Name: Extracting Metrics for Three-dimensional Root Systems: Volume and Surface Analysis from In-soil X-ray Computed Tomography Data (Video) | JoVE Journal
Uploaded: 2016-04-26T15:00:00.000+00:00
Duration: 9 min 37 s
Description: Learn to extract quantitative root data from in-soil X-ray computed tomography using non-invasive imaging techniques. This method enables detailed study of plant root structures in their native environment.

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5 minutes

This work was performed in the Environmental Molecular Sciences Laboratory, a national scientific user facility sponsored by the Department of Energy's Office of Biological and Environmental Research and located at Pacific Northwest National Laboratory.

Caution: The operation of an X-ray tomography scanner requires both general radiation training, and instrument-specific radiation safety training. All corresponding procedures relevant to the experimenter&#39;s laboratory should be followed.&#160;
1. Root Imaging
Note: This step describes the imaging of a grass specimen held in its original soil in a tubular plastic pot (a plastic tube with a diameter of 40 mm, a height of 210 mm, and wall thickness of about 2 mm).
<ol>
	<li>Place potted plant on the sample manipulator of the instrument at a distance desired for target magnification. For a plant in a 2-inch diameter holder, the sample to source distance should be about 3 inches (7 cm).</li>
	<li>Adjust the X-ray scan settings to achieve optimal color (grey level) contrast in detector image. Note: These settings are available in the instrument control software used.
	<ol>
		<li>Set X-ray power settings; 85 kV and 190 &#956;A were used in this example.</li>
		<li>Set exposure time. Here, a relatively long exposure time of 1 sec was used for better signal-to-noise ratio.</li>
		<li>Set number of projections and frames per projection; 4 frames per projection for a total of 3,142 projections is suggested for good data statistics.</li>
		<li>Run a shading correction using the measurement conditions set above by selecting the &#34;Shading correction&#34; tab, and clicking &#34;Create&#34;. 
		Note: The shading correction compensates for the variation in response of the imaging device&#39;s pixels when illuminated with a constant set of X-ray flux. The process takes blank images (with sample removed from beam path) with the X-ray beam turned on, and with the beam turned off. This correction is applied to all images collected.</li>
		<li>Select the &#34;Minimize ring artefacts&#34; option (also called &#34;shuttling mode&#34;); the sample will be rotated in angular steps whilst the projection images are acquired. This leads to data acquisition at a slower rate, but helps eliminate ring artefacts.</li>
		<li>Start the scan by clicking the &#34;Acquire&#34; button under the Acquisition tab (with the settings outlined above, image collection will take approximately 4 hr).</li>
	</ol>
	</li>
</ol>
2. Data Reconstruction&#160;
Note: This section describes the reconstruction of 3D volume data from the raw images (radiographs from the CT scan).
<ol>
	<li>Load the raw data into the program.</li>
	<li>Compare first and last image (they should be nearly identical as the last image is taken after a 360o rotation of the specimen) to make sure the specimen did not move or the scan settings did not change during data acquisition.</li>
	<li>Calculate the center of rotation (COR) by selecting the &#34;Center of rotation&#34; tab, and clicking &#34;Start&#34;; use options &#34;Automatic&#34; COR finding with &#34;High Quality&#34; accuracy, and &#34;Dual&#34; (upper and lower) slice selection for COR calculation.</li>
	<li>Select the sample volume to be reconstructed: select the &#34;Volume&#34; tab, and edit the volume selection windows using the thumbnails.</li>
	<li>Perform reconstruction to create the volume file containing 3D data by clicking &#34;Start&#34;.</li>
</ol>
3. Data Processing / Segmentation
Note: This section describes the steps to be taken to prepare the reconstructed data for further processing in the program RooTrak to track roots as they branch out through soil, and isolate the roots from any surrounding material to produce a stack of binary images of just the root itself.
<ol>
	<li>Processing of volume data in ImageJ to prepare a RooTrak processable image stack:
	<ol>
		<li>Load the volume file into ImageJ.</li>
		<li>Optimize image contrast between root and soil by adjusting brightness and contrast settings (Click Image/Adjust/Brightness/Contrast). When the region of interest within the image is visible and clearly distinguishable, the settings are considered optimized.</li>
		<li>Save as an image stack in jpeg, bmp, or png format.</li>
	</ol>
	</li>
	<li>Processing in RooTrak to segment the root:
	<ol>
		<li>Load image stack into RooTrak (go to &#34;Tools&#34; tab, and press &#34;Tracker&#34;).</li>
		<li>Set seed points inside root: click several points inside each of the pertinent root sections visible in the top view slice of the volume data.</li>
		<li>Set tracker parameters &#34;Smoothness&#34; and &#34;Similarity&#34; to 0.3 and 0.8, respectively.</li>
		<li>Run the tracking function. This will follow the root from the top image slice all the way to the bottom slice.</li>
		<li>After viewing the volume data, select the number of slices according to the useable data volume; in this case, tracking was stopped at 200 slices, equivalent to a depth of 6.2 mm, where root boundaries became ill-defined (the image of the tracked root started to blend into that of the soil). 
		Note: The image stack produced will be saved automatically to wherever the output directory was created.</li>
	</ol>
	</li>
</ol>
4. Volume and Surface Analysis
Note: This step describes the isosurface mesh generation from the image stack created by RooTrak.
<ol>
	<li>Convert image stack from RooTrak into a binary image format in ImageJ. Select &#34;Process&#34;, then &#34;Binary&#34;, then &#34;Make Binary&#34;.</li>
	<li>Use the open-source ImageJ plugin, BoneJ, to create the triangular mesh; in ImageJ select &#34;Plugins&#34;, then &#34;BoneJ&#34;, then &#34;Isosurface&#34;.</li>
	<li>Set &#34;Resampling&#34; and &#34;Threshold&#34; to 6 and 120, respectively (Default settings). Check &#34;Show surface&#34;, and press the &#34;OK&#34; button.</li>
	<li>On the &#34;3D viewer&#34; click on the File tab, then on &#34;Export surfaces&#34;, then save as &#34;STL (binary)&#34;.</li>
	<li>Open imeshJ, select the STL file and enter voxel size in microns. Click &#34;Calculate Surface Area&#34; to acquire total sample root surface area in mm2. Similarly, click &#34;Calculate Volume&#34; to obtain total sample root volume in mm3.</li>
</ol>

