Significance: Spatial frequency-domain imaging (SFDI) is a powerful technique for mapping tissue oxygen saturation over a wide field of view. However, current SFDI methods either require a sequence of several images with different illumination patterns or, in the case of single-snapshot optical properties (SSOP), introduce artifacts and sacrifice accuracy. Aim: We introduce OxyGAN, a data-driven, content-aware method to estimate tissue oxygenation directly from single structured-light images. Approach: OxyGAN is an end-to-end approach that uses supervised generative adversarial networks. Conventional SFDI is used to obtain ground truth tissue oxygenation maps for ex vivo human esophagi, in vivo hands and feet, and an in vivo pig colon sample under 659- and 851-nm sinusoidal illumination. We benchmark OxyGAN by comparing it with SSOP and a two-step hybrid technique that uses a previously developed deep learning model to predict optical properties followed by a physical model to calculate tissue oxygenation. Results: When tested on human feet, cross-validated OxyGAN maps tissue oxygenation with an accuracy of 96.5%. When applied to sample types not included in the training set, such as human hands and pig colon, OxyGAN achieves a 93% accuracy, demonstrating robustness to various tissue types. On average, OxyGAN outperforms SSOP and a hybrid model in estimating tissue oxygenation by 24.9% and 24.7%, respectively. Finally, we optimize OxyGAN inference so that oxygenation maps are computed ∼10 times faster than previous work, enabling video-rate, 25-Hz imaging. Conclusions: Due to its rapid acquisition and processing speed, OxyGAN has the potential to enable real-time, high-fidelity tissue oxygenation mapping that may be useful for many clinical applications.

中文翻译：

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通过对抗性深度学习，从快照结构光图像中快速绘制组织氧合图


意义：空间频域成像（SFDI）是一种强大的技术，可以在宽视场内绘制组织氧饱和度图。然而，当前的 SFDI 方法要么需要具有不同照明模式的多个图像序列，要么在单快照光学属性 (SSOP) 的情况下引入伪影并牺牲精度。目标：我们介绍 OxyGAN，这是一种数据驱动、内容感知的方法，可直接从单个结构光图像估计组织氧合。方法：OxyGAN 是一种使用监督生成对抗网络的端到端方法。传统的 SFDI 用于在 659 和 851 nm 正弦照明下获得离体人类食道、体内手和脚以及体内猪结肠样本的地面真实组织氧合图。我们通过将 OxyGAN 与 SSOP 和两步混合技术进行比较来对 OxyGAN 进行基准测试，该技术使用先前开发的深度学习模型来预测光学特性，然后使用物理模型来计算组织氧合。结果：在人脚上进行测试时，经过交叉验证的 OxyGAN 绘制的组织氧合图准确度为 96.5%。当应用于训练集中未包含的样本类型（例如人手和猪结肠）时，OxyGAN 的准确率达到 93%，证明了对各种组织类型的稳健性。平均而言，OxyGAN 在估计组织氧合方面分别优于 SSOP 和混合模型 24.9% 和 24.7%。最后，我们优化了 OxyGAN 推理，使氧合图的计算速度比之前的工作快约 10 倍，从而实现视频速率、25 Hz 成像。结论：由于其快速采集和处理速度，OxyGAN 有潜力实现实时、高保真组织氧合图，这可能对许多临床应用有用。