SP-XTIN: A single projection grating-based X-ray tri-contrast imaging network

  • Linhai Xu
  • , Changsheng Zhang
  • , Yu Liu
  • , Gang Zhao
  • , Shengping Yuan
  • , Wei Guan
  • , Jian Fu*
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Background and Objective: Grating-based X-ray imaging (GBXI) enables the acquisition of tri-contrast signals—absorption, phase, and dark- field—making it highly promising for applications in clinical diagnostics. However, traditional GBXI requires phase stepping of gratings, leading to high radiation doses. In this study, a single projection grating-based X-ray tri-contrast imaging network (SP-XTIN) is proposed. Methods: A Pix2pixHD-based architecture is adopted, and a multi-task learning strategy is employed to transform the generator into a multi-output model that can simultaneously generate tri-contrast images. Additionally, an edge loss term is integrated into the loss function to enhance edge preservation in the tri-contrast images. Results: The proposed SP-XTIN is validated on two experimental datasets: one acquired with synchrotron radiation (SR) and another using a laboratory X-ray tube source. For the SR dataset, the feature similarity index measure (FSIM) values for absorption, phase, and dark-field signals achieved were 0.9871, 0.9863, and 0.9786, respectively. Using the laboratory X-ray tube source dataset, the FSIM values were 0.9883, 0.9670, and 0.9631. Conclusion: The proposed SP-XTIN is effective in advancing GBXI technology. These results highlight its effectiveness and are expected to contribute to the further development of this field.

Original languageEnglish
Article number108718
JournalComputer Methods and Programs in Biomedicine
Volume264
DOIs
StatePublished - Jun 2025

Keywords

  • Contrast signal retrieval
  • Edge loss
  • Grating-based X-ray imaging
  • Multi-task learning
  • Pix2pixHD

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