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.
An X-ray computed tomography (CT) toolchain is presented to obtain tree-ring width (TRW), maximum latewood density (MXD), other density parameters, and quantitative wood anatomy (QWA) data without the need for labor-intensive surface treatment or any physical sample preparation. The focus here is on increment cores and scanning procedures at resolutions ranging from 60 µm down to 4 µm. Three scales are defined at which wood should be looked at: (i) inter-ring scale, (ii) ring scale, i.e., tree-ring analysis and densitometry scale, as well as (iii) anatomical scale, the latter approaching the conventional thin-section quality. Custom-designed sample holders for each of these scales enable high-throughput scanning of multiple increment cores. A series of software routines were specifically developed to efficiently treat three-dimensional X-ray CT images of the tree cores for TRW and densitometry. This work briefly explains the basic principles of CT, which are needed for a proper understanding of the protocol. The protocol is presented for some known species that are commonly used in dendrochronology. The combination of rough density estimates, TRW and MXD data, as well as quantitative anatomy data, allows us to broaden and deepen current analyses for climate reconstructions or tree response, as well as further develop the field of dendroecology/climatology and archeology.
Wood density is an easy-to-measure variable1 that reflects both the anatomical and chemical properties of the wood2. In biomass estimations of aboveground biomass, wood density is an important weighing variable 3,4,5, that is multiplied with the dimensions of the tree and a factor representing the carbon content of the wood. Wood density is tightly linked to the mechanical properties of timber6 and reflects the life history of a tree7.
Cell wall density is measured as being approximately 1500 kg/m³ and is considered fairly constant8, however intra-ring cell wall density variations should be considered as well8,9.Woody cells (in general tracheids in conifers, vessels, parenchyma and fibers in hardwoods) are oriented/shaped in different ways and cell wall thickness and lumen size of these cells varies10. Therefore, wood density varies between trees, within a tree (axial and transversal) and within short intervals within a tree ring11,12. In many cases the wood density variation at the ring scale also delimits the tree ring boundary13. Wood density and ultimately tissue fractions are generated and in this paper are broadly put into three categories (i.e., three different resolution scales), depending on the study goal (Figure 1) as described below.
Inter-ring scale: By measuring pieces of wood, a single value is obtained for that sample. This can be done through water immersion or geometrically14. This way, general biomass or wood technological variables can be obtained. To include pith-to-bark variation, these pieces of wood can be further divided into blocks that are measured manually to obtain information on the life history strategy15. When switching to low-resolution X-ray CT such as in medical scanners17,18, TRW data on medium-to-wide rings can be made in an efficient way on many samples18,19,20. This is also the scale that can be used to assess biomass from pith-to-bark from both temperate and tropical trees4,22, typically ranging in resolutions from 50 µm to 200 µm.
Ring scale: Wood is a recorder of past environmental conditions. The best known parameter is tree-ring width (TRW), but for global temperature reconstructions, maximum latewood density (MXD) records are proven to be a better proxy for temperature22. MXD is an easy-to-measure variable23, and a proxy for cell wall thickness and cell size on the last cells of a tree ring, and are at tree line and boreal sites positively linked to seasonal air temperature24: the warmer and longer the summers, more cell wall lignification occurs which thus increases the density of these last cells. Traditional measurements such as immersion and geometry are less accurate to determine this ring-level density. A previous work developed a toolchain for using X-ray film on thin-cut samples25. This sparked a revolution in both forestry and later paleoclimatology15,18, defining maximum latewood density (MXD), i.e., the peak density value often at the end of a ring, as a proxy for summer temperature. The basic principle is that the samples are sawn (approximately 1.2 mm to 7 mm13) to be perfectly parallel to the axial direction, and the sample is put on a sensitive film exposed to an X-ray source. Then these radiography films are read out through a light source that detects the intensity and saves the profiles and the annual tree ring parameters. These tools, however, require a significant amount of sample preparation and manual work. Recently this has been developed for X-ray CT in a more standardized way or based on mounted cores26. Resolution here ranges between 10 µm and 20 µm. TRW is measured on this scale as well, especially when dealing with smaller rings.
Anatomical scale: At this scale (resolution < 4 µm), the average density levels become less relevant as the main anatomical features are visualized and their width and proportions can be measured. Typically, this is done through making microsections or high-resolution optical scans or µ-CT scans. When the ultrastructure of the cell walls needs to be visualized, scanning electron microscopy is the most commonly used method27. At the anatomical scale, the individual tissue fractions become visible so that physiological parameters can be derived from the images. Based on the individual anatomical parameters and the cell wall density of wood, anatomical density can be derived for comparison with conventional estimators of wood density24.
Due to improved sectioning techniques and image software29,30, dendro-anatomy30 has been developed to have a more accurate record of the wood, both to have a closer estimate of the MXD in conifers and to measure several anatomical variables from broadleaf trees. On this scale, actual anatomical parameters are measured and related to environmental parameters31 . With µCT this level can be obtained as well32,33.