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<li>Farrar, J., Hawes, M., Jones, D., Lindow, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((How+roots+control+the+flux+of+carbon+to+the+rhizosphere%5BTitle%5D)+AND+%22Ecology%22%5BJournal%5D)">How roots control the flux of carbon to the rhizosphere.</a> Ecology. 84 (4), 827-837 (2003).</li>
<li>Gregory, P. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Roots+rhizosphere+and+soil%3A+the+route+to+a+better+understanding+of+soil+science%3F%5BTitle%5D)+AND+%22Eur.+J.+Soil+Sci%22%5BJournal%5D)">Roots rhizosphere and soil: the route to a better understanding of soil science?</a> Eur. J. Soil Sci. 57 (1), 2-12 (2006).</li>
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<li>Mairhofer, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((RooTrak%3A+Automated+Recovery+of+Three-Dimensional+Plant+Root+Architecture+in+Soil+from+X-Ray+Microcomputed+Tomography+Images+Using+Visual+Tracking%5BTitle%5D)+AND+%22Plant+Physiol%22%5BJournal%5D)">RooTrak: Automated Recovery of Three-Dimensional Plant Root Architecture in Soil from X-Ray Microcomputed Tomography Images Using Visual Tracking.</a> Plant Physiol. 158 (2), 561-569 (2012).</li>
<li><a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=%22Soil-Water-Root+Processes%3A+Advances+in+Tomography+and+Imaging%22">Soil-Water-Root Processes: Advances in Tomography and Imaging</a>. Anderson, S. H., Hopmans, J. W. , Soil Science Society of America. United States. (2013).</li>
<li>Mairhofer, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Recovering+complete+plant+root+system+architectures+from+soil+via+X-ray+mu-Computed+Tomography%5BTitle%5D)+AND+%22Plant+Methods%22%5BJournal%5D)">Recovering complete plant root system architectures from soil via X-ray mu-Computed Tomography.</a> Plant Methods. 9, 8(2013).</li>
<li>Lorensen, W. E., Cline, H. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Marching+cubes%3A+a+high+resolution+3D+surface+construction+algorithm%5BTitle%5D)+AND+%22Comput.+Graph%22%5BJournal%5D)">Marching cubes: a high resolution 3D surface construction algorithm.</a> Comput. Graph. 21 (4), 163-169 (1987).</li>
<li>ImageJ: Image Processing and Analysis in Java. , Available from: 
	
