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<p>Despite the growing interest in multicontrast X-ray imaging, <i>spatial harmonic imaging</i> remains limited by a lack of specialized computational resources. In this paper, we present <i>SHI</i>, a high-performance software framework that covers the range from data acquisition to processing in spatial harmonic imaging experiments. <i>SHI</i> is an open-source software package that facilitates the acquisition of precise measurement data and streamlines the workflow, ensuring that data can be efficiently organized, processed, and visualized, leading to high quality results. In addition, <i>SHI</i> includes higher-order harmonic extraction. Preliminary results show that spatial harmonic imaging improves experimental robustness and retrieves refraction and scattering information, albeit with reduced resolution. However, using these lower-resolution images enables faster CT reconstruction with fewer projections, while preserving essential sample features and allowing a substantial reduction in exposure levels. This research focuses on the acquisition methodology and the subsequent data processing for contrast retrieval and multicontrast computed tomography.</p>

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SHI: a framework for spatial harmonic imaging

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月29日

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  • Jorge Luis Beltran Diaz,
  • Jan G. Korvink,
  • Danays Kunka

摘要

Despite the growing interest in multicontrast X-ray imaging, spatial harmonic imaging remains limited by a lack of specialized computational resources. In this paper, we present SHI, a high-performance software framework that covers the range from data acquisition to processing in spatial harmonic imaging experiments. SHI is an open-source software package that facilitates the acquisition of precise measurement data and streamlines the workflow, ensuring that data can be efficiently organized, processed, and visualized, leading to high quality results. In addition, SHI includes higher-order harmonic extraction. Preliminary results show that spatial harmonic imaging improves experimental robustness and retrieves refraction and scattering information, albeit with reduced resolution. However, using these lower-resolution images enables faster CT reconstruction with fewer projections, while preserving essential sample features and allowing a substantial reduction in exposure levels. This research focuses on the acquisition methodology and the subsequent data processing for contrast retrieval and multicontrast computed tomography.