School of Life Sciences, Tiangong University, Tianjin 300387, China.
School of Electronic and Information Engineering, Tiangong University, Tianjin 300387, China.
Rev Sci Instrum. 2022 Apr 1;93(4):044102. doi: 10.1063/5.0056883.
A Concurrent-wavelength Reconstruction Algorithm (CRA) based on multi-wavelength information fusion is proposed in this paper that aims to further improve the accuracy of Fluorescence Molecular Tomography (FMT) reconstruction. Combining multi-spectral data with FMT technology, the information of 650 and 750 nm wavelengths near-infrared was used to increase the feature information of the dominant 850 nm wavelength near-infrared effectively. Principal component analysis, which can remove redundant information and achieve data dimensionality reduction, was then utilized to extract the feature information. Finally, tomographic reconstruction of the anomalies was performed based on the stacked auto-encoder neural network model. The comparison results of numerical experiments showed that the reconstruction effect of CRA was superior to the performance of the single wavelength model. The correlation coefficient between CRA reconstructed anomalies' fluorescence yield values and the real fluorescence yield values remained at 0.95 or more under the noise of different levels of signal-to-noise ratios. Therefore, the CRA proposed in this paper could effectively improve on the ill-posedness of the inverse problem, which could further enhance the accuracy of FMT reconstruction.
本文提出了一种基于多波长信息融合的共波长重建算法（CRA），旨在进一步提高荧光分子断层扫描（FMT）重建的准确性。该算法将多光谱数据与 FMT 技术相结合，利用近红外 650nm 和 750nm 两个波长的光谱数据，有效增加了主波长 850nm 近红外的特征信息量。通过主成分分析（PCA）去除冗余信息并实现数据降维，提取特征信息。最后，基于堆叠自编码器神经网络模型进行异常体的断层重建。数值实验的对比结果表明，CRA 的重建效果优于单波长模型。在不同信噪比水平的噪声下，CRA 重建异常的荧光产率值与真实荧光产率值之间的相关系数始终保持在 0.95 或更高。因此，本文提出的 CRA 可以有效改善反问题的不适定性，从而进一步提高 FMT 重建的准确性。