	 <a target="_blank" href="http://imagej.nih.gov/ij">http://imagej.nih.gov/ij</a> (2014).</li>
<li>Lobet, G., Draye, X., Perilleux, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((An+online+database+for+plant+image+analysis+software+tools%5BTitle%5D)+AND+%22Plant+Methods%22%5BJournal%5D)">An online database for plant image analysis software tools.</a> Plant Methods. 9 (38), (2013).</li>
<li>Schmidt, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+iRoCS+Toolbox+-+3D+analysis+of+the+plant+root+apical+meristem+at+cellular+resolution%5BTitle%5D)+AND+%22Plant+J%22%5BJournal%5D)">The iRoCS Toolbox - 3D analysis of the plant root apical meristem at cellular resolution.</a> Plant J. 77 (5), 806-814 (2014).</li>
<li>Galkovskyi, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((GiA+Roots%3A+software+for+the+high+throughput+analysis+of+plant+root+system+architecture%5BTitle%5D)+AND+%22BMC+Plant+Biol%22%5BJournal%5D)">GiA Roots: software for the high throughput analysis of plant root system architecture.</a> BMC Plant Biol. 12, 116(2012).</li>
<li>Clark, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((3-Dimensional+Root+Phenotyping+with+a+Novel+Imaging+and+Software+Platform%5BTitle%5D)+AND+%22Plant+Physiol%22%5BJournal%5D)">3-Dimensional Root Phenotyping with a Novel Imaging and Software Platform.</a> Plant Physiol. 156, 455-465 (2011).</li>
<li>RootSnap!. , CID Bio-Science. Available from: 
	
	 <a target="_blank" href="https://www.cid-inc.com">https://www.cid-inc.com</a> (2013).</li>
<li>Skye Leaf Area and Analysis Systems and Root Length Measurement System. , Skye Instruments Limited. Available from: 
	
	 <a target="_blank" href="http://www.skyeinstruments.com/products/plant-analysis-systems/leaf-arearoot-length-systems">http://www.skyeinstruments.com/products/plant-analysis-systems/leaf-arearoot-length-systems</a> (2014).</li>
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We have nothing to disclose.

A combination of X-ray computed tomography and several open-source programs proved to be a powerful combination to noninvasively image plant root samples, segment root data, and extract quantitative information (surface area and volume) from the 3D data. Our ability to visualize and measure features is always limited by scan resolution, as well as by limitations of the RooTrak software. However, scan resolution was sufficient to capture the majority of the features of the sample in this study, and RooTrak was able to suc...

