@Article{CiCP-28-2,
author = {Weizhi, Qi and Yao, Lei and Yunchao, Jiang and Na, Huang and Heng, Guo and Rong, Jian and Hui, Feng and Yang, Wanneng and Xi, Lei},
title = {Quantitative Photoacoustic Imaging of Chlorophyll Using a GPU-Accelerated Finite Element Method},
journal = {Communications in Computational Physics},
year = {2020},
volume = {28},
number = {2},
pages = {679--690},
abstract = {<p style="text-align: justify;">Chlorophyll in leaves is tightly associated with physiological status of plants.
Chemical extraction or hyperspectral estimation is the conventional method to estimate the concentration of Chlorophyll in leaves. However, chemical extraction is invasive and time consuming, and hyperspectral method is extremely sensitive to background light. In this paper, we develop a quantitative photoacoustic imaging technique based on a finite-element-based reconstruction algorithm accelerated by a multicore GPU card to image morphological features and derive distribution of Chlorophyll
A in rice leaves. The results suggest that this new method holds great potential in various studies of plant physiology.</p>},
issn = {1991-7120},
doi = {https://doi.org/10.4208/cicp.OA-2017-0248},
url = {https://global-sci.com/article/79699/quantitative-photoacoustic-imaging-of-chlorophyll-using-a-gpu-accelerated-finite-element-method}
}