scholarly journals X-Ray Computed Tomography Reveals the Response of Root System Architecture to Soil Texture

2016 ◽  
Vol 171 (3) ◽  
pp. 2028-2040 ◽  
Author(s):  
Eric D. Rogers ◽  
Daria Monaenkova ◽  
Medhavinee Mijar ◽  
Apoorva Nori ◽  
Daniel I. Goldman ◽  
...  
2013 ◽  
Vol 370 (1-2) ◽  
pp. 35-45 ◽  
Author(s):  
Susan Zappala ◽  
Stefan Mairhofer ◽  
Saoirse Tracy ◽  
Craig J. Sturrock ◽  
Malcolm Bennett ◽  
...  

Plant Methods ◽  
2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Shota Teramoto ◽  
Satoko Takayasu ◽  
Yuka Kitomi ◽  
Yumiko Arai-Sanoh ◽  
Takanari Tanabata ◽  
...  

2014 ◽  
Vol 13 (8) ◽  
pp. vzj2014.03.0024 ◽  
Author(s):  
Nicolai Koebernick ◽  
Ulrich Weller ◽  
Katrin Huber ◽  
Steffen Schlüter ◽  
Hans-Jörg Vogel ◽  
...  

2021 ◽  
Author(s):  
Mon-Ray Shao ◽  
Ni Jiang ◽  
Mao Li ◽  
Anne Howard ◽  
Kevin Lehner ◽  
...  

ABSTRACTThe root system is critical for the survival of nearly all land plants and a key target for improving abiotic stress tolerance, nutrient accumulation, and yield in crop species. Although many methods of root phenotyping exist, within field studies one of the most popular methods is the extraction and measurement of the upper portion of the root system, known as the root crown, followed by trait quantification based on manual measurements or 2D imaging. However, 2D techniques are inherently limited by the information available from single points of view. Here, we used X-ray computed tomography to generate highly accurate 3D models of maize root crowns and created computational pipelines capable of measuring 71 features from each sample. This approach improves estimates of the genetic contribution to root system architecture, and is refined enough to detect various changes in global root system architecture over developmental time as well as more subtle changes in root distributions as a result of environmental differences. We demonstrate that root pulling force, a high-throughput method of root extraction that provides an estimate of root biomass, is associated with multiple 3D traits from our pipeline. Our combined methodology can therefore be used to calibrate and interpret root pulling force measurements across a range of experimental contexts, or scaled up as a stand-alone approach in large genetic studies of root system architecture.


2012 ◽  
Vol 110 (2) ◽  
pp. 511-519 ◽  
Author(s):  
Saoirse R. Tracy ◽  
Colin R. Black ◽  
Jeremy A. Roberts ◽  
Craig Sturrock ◽  
Stefan Mairhofer ◽  
...  

Microscopy ◽  
2021 ◽  
Author(s):  
Tomofumi Kurogane ◽  
Daisuke Tamaoki ◽  
Sachiko Yano ◽  
Fumiaki Tanigaki ◽  
Toru Shimazu ◽  
...  

Abstract Plant roots change their morphological traits in order to adapt themselves to different environmental conditions, resulting in alteration of the root system architecture. To understand this mechanism, it is essential to visualize morphology of the entire root system. To reveal effects of long-term alteration of gravity environment on root system development, we have performed an experiment in the International Space Station using Arabidopsis plants and obtained dried root systems grown in rockwool slabs. X-ray computed tomography (CT) technique using industrial X-ray scanners has been introduced to visualize root system architecture of crop species grown in soil in 3D non-invasively. In the case of the present study, however, root system of Arabidopsis is composed of finer roots compared with typical crop plants and rockwool is also composed of fibers having similar dimension to that of the roots. A higher spatial resolution imaging method is required for distinguishing roots from rockwool. Therefore, in the present study, we tested refraction-contrast X-ray micro-CT using coherent X-ray optics available at the beamline of the synchrotron radiation facility SPring-8 for bio-imaging. We have found that wide field of view but with low resolution obtained at the experimental Hutch 3 of this beamline provided an overview map of the root systems, while narrow field of view but with high resolution obtained at the experimental Hutch 1 provided extended architecture of the secondary roots, by clear distinction between roots and individual rockwool fibers, resulting in successful tracing of these roots from their basal regions.