Roots, as part of the rhizosphere2-5, represent an &#34;invisible&#34; part of plant biology since soil makes it difficult to image roots non-invasively 6, 7. However, studying root growth and interaction within the soil environment is critical to understanding root/plant growth and nutrient cycling, which in turn affect forestation, food security, and climate. X-ray computed tomography (XCT) has proven to be a valuable tool for noninvasive imaging of plant root samples in their local environments 8. In order to measure root development and dimensional changes under different conditions, and be able to compare data from different datasets/specimens, one needs to extract quantitative information from the tomography data. Segmentation of the root data from that of the surrounding soil, that is, the isolation of the root image from everything else around it (including, for example, a neighboring plant) is a critical step before accurate size analysis can be done. However, a simple thresholding approach is often unfeasible for root data. The challenges associated with imaging plant roots in soil include variations in the X-ray attenuation properties of the root material, and the overlap in attenuation values between root and soil caused by water and organic matter. These issues have been superbly addressed recently by Mairhofer et al. in their visual tracking tool RooTrak 7, 9. The next step after a successful segmentation is the accurate determination of root volume and surface area. The volume may be estimated by counting the number of voxels and multiplying by the voxels&#39; size cubed as shown before7. For a more accurate determination of root surface area and volume, the isosurface of the segmented root system can be represented by a mesh of triangles, using an algorithm known as Marching Cubes 10. The open-source ImageJ11 can be employed to approximate the root volume based on the Marching Cubes algorithm. To the best of our knowledge, only a limited number of open-source software dedicated to calculating tomography-based volume/surface data for root specimens in the centimeter range and above is currently available 12. One open-source software we looked at13 focuses on root growth and is aimed at cellular features enabling quantitative volume analysis at single-cell resolution. Some open-source software dedicated to whole root systems14 is excellent for small-diameter tubular root systems based on the approximation that their shape is actually tubular. However, some work with 2D images and are unable to handle 3D stacks14. Furthermore, the tubular shape approximation may not be valid when root systems with rough surfaces and non-uniform shapes, such as those of trees, are studied. Another approach15 uses two-dimensional (2D) rotational image sequences innovatively circumventing the need for a costly CT scanner. It measures, records, and displays root system lengths. The software we have tested from those only available commercially16-18; one does not appear to be able to handle 3D image stacks16, the second is a leaf area and root length measurement tool17, while the third is based on color analysis18. Based on this survey, we suggest that a costless option that approximates the surface and volume of the root regardless of its shape from 3D tomography data is desirable.
Building on the freely available RooTrak and ImageJ, we have developed a program, named imeshJ (see Supplemental Code File) which processes an isosurface mesh (surface stereolithography file) generated from segmented root data, and calculates the volume and surface area of the root by doing simple geometrical calculations on the mesh triangle index data. Here we report a method that combines the use of XCT imaging, data reconstruction and visualization (software CT Pro 3D and VG Studio), segmentation of the root of the specimen from the soil in the 3D data (open-source software ImageJ and RooTrak), and extraction of the surface and volume information from a triangular mesh (ImageJ and the computer code imeshJ).

<table><tbody><tr><td>X-Tek/Metris XTH 320/225 kV&amp;nbsp;</td><td>Nikon Metrology</td><td></td><td>X-ray tomography scanner</td></tr><tr><td>Inspect X</td><td>Nikon Metrology</td><td></td><td>Instrument control software</td></tr><tr><td>CT Pro 3D</td><td>Nikon Metrology</td><td></td><td>Reconstruction software, version XT 2.2</td></tr><tr><td>VG Studio MAX</td><td>Visual Graphics GmbH</td><td></td><td>Visualization software for 3D volumes, version 2.1.5</td></tr><tr><td>ImageJ</td><td>Open-source</td><td></td><td>Image processing and analysis software, version 1.6</td></tr><tr><td>RooTrak</td><td>Open-source</td><td></td><td>Root segmentation software, version 0.3.1-b1 beta</td></tr><tr><td>imeshJ</td><td>EMSL</td><td></td><td>MATLAB script developed by the authors</td></tr><tr><td>Prairie dropseed grass sample</td><td></td><td></td><td>Sample obtained from ground in residential area</td></tr></tbody></table>

extracting metrics for three-dimensional root systems: volume and surface analysis from in-soil x-ray computed tomography data

Extracting Metrics for Three-dimensional Root Systems: Volume and Surface Analysis from In-soil X-ray Computed Tomography Data

Plant roots play a critical role in plant-soil-microbe interactions that occur in the rhizosphere, as well as processes with important implications to climate change and crop management. Quantitative size information on roots in their native environment is invaluable for studying root growth and environmental processes involving plants. X-ray computed tomography (XCT) has been demonstrated to be an effective tool for in situ root scanning and analysis. We aimed to develop a costless and efficient tool that approximates the surface and volume of the root regardless of its shape from three-dimensional (3D) tomography data. The root structure of a Prairie dropseed (Sporobolus heterolepis) specimen was imaged using XCT. The root was reconstructed, and the primary root structure was extracted from the data using a combination of licensed and open-source software. An isosurface polygonal mesh was then created for ease of analysis. We have developed the standalone application imeshJ, generated in MATLAB1, to calculate root volume and surface area from the mesh. The outputs of imeshJ are surface area (in mm2) and the volume (in mm3). The process, utilizing a unique combination of tools from imaging to quantitative root analysis, is described. A combination of XCT and open-source software proved to be a powerful combination to noninvasively image plant root samples, segment root data, and extract quantitative information from the 3D data. This methodology of processing 3D data should be applicable to other material/sample systems where there is connectivity between components of similar X-ray attenuation and difficulties arise with segmentation.

