A Joint Variational Framework for Multimodal X-ray Ptychography and Fluorescence Reconstruction
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916005148622848 |
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| author | Zou, Chengru Eric Buser, Elle Di, Zichao Wendy Xi, Yuanzhe |
| author_facet | Zou, Chengru Eric Buser, Elle Di, Zichao Wendy Xi, Yuanzhe |
| contents | Recovering high-resolution structural and compositional information from coherent X-ray measurements involves solving coupled, nonlinear, and ill-posed inverse problems. Ptychography reconstructs a complex transmission function from overlapping diffraction patterns, while X-ray fluorescence provides quantitative, element-specific contrast at lower spatial resolution. We formulate a joint variational framework that integrates these two modalities into a single nonlinear least-squares problem with shared spatial variables. This formulation enforces cross-modal consistency between structural and compositional estimates, improving conditioning and promoting stable convergence. The resulting optimization couples complementary contrast mechanisms (i.e., phase and absorption from ptychography, elemental composition from fluorescence) within a unified inverse model. Numerical experiments on simulated data demonstrate that the joint reconstruction achieves faster convergence, sharper and more quantitative reconstructions, and lower relative error compared with separate inversions. The proposed approach illustrates how multimodal variational formulations can enhance stability, resolution, and interpretability in computational X-ray imaging. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_02153 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Joint Variational Framework for Multimodal X-ray Ptychography and Fluorescence Reconstruction Zou, Chengru Eric Buser, Elle Di, Zichao Wendy Xi, Yuanzhe Numerical Analysis Optimization and Control Recovering high-resolution structural and compositional information from coherent X-ray measurements involves solving coupled, nonlinear, and ill-posed inverse problems. Ptychography reconstructs a complex transmission function from overlapping diffraction patterns, while X-ray fluorescence provides quantitative, element-specific contrast at lower spatial resolution. We formulate a joint variational framework that integrates these two modalities into a single nonlinear least-squares problem with shared spatial variables. This formulation enforces cross-modal consistency between structural and compositional estimates, improving conditioning and promoting stable convergence. The resulting optimization couples complementary contrast mechanisms (i.e., phase and absorption from ptychography, elemental composition from fluorescence) within a unified inverse model. Numerical experiments on simulated data demonstrate that the joint reconstruction achieves faster convergence, sharper and more quantitative reconstructions, and lower relative error compared with separate inversions. The proposed approach illustrates how multimodal variational formulations can enhance stability, resolution, and interpretability in computational X-ray imaging. |
| title | A Joint Variational Framework for Multimodal X-ray Ptychography and Fluorescence Reconstruction |
| topic | Numerical Analysis Optimization and Control |
| url | https://arxiv.org/abs/2511.02153 |