Significance: Spatial frequency domain imaging (SFDI) is an imaging modality that projects spatially modulated light patterns to determine optical property maps for absorption and reduced scattering of biological tissue via a pixel-by-pixel data acquisition and analysis procedure. Compressive sensing (CS) is a signal processing methodology which aims to reproduce the original signal with a reduced number of measurements, addressing the pixel-wise nature of SFDI. These methodologies have been combined for complex heterogenous data in both the image detection and data analysis stage in a compressive sensing SFDI (cs-SFDI) approach, showing reduction in both the data acquisition and overall computational time. Aim: Application of CS in SFDI data acquisition and image reconstruction significantly improves data collection and image recovery time without loss of quantitative accuracy. Approach: cs-SFDI has been applied to an increased heterogenic sample from the AppSFDI data set (back of the hand), highlighting the increased number of CS measurements required as compared to simple phantoms to accurately obtain optical property maps. A novel application of CS to the parameter recovery stage of image analysis has also been developed and validated. Results: Dimensionality reduction has been demonstrated using the increased heterogenic sample at both the acquisition and analysis stages. A data reduction of 30% for the cs-SFDI and up to 80% for the parameter recover was achieved as compared to traditional SFDI, while maintaining an error of <10 % for the recovered optical property maps. Conclusion: The application of data reduction through CS demonstrates additional capabilities for multi- and hyperspectral SFDI, providing advanced optical and physiological property maps.

中文翻译：

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压缩感知在空间频域成像中的应用

意义：空间频域成像 (SFDI) 是一种成像模式，它投射空间调制光图案，通过逐像素数据采集和分析程序确定生物组织吸收和减少散射的光学特性图。压缩感知 (CS) 是一种信号处理方法，旨在通过减少测量次数重现原始信号，解决 SFDI 的逐像素性质。这些方法已在压缩传感 SFDI (cs-SFDI) 方法中的图像检测和数据分析阶段结合用于复杂的异构数据，显示出数据采集和整体计算时间的减少。目的：CS 在 SFDI 数据采集和图像重建中的应用在不损失定量精度的情况下显着提高了数据采集和图像恢复时间。方法：cs-SFDI 已应用于 AppSFDI 数据集（手背）中增加的异质样本，突出显示与简单的幻影相比，准确获得光学特性图所需的 CS 测量数量增加。还开发并验证了 CS 在图像分析参数恢复阶段的新应用。结果：已在采集和分析阶段使用增加的异质样本证明了降维。与传统 SFDI 相比，cs-SFDI 的数据减少了 30%，参数恢复的数据减少了 80%，同时保持了 < 10% 用于恢复的光学特性图。结论：通过 CS 进行数据缩减的应用展示了多光谱和高光谱 SFDI 的附加功能，提供了先进的光学和生理特性图。