2020 ◽  
Author(s):  
Maxime Phalempin ◽  
Eva Lippold ◽  
Doris Vetterlein ◽  
Steffen Schlueter

<p>X-ray computed tomography (CT) is acknowledged as a powerful tool for the study of root system architecture (RSA) of plants grown in soil. The study of the root system properties is however only possible after performing root segmentation, i.e. the binarization of all root voxels. Root segmentation is often regarded as a tedious and difficult task as its success depends on several factors such as the image resolution, the signal to noise during image acquisition and the gray value contrast between the roots and all other surrounding features. Here, we present an improved method for the segmentation of roots from X-Ray computed tomography 3D images. The algorithm Rootine (Gao et al. 2019) does not detect roots by their gray values but by their characteristic tubular shape. This algorithm was further developed in order to improve the root recovery rate and to reduce the number of parameters involved during the segmentation process. This was achieved by adding two key steps: (1) an absolute difference transform and (2) an automatic calculation of the parameters used during the Gaussian smoothing. The first step allows for targeting specific features based on a gray value criteria contained within a user-defined gray value range in order to better distinguish roots from pores whereas the second step allows for targeting root segments of specific diameters. On the benchmark dataset of Gao et al. 2019, the newly called “Rootine v.2” was able to recover 34 % more roots as compared to its preceding version. Moreover, the number of parameters was reduced from 10 down to 5 which allows for a faster calibration and an overall better usability of the algorithm. The presented method also allows for a more reliable estimation of root diameter derived from X-Ray CT images. This work was carried out in the framework of the priority programme 2089 “Rhizosphere spatiotemporal organization - a key to rhizosphere functions” funded by DFG (project number 403640293).</p>


2020 ◽  
Author(s):  
Maxime Phalempin ◽  
Eva Lippold ◽  
Doris Vetterlein ◽  
Steffen Schlüter

Abstract BackgroundX-ray computed tomography is acknowledged as a powerful tool for the study of root system architecture of plants growing in soil. In this paper, we improved the original root segmentation algorithm “Rootine” and present its succeeding version “Rootine v.2”. In addition to grey value information, Rootine algorithms are based on shape detection of cylindrical roots. Both algorithms are macros for the ImageJ software and are made freely available to the public. New features in Rootine v.2 are (1) a pot wall detection and removal step to avoid segmentation artefacts for roots growing along the pot wall, (2) a calculation of the root average grey value based on a histogram analysis, (3) an automatic calculation of thresholds for hysteresis thresholding of the tubeness image to reduce the number of parameters and (4) a false negatives recovery based on shape criteria to increase root recovery. We compare the segmentation results of Rootine v.1 and Rootine v.2 with the results of root washing and subsequent analysis with WinRhizo. We use a benchmark dataset of maize roots (Zea mays L. cv. B73) grown in repacked soil for two scenarios with differing soil heterogeneity and image quality. ResultsWe demonstrate that Rootine v.2 outperforms its preceding version in terms of root recovery and enables to match better the root diameter distribution data obtained with root washing. Despite a longer processing time, Rootine v.2 comprises less user-defined parameters and shows an overall greater usability. ConclusionThe proposed method facilitates higher root detection accuracy than its predecessor and has the potential for improving high-throughput root phenotyping procedures based on X-ray CT data analysis.


2020 ◽  
Author(s):  
Shota Teramoto ◽  
Satoko Takayasu ◽  
Yuka Kitomi ◽  
Yumiko Arai-Sanoh ◽  
Takanari Tanabata ◽  
...  

Abstract Background: X-ray computed tomography (CT) allows us to visualize root system architecture (RSA) beneath the soil, non-destructively and in a three-dimensional (3-D) form. However, CT scanning, reconstruction processes, and root isolation from X-ray CT volumes, take considerable time. For genetic analyses, such as quantitative trait locus mapping, which require a large population size, a high-throughput RSA visualization method is required. Results: We have developed a high-throughput process flow for the 3-D visualization of rice (Oryza sativa) RSA (consisting of radicle and crown roots), using X-ray CT. The process flow includes use of a uniform particle size, calcined clay to reduce the possibility of visualizing non-root segments, use of a higher tube voltage and current in the X-ray CT scanning to increase root-to-soil contrast, and use of a 3-D median filter and edge detection algorithm to isolate root segments. Using high-performance computing technology, this analysis flow requires only 10 min (33 s, if a rough image is acceptable) for CT scanning and reconstruction, and 2 min for image processing, to visualize rice RSA. This reduced time allowed us to conduct the genetic analysis associated with 3-D RSA phenotyping. In 2-week-old seedlings, 85% and 100% of radicle and crown roots were detected, when 16 cm and 20 cm diameter pots were used, respectively. The X-ray dose per scan was estimated at < 0.09 Gy, which did not impede rice growth. Using the developed process flow, we were able to follow daily RSA development, i.e., 4-D RSA development, of an upland rice variety, over three weeks. Conclusions: We developed a high-throughput process flow for 3-D rice RSA visualization by X-ray CT. The X-ray dose assay on plant growth has shown that this methodology could be applicable for 4-D RSA phenotyping. We named the RSA visualization method ‘RSAvis3D’ and are confident that it represents a potentially efficient application for 3-D RSA phenotyping of various plant species.


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