As wood is inherently hygroscopic and anisotropic, wood density needs to be carefully defined and the measurement conditions need to be specified, either as oven-dry, conditioned (typically at 12% moisture content) or green (as felled in the forest)34. For large samples and technical purposes, wood density is defined as the weight divided by its volume at given conditions. However, the value of wood density is strongly dependent on the scale at which it is measured, for instance from pith-to-bark wood density can double, and on a ring scale (in conifers) the transition of earlywood to latewood results in a significant rise in wood density as well, with a peak at the ring boundary.
Here, an X-ray CT scanning protocol of increment cores is presented in order to measure features at the aforenoted 3 scales (Figure 1). Recent developments in X-ray CT can cover most of these scales, due to a flexible set-up. The research goals will determine the eventual protocol for scanning.
A crucial limiting factor (which is inherently connected to the scaled nature of wood density and wood in general) is the resolution and time necessary for scanning. Examples demonstrate how to: (i) obtain inter-ring tree scale wood density profiles for biomass estimations in Terminalia superba from the Congo Basin, (ii) obtain density records from Clanwilliam cedar (Widdringtonia cedarbergensis) based on helical scanning on a HECTOR system35, and (iii) measure vessel parameters on sessile oak, on the Nanowood system. Both scanners are part of the suite of scanners at the UGent Center for X-ray Tomography (UGCT,
Figure 1: General methodological decision tree for X-ray CT scanning. The rows indicate the steps to take, starting from the research goal all the way to the final data format. White boxes are the steps that are relevant for this toolchain. Greyed-out boxes are steps that can be performed with other software or R packages, such as dplr47 and Treeclim48 for tree-ring analysis, and ROXAS44 as well as ImageJ42 or other (commercial) applications for deriving wood anatomical parameters based on the CT images. Please click here to view a larger version of this figure.
X-CT research on wood
Set-up of a scanner: A standard X-ray CT scanner consists of an X-ray tube, an X-ray detector, a rotation stage, and a set of motors to move the rotation stage, and in most cases also the detector, back and forth (Figure 2).
Figure 2. The HECTOR scanning system. The system35, showing the source detector distance (SDD) and the source object distance (SOD). Please click here to view a larger version of this figure.
Most lab-based systems have a cone-beam geometry, which means that the produced X-rays are distributed from the tube's exit window in a cone-beam shape, implying that by changing the distance between the object and the tube (SOD = Source-Object-Distance) and the detector and the tube (SDD = Source-Detector-Distance), the magnification is controlled (see the discussion on resolution). Due to the penetrating power of X-rays, they pass through the object, and the intensity of the attenuation beam is a function of the energy of the X-ray beam, the chemical composition of the object (the atomic number of the elements present) and the density of the material. Given a constant energy spectrum and a constant material composition of wood, the attenuation of the X-ray beam is highly dependent on the density of the material, which explains its use for densitometry. The attenuation (or transmission) can be expressed by the Beer-Lambert law:
with I0 the incoming X-ray beam exponentially which decays to a transmitted X-ray beam Id when propagating through the material over a distance d. The linear attenuation coefficient µ depends on a series of interactions with the material of the object. The projections are thus recordings of the transmitted beam.
Practically, the object is mounted on the rotation stage, a proper SOD and SDD are selected, a certain power is selected as well (related to object size, density, and composition) and the object is rotated 360° and during that rotation multiple projections are taken. These projections are then used to reconstruct the interior structure of the object. There are several reconstruction algorithms available, of which the most used ones are still based on the analytical framework developed decades ago, relying on the Radon transformation and the Fourier slice theorem. For more details, the reader is referred to specialized literature36.
Conundrum of resolution, data volume and sample size: Resolution is key in X-ray CT scanning. In systems with inverse geometry, or parallel beam geometry such as synchrotron beamlines, other considerations play a role. This protocol only discusses standard lab-based X-ray CT scanning with cone-beam geometry. Here, the concept of magnification, detector pixel size and spot size are essential. Magnification is defined as the ratio of SDD/SOD. Next, the pixel size of the detector obviously also impacts the resolution: the smaller the pixel size, the higher the resolution, but in most cases the field of view (FoV) is also directly related to pixel size and size of the detector (smaller pixel size, smaller FoV for the same number of pixels). Further, the spot size of the X-ray beam is also important: the larger the spot size, the lower the resolution, which means that less details can be seen.
It is important to address that one could obtain a resolution that is higher than what is possible according to the abovementioned limits, therefore it is better to use the term voxel size (a voxel is a volume pixel) instead of resolution. Moreover, there are other factors at play, such as detector sharpness, which further limit the true resolution at which an object is scanned. Only a true calibration of the system, using established targets, supplies the truthful answer.
In most cases, however, the voxel size at which an object can be scanned is mostly limited by the size of the object. This means that the larger the object, the lower the voxel size will be. If the object does not fit within the FoV of the detector for a certain voxel size, then the voxel size can be reduced, e.g., by limiting the magnification.
