We introduce a novel image reconstruction method for time-resolved diffuse optical tomography (DOT) that yields submillimeter resolution in less than a second. This opens the door to high-resolution real-time DOT in imaging of the brain activity. We call this approach the sensitivity equation based noniterative sparse optical reconstruction (SENSOR) method. The high spatial resolution is achieved by implementing an asymptotic l₀-norm operator that guarantees to obtain sparsest representation of reconstructed targets. The high computational speed is achieved by employing the nontruncated sensitivity equation based noniterative inverse formulation combined with reduced sensing matrix and parallel computing. We tested the new method with numerical and experimental data. The results demonstrate that the SENSOR algorithm can achieve 1 mm³ spatial-resolution optical tomographic imaging at depth of ∼60 mean free paths (MFPs) in 20∼30 milliseconds on an Intel Core i9 processor.

我们介绍了一种用于时间分辨漫反射光学断层扫描 (DOT) 的新型图像重建方法，可在不到一秒的时间内产生亚毫米分辨率。这为大脑活动成像中的高分辨率实时 DOT 打开了大门。我们称这种方法为基于灵敏度方程的非迭代稀疏光学重建 (SENSOR) 方法。高空间分辨率是通过实现渐近l ₀-norm 运算符，保证获得重构目标的最稀疏表示。通过采用基于非截断灵敏度方程的非迭代逆公式结合简化的传感矩阵和并行计算来实现高计算速度。我们用数值和实验数据测试了新方法。结果表明，在英特尔酷睿 i9 处理器上，SENSOR 算法可以在 20~30 毫秒内在~60 个平均自由程 (MFP) 的深度上实现 1 mm ³空间分辨率的光学断层成像。