Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:3109-3113. doi: 10.1109/EMBC46164.2021.9630213.
Bioluminescence tomography (BLT) is an effective noninvasive molecular imaging modality for three dimensional visualization of in vivo tumor research in small animals. The approaches of deep learning have shown great potential in the field of optical molecular imaging in recent years. However, the common problem with these existing end-to-end networks is the black box technology, whose solving process is not theoretically proven. In this work, we proposed a novel Alternating Direction Method of Multipliers Network (ADMM-Net) to solve the poor interpretation problem of internal process. The ADMM-Net combines the framework of deep learning on the basis of traditional ADMM algorithm to dynamically learn various parameters of the algorithm in the form of network. To evaluate the performance of our proposed network, we implemented numerical simulation experiments. The results show that the ADMM-Net can accurately reconstruct the location of the source, and the morphological similarity with the real source is also higher.
生物发光断层扫描（BLT）是一种有效的非侵入性分子成像方式，可用于小动物体内肿瘤研究的三维可视化。近年来，深度学习方法在光学分子成像领域显示出巨大的潜力。然而，这些现有端到端网络的一个共同问题是黑盒技术，其求解过程在理论上没有得到证明。在这项工作中，我们提出了一种新的交替方向乘子网络（ADMM-Net）来解决内部过程解释能力差的问题。ADMM-Net 在传统 ADMM 算法的基础上结合了深度学习的框架，以网络的形式动态学习算法的各种参数。为了评估我们提出的网络的性能，我们进行了数值模拟实验。结果表明，ADMM-Net 可以准确地重建源的位置，并且与真实源的形态相似性也更高。