The specimen consisting of two stems of the native grass Prairie dropseed (Sporobolus heterolepis) and the original soil around it was taken from a residential area and placed in a small tube-shaped holder seen in Figure 1. The reconstructed data voxel size was approximately 31 µm x 31 µm x 31 µm. The reconstructed volume file was used to create a stack of images from a selected orientation (top view) using the open-source image-processing program ImageJ 1.6...

A methodology for obtaining visual and quantitative root structure information from X-ray computed tomography data acquired in-soil is presented.

Plant roots play a critical role in plant-soil-microbe interactions that occur in the rhizosphere, as well as processes with important implications to ...

The overall goal of this procedure is to extract quantitative information on plant roots from in-soil X-ray computed tomography data. This method of investigating the root structure of live plants can help answer key questions in the biology field, such as how plants respond to changing environmental conditions like drought, microbial activities, and so forth. The main advantage of this technique is that it is non-invasive, therefore, it allows us to study plants in their native environment, the soil.Visual demonstration of this method is valuable, as the procedure involves numerous steps, including tomographic imaging, data reconstruction, and analysis, which may be obscure without visual aid. To begin, place a potted plant on the sample manipulator of the X-ray tomography scanner at a distance desired for target magnification. For a plant in a two inch diameter holder, set a sample to a source distance of about three inches.In the instrument control software, set the X-ray power settings to 85 kilovolts and 190 microamps. Then, set the exposure time to one second for a better signal to noise ratio. Set the number of projections to 3, 142 and the number of frames per projection to four for good data statistics.Using the scan parameters just set, select the Shading Correction"tab. Then click on New"and name the shading correction file. Then click Present"to confirm the new conditions.Next, click Create"and click OK"to using two reference images. Then click Generate. Then, when the warning popup window appears, physically remove the sample from the beam path before clicking OK.The shading correction will now run.Now, click on the Acquisition"tab. Name the data file, then select None"for X-ray filter. Ensure that the Minimise ring artefacts"checkbox is checked so that the sample will be rotated in angular steps while the projection images are acquired.Finally, start the scan by clicking the Acquire"button. After acquiring the raw data, load it into the reconstruction program. Compare the first and last images by clicking on Blink"to make sure the specimen did not move or the scan settings did not change during data acquisition.Calculate the center of rotation, or COR, by selecting the Center of rotation"tab and using options Automatic"COR finding with High Quality"accuracy and Dual"slice selection for COR calculation. Then, click Start"to run the calculation. After the COR is found, click the Volume"tab to select the sample to be reconstructed.Use the thumbnails to edit the volume selection windows. Then click Start"to perform reconstruction to create the volume file containing 3D data. To prepare a RooTrak processable image stack, load the volume file into ImageJ by clicking File, Import, Raw, then selecting the volume file.To optimize the image contrast between the root and soil, click the Image"tab, then choose Adjust Brightness/Contrast"and select Auto. When the region of interest is visible in clear, the settings are considered optimized. Save the images as an image stack in JPEG, BMP, or PNG format by clicking Save As"and choosing Image Sequence.To process in RooTrak to segment the root, select the Tools"tab and choose Tracker. Then double click on the white entry area of the dialogue box to load an image stack. After the images are loaded, choose the Tracker"tab and set seed points inside the root by clicking several points inside each of the pertinent root sections visible in the top view slice of the volume data.Set the tracker parameter smoothness to 0.3 and similarity to 0.8. Click the field below Output Directory"to choose a file folder. Then, click Select Folder"and click Start.The tracking function will then run. This will follow the root from the top image slice all the way to the bottom slice. After opening and viewing the volume data, select the number of slices according to the usable data volume.In the example shown here, tracking was stopped at 200 slices, equivalent to a depth of 6.2 millimeters, where root boundaries became ill defined. To carry out volume and surface analysis in ImageJ, convert the image stack from RooTrak into a binary image format by selecting Process, Binary, then Make Binary. Use the open source ImageJ plugin, BoneJ, to create the triangular mesh by selecting Plugins, BoneJ, then Isosurface.In the options window that opens up, set Resampling"and Threshold"to six and 120, respectively. Then, check Show surface"and press the OK"button. In the 3D viewer that opens up, click on the File"tab.Then click Export surfaces"and save as STL binary. Finally, open IMESHJ. Load the STL file by clicking Select STL File"and enter the voxel size in microns.Click Enter Voxel Size"and enter 31 microns, then click OK"twice. Click Calculate Surface Area"to acquire the total sample root surface area in square millimeters. Then, click Calculate Volume"to obtain the total sample root volume in cubic millimeters.The specimen, consisting of two stems of the native grass prairie dropseed, Sporobolus heterolepis, and the original soil around it, is shown here. Voxel size of the reconstructed data generated was approximately 31 cubic microns. After creating a stack of images and increasing the contrast between the roots and soil, the reconstructed data showed that the root and some of the components of the soil have very similar X-ray attenuation factors, resulting in little to no grayscale contrast in the images.This figure shows a representative seed point that was selected inside a pertinent root section visible in the top slice of the volume data. Using the RooTrak parameters described in this video, RooTrak successfully segmented the selected approximately 200 slices of volume data, which was equivalent to a depth of 6.2 millimeters. Segmentation of the root is also demonstrated in this animation.Here, ImageJ was used to generate a triangular mesh isosurface of the 3D volume from the data produced by RooTrak. The default settings used produced a detailed isosurface in a relatively quick manner. Finally, using IMESHJ, the surface area was calculated as 351.87 square millimeters, and the calculated volume was 47.27 cubic millimeters.Once mastered, this procedure can be done in five hours if it is performed properly. While attempting this technique, it is important to remember to follow all safety procedures. It is also important to remember that root segmentation is only possible if there is sufficient X-ray contrast between root and the soil.Following this procedure, other specimens with branching structures like tree leaves, animal and human lungs, et cetera, can be imaged and analyzed in order to answer additional questions, like how living tissues grow or how they tolerate certain conditions. After its development, this technique paves the way for researchers in the plant science field to explore things like drought resistance in model plants such as Brachypodium. After watching this video, you should have a good understanding of how to image a plant specimen using tomography, how to visualize its root structure and extract quantitative information on the root from in-soil X-ray computed tomography data.Don't forget that working with X-ray instrumentation can be hazardous, and the relevant safety procedures should be followed while attempting this technique.

