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您的检索式：作者名="Xavier Intes"
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| 1 | 基于联合代数重建技术的介观荧光分子层析成像显示文摘针对介观荧光分子层析成像重建问题,提出了一种基于联合代数重建技术的介观荧光分子层析成像重建方法。首先应用主成分分析算法对敏感矩阵进行双重降维操作,以消除敏感矩阵中的冗余信息;其次为保持重建结果与目标数据的一致性,对降维后的矩阵进行零填充;最后应用荧光光强测量数据和填充后的敏感矩阵,经带有总变差正则化项的联合代数重建技术重建组织中荧光探针的三维分布。为了评估所提方法的性能,设计了计算机数值仿体实验和血管树实验。实验结果表明,文中所提方法既提高了算法的重建精度和抗噪声性能又缩短了重建时间。因此,文中所提方法适用于介观荧光分子层析成像重建研究。 | 杨福刚 陈洋 Denzel Faulkner Xavier Intes | 2021 | 光电子.激光2021，32，2： | 1 |
| 2 | The integration of 3-D cell printing and mesoscopic fluorescence molecular tomography of vascular constructs within thick hydrogel scaffolds显示文摘 | Lingling Zhao Vivian K Lee Seung-Schik Yoo Guohao Dai Xavier Intes | | 0，，07： | 1 |
| 3 | Net-FLICS:fast quantitative wide-field fluorescence lifetime imaging with compressed sensing–a deep learning approach显示文摘Macroscopic fluorescence lifetime imaging(MFLI)via compressed sensed(CS)measurements enables efficient and accurate quantification of molecular interactions in vivo over a large field of view(FOV).However,the current dataprocessing workflow is slow,complex and performs poorly under photon-starved conditions.In this paper,we propose Net-FLICS,a novel image reconstruction method based on a convolutional neural network(CNN),to directly reconstruct the intensity and lifetime images from raw time-resolved CS data.By carefully designing a large simulated dataset,Net-FLICS is successfully trained and achieves outstanding reconstruction performance on both in vitro and in vivo experimental data and even superior results at low photon count levels for lifetime quantification. | Ruoyang Yao Marien Ochoa Pingkun Yan Xavier Intes | 2019 | Light(Science & Applications)2019，8，1： | 1 |
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