* \rangle^2 + \zeta_i$ (where $\zeta_i$ denotes noise, and $a_i$ is the sensing vector) for $i \in [m]$. The problem of phase retrieval has a rich histor…

• PDE-Constrained Optimization for Neural Image Segmentation with Physics Priors

  Seema K. Poudel, Sunny K. Khadka · 3 février 2026

  Segmentation of microscopy images constitutes an ill-posed inverse problem due to measurement noise, weak object boundaries, and limited labeled data. Although deep neural networks provide flexible nonparametric estimators, unconstrained empirical risk minimization often leads to unstable solutions …

• Uncertainty-guided Generation of Dark-field Radiographs

  Lina Felsner, Henriette Bast, Tina Dorosti, Florian Schaff, Franz Pfeiffer, Daniela Pfeiffer, Julia Schnabel · 23 janvier 2026

  X-ray dark-field radiography provides complementary diagnostic information to conventional attenuation imaging by visualizing microstructural tissue changes through small-angle scattering. However, the limited availability of such data poses challenges for developing robust deep learning models. In …

• Towards generalizable deep ptychography neural networks

  Albert Vong, Steven Henke, Oliver Hoidn, Hanna Ruth, Junjing Deng, Alexander Hexemer, David Shapiro, Apurva Mehta, Arianna Gleason, Levi Hancock, Nicholas Schwarz · 9 janvier 2026

  X-ray ptychography is a data-intensive imaging technique expected to become ubiquitous at next-generation light sources delivering many-fold increases in coherent flux. The need for real-time feedback under accelerated acquisition rates motivates surrogate reconstruction models like deep neural netw…

• Fast Escape, Slow Convergence: Learning Dynamics of Phase Retrieval under Power-Law Data

  Guillaume Braun, Bruno Loureiro, Ha Quang Minh, Masaaki Imaizumi · 25 novembre 2025

  Scaling laws describe how learning performance improves with data, compute, or training time, and have become a central theme in modern deep learning. We study this phenomenon in a canonical nonlinear model: phase retrieval with anisotropic Gaussian inputs whose covariance spectrum follows a power l…

• DLMMPR:Deep Learning-based Measurement Matrix for Phase Retrieval

  Jing Liu, Bing Guo, Ren Zhu · 18 novembre 2025

  This paper pioneers the integration of learning optimization into measurement matrix design for phase retrieval. We introduce the Deep Learning-based Measurement Matrix for Phase Retrieval (DLMMPR) algorithm, which parameterizes the measurement matrix within an end-to-end deep learning architecture.…