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Towards single-shot coherent imaging via overlap-free ptychography

Oliver Hoidn, Aashwin Mishra, Steven Henke, Albert Vong, Matthew Seaberg

Abstract

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{Scientific Reports} \textbf{13}, 22789, 2023) to deliver \emph{overlap-free, single-shot} reconstructions in a Fresnel coherent diffraction imaging (CDI) geometry while also accelerating conventional multi-shot ptychography. The framework couples a differentiable forward model of coherent scattering with a Poisson photon-counting likelihood; real-space overlap enters as a tunable parameter via coordinate-based grouping rather than a hard requirement. On synthetic benchmarks, reconstructions remain accurate at low counts ($\sim\!10^4$ photons/frame), and overlap-free single-shot reconstruction with an experimental probe reaches amplitude structural similarity (SSIM) 0.904, compared with 0.968 for overlap-constrained reconstruction. Against a data-saturated supervised model with the same backbone (16,384 training images), PtychoPINN achieves higher SSIM with only 1,024 images and generalizes to unseen illumination profiles. Per-graphics processing unit (GPU) throughput is approximately $40\times$ that of least-squares maximum-likelihood (LSQ-ML) reconstruction at matched $128\times128$ resolution. These results, validated on experimental data from the Advanced Photon Source and the Linac Coherent Light Source, unify single-exposure Fresnel CDI and overlapped ptychography within one framework, supporting dose-efficient, high-throughput imaging at modern light sources.

Towards single-shot coherent imaging via overlap-free ptychography

Abstract

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{Scientific Reports} \textbf{13}, 22789, 2023) to deliver \emph{overlap-free, single-shot} reconstructions in a Fresnel coherent diffraction imaging (CDI) geometry while also accelerating conventional multi-shot ptychography. The framework couples a differentiable forward model of coherent scattering with a Poisson photon-counting likelihood; real-space overlap enters as a tunable parameter via coordinate-based grouping rather than a hard requirement. On synthetic benchmarks, reconstructions remain accurate at low counts ( photons/frame), and overlap-free single-shot reconstruction with an experimental probe reaches amplitude structural similarity (SSIM) 0.904, compared with 0.968 for overlap-constrained reconstruction. Against a data-saturated supervised model with the same backbone (16,384 training images), PtychoPINN achieves higher SSIM with only 1,024 images and generalizes to unseen illumination profiles. Per-graphics processing unit (GPU) throughput is approximately that of least-squares maximum-likelihood (LSQ-ML) reconstruction at matched resolution. These results, validated on experimental data from the Advanced Photon Source and the Linac Coherent Light Source, unify single-exposure Fresnel CDI and overlapped ptychography within one framework, supporting dose-efficient, high-throughput imaging at modern light sources.
Paper Structure (25 sections, 11 equations, 5 figures, 4 tables)

This paper contains 25 sections, 11 equations, 5 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Methods and Architecture
  3. Formulation and Forward Model
  4. Data model and notation.
  5. Constraint map ($F_c$): translation-aware merging.
  6. Coordinate-aware grouping.
  7. Diffraction map ($F_d$): coherent scattering.
  8. Data Preprocessing
  9. Neural Network Architecture
  10. Handling extended probes.
  11. Training Objective and Optimization
  12. Poisson negative log-likelihood (NLL).
  13. Amplitude loss for unknown counts.
  14. Implementation notes.
  15. Supervised Baseline
  16. ...and 10 more sections

Figures (5)

  • Figure 1: Reconstruction comparison across probe types and acquisition modes. Rows: idealized probe (Gaussian-smoothed disk, uniform phase) vs semi-synthetic (experimental probe, synthetic object). Columns: single-shot CDI vs overlapped ptychography.
  • Figure 2: Comparison of reconstruction quality with different numbers of diffraction patterns.
  • Figure 3: Photon-limited performance for two self-supervised PtychoPINN variants trained with mean absolute error (MAE) and Poisson negative log likelihood (NLL) reconstruction penalties.
  • Figure 4: Structural similarity of PtychoPINN and the supervised baseline as a function of training set size.
  • Figure 5: Comparison of methods for an in-distribution LCLS control (train LCLS XPP, test LCLS XPP) and out-of-distribution transfer (train APS, test LCLS XPP). The reference column shows an ePIE reconstruction of the LCLS data.