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Micron-scale Phenotyping Techniques of Maize Vascular Bundles Based on X-ray Microcomputed Tomography

The protocol describes procedures to acquire high-spatial resolution computed tomography (CT) images of a granular soil during triaxial compression, and to apply image processing techniques to these CT images to explore the grain-scale mechanical behavior of the soil under loading.

Visualization of Failure and the Associated Grain-Scale Mechanical Behavior of Granular Soils under Shear using Synchrotron X-Ray Micro-Tomography

This protocol presents a workflow for sub-mm 2D visualization of multiple labile inorganic nutrient and contaminant solute species using diffusive gradients in thin films (DGT) combined with mass spectrometry imaging. Solute sampling and high-resolution chemical analysis are described in detail for quantitative mapping of solutes in the rhizosphere of terrestrial plants.

Two-Dimensional Visualization and Quantification of Labile, Inorganic Plant Nutrients and Contaminants in Soil

Micro-CT is a non-destructive tool that can analyze plant structures in three dimensions. The present protocol describes the sample preparation to leverage micro-CT to analyze parasitic plant structure and function. Different species are used to highlight the advantages of this method when coupled with specific preparations.

Leveraging Micro-CT Scanning to Analyze Parasitic Plant-Host Interactions

Here we show how to process tree cores with an X-ray computed tomography toolchain. Except for chemical extraction for some purposes, no further physical lab treatment is needed. The toolchain can be used for biomass estimations, for obtaining MXD/tree-ring width data as well as for obtaining quantitative wood anatomy data.

Tree Core Analysis with X-ray Computed Tomography

Learn to extract quantitative root data from in-soil X-ray computed tomography using non-invasive imaging techniques. This method enables detailed study of plant root structures in their native environment.

Extracting Metrics for Three-dimensional Root Systems: Volume and Surface Analysis from In-soil X-ray Computed Tomography Data

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