Nizam Navid Ibtehaj, Ochoa Marien, Smith Jason T, Intes Xavier
Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA.
Biomed Opt Express. 2023 Feb 7;14(3):1041-1053. doi: 10.1364/BOE.480091. eCollection 2023 Mar 1.
Widefield illumination and detection strategies leveraging structured light have enabled fast and robust probing of tissue properties over large surface areas and volumes. However, when applied to diffuse optical tomography (DOT) applications, they still require a time-consuming and expert-centric solving of an ill-posed inverse problem. Deep learning (DL) models have been recently proposed to facilitate this challenging step. Herein, we expand on a previously reported deep neural network (DNN) -based architecture (modified AUTOMAP - ModAM) for accurate and fast reconstructions of the absorption coefficient in 3D DOT based on a structured light illumination and detection scheme. Furthermore, we evaluate the improved performances when incorporating a micro-CT structural prior in the DNN-based workflow, named Z-AUTOMAP. This Z-AUTOMAP significantly improves the widefield imaging process's spatial resolution, especially in the transverse direction. The reported DL-based strategies are validated both and in experimental phantom studies using spectral micro-CT priors. Overall, this is the first successful demonstration of micro-CT and DOT fusion using deep learning, greatly enhancing the prospect of rapid data-integration strategies, often demanded in challenging pre-clinical scenarios.
利用结构光的宽场照明和检测策略能够在大表面积和体积上对组织特性进行快速且稳健的探测。然而，当应用于漫射光学层析成像（DOT）应用时，它们仍然需要对一个不适定的逆问题进行耗时且以专家为中心的求解。最近有人提出深度学习（DL）模型来促进这一具有挑战性的步骤。在此，我们基于之前报道的基于深度神经网络（DNN）的架构（改进的自动映射 - ModAM）进行扩展，用于基于结构光照和检测方案在三维DOT中准确快速地重建吸收系数。此外，我们评估了在基于DNN的工作流程中纳入微型CT结构先验（名为Z - AUTOMAP）时性能的提升。这种Z - AUTOMAP显著提高了宽场成像过程的空间分辨率，尤其是在横向方向。所报道的基于深度学习的策略在模拟和使用光谱微型CT先验的实验体模研究中均得到了验证。总体而言，这是首次成功展示利用深度学习实现微型CT和DOT融合，极大地提升了在具有挑战性的临床前场景中经常需要的快速数据整合策略的前景。
