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<p>Optical diffraction tomography (ODT) reconstructs the 3D refractive index (RI) distribution of transparent microsamples using angle-scanned holographic complex field measurements, enabling quantitative and label-free 3D imaging. High-quality ODT typically requires low-coherence illumination combined with a common-path, preferably shearing holographic setup to ensure stable interference. However, shearing configurations are limited to sparse samples due to their reliance on object-free regions for self-interference. Moreover, low coherence necessitates small shears, pushing many approaches towards gradient-based imaging that usually relies on error-prone phase integration and <i>z</i>-scanning, achieving only quasi-3D visualization. In this work we present Gradient Optical Diffraction Tomography (GODT) – a rigorous tomographic method that directly reconstructs the 3D RI derivative from the set of phase gradient measurements. GODT is validated with simulations and experiments on nano-printed cell phantom and fixed neural cells. It is shown that GODT can reveal fine sample structure with enhanced contrast and sensitivity to RI variations.</p>

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Gradient optical diffraction tomography

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月17日

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  • Julianna Winnik,
  • Piotr Zdańkowski,
  • Marzena Stefaniuk,
  • Azeem Ahmad,
  • Chao Zuo,
  • Balpreet S. Ahluwalia,
  • Maciej Trusiak

摘要

Optical diffraction tomography (ODT) reconstructs the 3D refractive index (RI) distribution of transparent microsamples using angle-scanned holographic complex field measurements, enabling quantitative and label-free 3D imaging. High-quality ODT typically requires low-coherence illumination combined with a common-path, preferably shearing holographic setup to ensure stable interference. However, shearing configurations are limited to sparse samples due to their reliance on object-free regions for self-interference. Moreover, low coherence necessitates small shears, pushing many approaches towards gradient-based imaging that usually relies on error-prone phase integration and z-scanning, achieving only quasi-3D visualization. In this work we present Gradient Optical Diffraction Tomography (GODT) – a rigorous tomographic method that directly reconstructs the 3D RI derivative from the set of phase gradient measurements. GODT is validated with simulations and experiments on nano-printed cell phantom and fixed neural cells. It is shown that GODT can reveal fine sample structure with enhanced contrast and sensitivity to RI variations.