Significance: Sub-diffuse optical properties may serve as useful cancer biomarkers, and wide-field heatmaps of these properties could aid physicians in identifying cancerous tissue. Sub-diffuse spatial frequency domain imaging (sd-SFDI) can reveal such wide-field maps, but the current time cost of experimentally validated methods for rendering these heatmaps precludes this technology from potential real-time applications. Aim: Our study renders heatmaps of sub-diffuse optical properties from experimental sd-SFDI images in real time and reports these properties for cancerous and normal skin tissue subtypes. Approach: A phase function sampling method was used to simulate sd-SFDI spectra over a wide range of optical properties. A machine learning model trained on these simulations and tested on tissue phantoms was used to render sub-diffuse optical property heatmaps from sd-SFDI images of cancerous and normal skin tissue. Results: The model accurately rendered heatmaps from experimental sd-SFDI images in real time. In addition, heatmaps of a small number of tissue samples are presented to inform hypotheses on sub-diffuse optical property differences across skin tissue subtypes. Conclusion: These results bring the overall process of sd-SFDI a fundamental step closer to real-time speeds and set a foundation for future real-time medical applications of sd-SFDI such as image guided surgery.

中文翻译：

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使用机器学习辅助空间频域成像对癌性和正常皮肤组织的亚漫射光学特性进行成像


意义：亚漫射光学特性可以作为有用的癌症生物标志物，这些特性的广域热图可以帮助医生识别癌组织。次漫射空间频域成像 (sd-SFDI) 可以揭示此类宽视场图，但目前用于渲染这些热图的经过实验验证的方法的时间成本阻碍了该技术的潜在实时应用。目标：我们的研究实时渲染来自实验 sd-SFDI 图像的亚漫射光学特性的热图，并报告癌性和正常皮肤组织亚型的这些特性。方法：使用相函数采样方法来模拟各种光学特性的 sd-SFDI 光谱。在这些模拟上训练并在组织模型上进行测试的机器学习模型被用来从癌性和正常皮肤组织的 sd-SFDI 图像中渲染亚漫反射光学特性热图。结果：该模型实时准确地渲染实验 sd-SFDI 图像的热图。此外，还提供了少量组织样本的热图，以便为有关皮肤组织亚型之间的亚漫射光学特性差异的假设提供信息。结论：这些结果使 sd-SFDI 的整个过程向实时速度迈进了根本性的一步，并为 sd-SFDI 未来的实时医疗应用（例如图像引导手术）奠定了基础。