3D-deep optical learning: a multimodal and multitask reconstruction framework for optical molecular tomography.
3D-deep optical learning: a multimodal and multitask reconstruction framework for optical molecular tomography.
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DOI：
10.1364/oe.490139
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发表时间：
2023-07
期刊：
影响因子：
3.8
通讯作者：
Shuangchen Li;Beilei Wang;Jingjing Yu;Dizhen Kang;Xuelei He;Hongbo Guo;Xiaowei He
中科院分区：
文献类型：
--
作者：
Shuangchen Li;Beilei Wang;Jingjing Yu;Dizhen Kang;Xuelei He;Hongbo Guo;Xiaowei He
光学分子断层成像(OMT)是一种新兴的成像技术。到目前为止，基于深度学习的重建算法对各种成像对象和光学探头的通用性较差，限制了OMT的发展和应用。本研究基于一种新的映射表示，提出了一种多通道多任务重建框架--3D深度光学学习(3DOL)，通过将其分解为光场恢复和光源重建两个任务来克服OMT在普适性上的局限性。具体地说，原始解剖切片(由计算机断层扫描提供)和成像对象的边界光学测量作为递归卷积神经网络的输入，并行编码以提取多峰特征，并显式合并来自样本内几个轴面的2D信息，这使得3DOL能够识别不同的成像对象。随后，在物体几何约束下恢复光场，然后利用可学习的拉普拉斯算子从恢复的光场中分割光源，以极少的参数获得稳定和高质量的重建结果。这一策略使3DOL能够更好地了解边界光学测量、光场和光源之间的关系，从而提高3DOL在广泛光谱范围内工作的能力。数值模拟、物理模型和活体实验的结果表明，3DOL是一种兼容的层析成像不同对象的深度学习方法。此外，在特定波长下的完全训练的3DOL可以推广到620-900 nm NIR-I窗口的其他光谱。
Optical molecular tomography (OMT) is an emerging imaging technique. To date, the poor universality of reconstruction algorithms based on deep learning for various imaged objects and optical probes limits the development and application of OMT. In this study, based on a new mapping representation, a multimodal and multitask reconstruction framework-3D deep optical learning (3DOL), was presented to overcome the limitations of OMT in universality by decomposing it into two tasks, optical field recovery and luminous source reconstruction. Specifically, slices of the original anatomy (provided by computed tomography) and boundary optical measurement of imaged objects serve as inputs of a recurrent convolutional neural network encoded parallel to extract multimodal features, and 2D information from a few axial planes within the samples is explicitly incorporated, which enables 3DOL to recognize different imaged objects. Subsequently, the optical field is recovered under the constraint of the object geometry, and then the luminous source is segmented by a learnable Laplace operator from the recovered optical field, which obtains stable and high-quality reconstruction results with extremely few parameters. This strategy enable 3DOL to better understand the relationship between the boundary optical measurement, optical field, and luminous source to improve 3DOL's ability to work in a wide range of spectra. The results of numerical simulations, physical phantoms, and in vivo experiments demonstrate that 3DOL is a compatible deep-learning approach to tomographic imaging diverse objects. Moreover, the fully trained 3DOL under specific wavelengths can be generalized to other spectra in the 620-900 nm NIR-I window.