2023-06-07 パデュー大学
◆研究者たちは光と組織の相互作用に基づき、スマートフォンのカメラ画像から完全な可視光スペクトルを再構築することに成功しました。この技術は、モバイルヘルスの分野での応用に期待されています。
<関連情報>
- https://www.purdue.edu/newsroom/releases/2023/Q2/ai-driven-mobile-health-algorithm-uses-phone-camera-to-detect-blood-vessel-oxygen-levels.html
- https://academic.oup.com/pnasnexus/article/2/4/pgad111/7093017
mHealthハイパースペクトル学習による血行動態の瞬間的なスペシオスペクトルイメージング mHealth hyperspectral learning for instantaneous spatiospectral imaging of hemodynamics
Yuhyun Ji, Sang Mok Park, Semin Kwon, Jung Woo Leem, Vidhya Vijayakrishnan Nair, Yunjie Tong, Young L Kim
PNAS Nexus Published:29 March 2023
DOI:https://doi.org/10.1093/pnasnexus/pgad111
Abstract
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.
