Kinetically Consistent Data Assimilation for Plant PET Sparse Time Activity Curve Signals

Nicola D'Ascenzo; Qingguo Xie; Emanuele Antonecchia; Mariachiara Ciardiello; Giancarlo Pagnani; Michele Pisante

doi:10.3389/fpls.2022.882382

Kinetically Consistent Data Assimilation for Plant PET Sparse Time Activity Curve Signals

Front Plant Sci. 2022 Jul 22:13:882382. doi: 10.3389/fpls.2022.882382. eCollection 2022.

Authors

Nicola D'Ascenzo^{1

2}, Qingguo Xie^{1

2

3}, Emanuele Antonecchia^{1

2}, Mariachiara Ciardiello², Giancarlo Pagnani⁴, Michele Pisante⁴

Affiliations

¹ School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China.
² Department of Medical Physics and Engineering, Istituto Neurologico Mediterraneo, Istituto di Ricovero e Cura a Carattere Scientifico, Pozzilli, Italy.
³ Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China.
⁴ Faculty of Bioscience and Technology for Food, Agriculture and Environment, University of Teramo, Teramo, Italy.

Abstract

Time activity curve (TAC) signal processing in plant positron emission tomography (PET) is a frontier nuclear science technique to bring out the quantitative fluid dynamic (FD) flow parameters of the plant vascular system and generate knowledge on crops and their sustainable management, facing the accelerating global climate change. The sparse space-time sampling of the TAC signal impairs the extraction of the FD variables, which can be determined only as averaged values with existing techniques. A data-driven approach based on a reliable FD model has never been formulated. A novel sparse data assimilation digital signal processing method is proposed, with the unique capability of a direct computation of the dynamic evolution of noise correlations between estimated and measured variables, by taking into explicit account the numerical diffusion due to the sparse sampling. The sequential time-stepping procedure estimates the spatial profile of the velocity, the diffusion coefficient and the compartmental exchange rates along the plant stem from the TAC signals. To illustrate the performance of the method, we report an example of the measurement of transport mechanisms in zucchini sprouts.

Keywords: data assimilation algorithms; data-driven digital signal processing for plant imaging; dynamic plant positron emission tomography; functional plant imaging; kinetic modeling; plant physiology; portable imaging device.