X-ray computed tomography for 3D plant imaging. (November 2021)

• Record Type: Journal Article
• Authors: Piovesan, Agnese; Vancauwenberghe, Valérie; Van De Looverbosch, Tim; Verboven, Pieter; Nicolaï, Bart
• Is Part Of: Trends in plant science. Volume 26:Number 11(2021)
• Page Start: 1171
• Page End: 1185
• Publication Date: 2021-11
• Subjects: plant microstructure -- digital model -- multiscale modeling -- image database; Botany -- Periodicals; Botanique -- Périodiques; Botany; Periodicals; 580.5
• DOI: 10.1016/j.tplants.2021.07.010
• Languages: English
• ISSNs: 1360-1385

Abstract

Abstract : X-ray computed tomography (CT) is a valuable tool for 3D imaging of plant tissues and organs. Applications include the study of plant development and organ morphogenesis, as well as modeling of transport processes in plants. Some challenges remain, however, including attaining higher contrast for easier quantification, increasing the resolution for imaging subcellular features, and decreasing image acquisition and processing time for high-throughput phenotyping. In addition, phase contrast, multispectral, dark-field, soft X-ray, and time-resolved imaging are emerging. At the same time, a large amount of 3D image data are becoming available, posing challenges for data management. We review recent advances in the area of X-ray CT for plant imaging, and describe opportunities for using such images for studying transport processes in plants. Highlights: High-resolution X-ray computed tomography (CT) instruments are increasingly becoming available to plant research laboratories for structural phenotyping experiments at the macro-, micro-, and nanoscales. New developments in contrast agents, phase contrast, and soft X-ray imaging improve image contrast and allow the visualization of previously unresolved structural features Time-resolved X-ray CT techniques are emerging and permit dynamic imaging in real time owing to improvements in acquisition and image reconstruction. Automatic image-processing algorithms based on machine and deep learning are starting to appear and replace conventional image processing based on often cumbersome and manually operated segmentation procedures. X-ray CT-based mathematical models are being developed to compute the effect of plant tissue structure on relevant physiological processes such as respiration and photosynthesis.