mHealth hyperspectral learning for instantaneous spatiospectral imaging of hemodynamics.
mHealth hyperspectral learning for instantaneous spatiospectral imaging of hemodynamics.
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DOI：
10.1093/pnasnexus/pgad111
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发表时间：
2023-04
期刊：
影响因子：
--
通讯作者：
Kim, Young L.
中科院分区：
文献类型：
--
作者：
Ji, Yuhyun;Park, Sang Mok;Kwon, Semin;Leem, Jung Woo;Nair, Vidhya Vijayakrishnan;Tong, Yunjie;Kim, Young L.
关键词：
高光谱成像在空间和频率域中获取数据，以提供丰富的物理或生物信息。然而，传统的高光谱成像具有体积庞大的仪器，缓慢的数据采集速率和空间光谱权衡的固有局限性。在这里，我们介绍了高光谱学习的快照高光谱成像，其中采样的高光谱数据在一个小的子区域被纳入一个学习算法来恢复超立方体。高光谱学习利用了照片不仅仅是一张图片并且包含详细光谱信息的想法。高光谱数据的小采样使得光谱信息学习能够在没有完整的高光谱测量的情况下从红-绿-蓝（RGB）图像恢复超立方体。超光谱学习能够在超立方体中恢复完整的光谱分辨率，与科学光谱仪的高光谱分辨率相当。高光谱学习还可以实现超快动态成像，利用现成的智能手机中的超低视频记录，因为视频包括多个RGB图像的时间序列。为了证明其多功能性，血管发育的实验模型用于通过统计和深度学习方法提取血液动力学参数。随后，使用传统的智能手机相机，以高达毫秒的超快时间分辨率评估外周微循环的血液动力学。这种频谱信息学习方法类似于压缩感知;然而，它还允许使用透明的学习算法进行可靠的超立方体恢复和关键特征提取。这种学习驱动的快照高光谱成像方法产生高光谱和时间分辨率，消除了空间光谱的权衡，提供了简单的硬件要求和各种机器学习技术的潜在应用。
Hyperspectral imaging acquires data in both the spatial and frequency domains to offer abundant physical or biological information. However, conventional hyperspectral imaging has intrinsic limitations of bulky instruments, slow data acquisition rate, and spatiospectral trade-off. Here we introduce hyperspectral learning for snapshot hyperspectral imaging in which sampled hyperspectral data in a small subarea are incorporated into a learning algorithm to recover the hypercube. Hyperspectral learning exploits the idea that a photograph is more than merely a picture and contains detailed spectral information. A small sampling of hyperspectral data enables spectrally informed learning to recover a hypercube from a red–green–blue (RGB) image without complete hyperspectral measurements. Hyperspectral learning is capable of recovering full spectroscopic resolution in the hypercube, comparable to high spectral resolutions of scientific spectrometers. Hyperspectral learning also enables ultrafast dynamic imaging, leveraging ultraslow video recording in an off-the-shelf smartphone, given that a video comprises a time series of multiple RGB images. To demonstrate its versatility, an experimental model of vascular development is used to extract hemodynamic parameters via statistical and deep learning approaches. Subsequently, the hemodynamics of peripheral microcirculation is assessed at an ultrafast temporal resolution up to a millisecond, using a conventional smartphone camera. This spectrally informed learning method is analogous to compressed sensing; however, it further allows for reliable hypercube recovery and key feature extractions with a transparent learning algorithm. This learning-powered snapshot hyperspectral imaging method yields high spectral and temporal resolutions and eliminates the spatiospectral trade-off, offering simple hardware requirements and potential applications of various machine learning techniques.
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DOI：
10.1093/pnasnexus/pgac164
发表时间：
2022-09
期刊：
PNAS NEXUS
影响因子：
--
作者：
Du, Xiaoxi;Koronyo, Yosef;Mirzaei, Nazanin;Yang, Chengshuai;Fuchs, Dieu-Trang;Black, Keith L.;Koronyo-Hamaoui, Maya;Gao, Liang
通讯作者：
Gao, Liang
影响因子：
64.8
作者：
Bao, Jie;Bawendi, Moungi G.
通讯作者：
Bawendi, Moungi G.
影响因子：
16.6
作者：
Hadoux, Xavier;Hui, Flora;van Wijngaarden, Peter
通讯作者：
van Wijngaarden, Peter
影响因子：
3.4
作者：
He, Qinghua;Wang, Ruikang K.
通讯作者：
Wang, Ruikang K.
影响因子：
3.6
作者：
Finlay, JC;Foster, TH
通讯作者：
Foster, TH