Scan time and data volume are important to consider when deciding the desired voxel size. In general, the smaller the voxel size means the higher the detail one wants to see, the smaller the sample or the fewer samples that can be scanned at once, more time is required and larger the data volumes will be collected. Imagine the following theoretical example: one can scan a sample measuring 10 cm x 10 cm x 10 cm at 50 µm at once with a certain X-ray CT system and would like to scan that same volume at 10 µm, the volume that fits within the FoV would only be 2 cm x 2 cm x 2 cm, assuming that this is physically possible. This means that 125 scans are needed (5³ = 5 times higher resolution, scales to the power of 3 due to the volumetric nature of the imaging technique) to cover the entire volume, and that the data volume would increase likewise. Of course, this is only a thought experiment, and one needs to consider much more than only resolution. For more information, the reader is referred to an overview of the scanning possibilities37.
Flexibility of instruments for scanning of wooden objects: In the last decade, many companies have delivered X-ray CT systems with a similar assembly as HECTOR35. An overview of several CT systems, especially assessed for their temporal resolution, is given in38.
In all, the flexibility and user-friendliness of X-ray CT systems has improved considerably. Many systems allow scanning of a diverse range of objects, which is also the case with the systems at UGCT. The protocol below is demonstrated for the HECTOR system, which is suitable for tree-ring analysis. The protocol however is valid for any other available system if resolution and data format allows for it.
These systems allow the scanning of a variety of objects. A few pictures of different wooden objects scanned with the HECTOR system are given in Figure 3. It is this flexibility that comprises the three scales we present in Figure 1, ranging from a coarse resolution to a very fine resolution.
Figure 3. Scanning set-up examples. (A) A log, (B) a cello49, (C) sample holders (type 1) with tree cores for batch scanning and (D) sample holder type 2 with increment cores for helical scanning mounted on the rotation stage of HECTOR. Please click here to view a larger version of this figure.
1. Core sampling
2. Core preparation before scanning
Figure 4. Workflow for extracting and drying cores prior to scanning. (A) Increment cores in paper straws are first put in a hot water bath and subsequently in a hot ethanol-toluene mixture with a Soxhlet apparatus for 24 h then dried, put in a hot water bath for 24 h, then dried again at 103.5 °C, then conditioned before scanning. (B) Images of the adapted Soxhlet set-up at Ghent University. Multiple adapted Soxhlet apparatuses are serially attached. Note the aluminum foil and insulation tubes to keep the solvent or solvent mixture warm enough for extraction. (C) Example of the surface of an increment core from Pinus longaeva before and after extraction. Resins and other extractives mask the true density signal, due to attenuation of the X-rays. Please click here to view a larger version of this figure.
3. Core scanning
Figure 5. Sample holder types and resolution. Design of the three main holder types, that correspond to (A) inter-ring scale, (B) tree ring scale, and (C) anatomical scale. Due to the resulting size, the number of samples decreases with increasing resolution requirement. The corresponding 3D renderings for (D) inter-ring scale (holder 1), (E) ring width and MXD (holder type 2), and (F) anatomical parameters (holder type 3). Scale bar = 5 mm. Please click here to view a larger version of this figure.
4. Obtain TRW data, density data and anatomical data
Figure 6. Obtaining wood density values from grey values. Conversion of the grey values of the image to actual wood density estimates, through calibration with a reference material. (A) Sample holder 1, at 60 µm resolution, showing the air reference, white reference, and the core. (B) Sample holder, at 20 µm, air, reference and core are shown as well. Please click here to view a larger version of this figure.
Figure 7. Pre-processing steps of the 3D core volume. Pre-processing steps of the 3D core volume before visualization and indicating the tree rings. (A) Tilt (axial) correction, (B) tangential correction, with the grey bars indicating the fiber direction, (C) the cropping of the core volume to obtain a volume with only woody voxels. Please click here to view a larger version of this figure.
Figure 8. Screenshot of a core. Screenshot of a core (tangential width approximately 3 mm, resolution µm) opened in the RingIndicator toolbox. Top pane is the transversal view, bottom pane is the radial view. Green bars show the structure indication (in this case on the ring boundary), red line is the density profile. The yellow numbers indicate calendar year, and the blue numbers the generic ring number (counting from the first indication). Please click here to view a larger version of this figure.
Figure 9. Ring indication and density profile visualization in the RingIndicator module. Green lines are indications, red line is the wood density profile. Please click here to view a larger version of this figure.
Figure 10. How to deal with cracks in the cores. (A) When a crack occurs in the middle of the ring, the generic ring number (starting from the pith) will be labeled as a gap, and TRW will be calculated by summing the widths of the two ring parts. Parameters are 3 (number of the ring) and 1 (case 1: crack in the middle of a ring)31. (B) When a crack occurs at a ring boundary, the gap will be omitted from the TRW calculation. Parameters are 3 (number of the ring) and 2 (case 2: crack at the ring boundary)31. Please click here to view a larger version of this figure.
Figure 11. Screenshot of the Core Comparison module. Screenshot of the Core Comparison module, where ring width series are distributed vertically for easy visual comparison. The top pane shows the ring width of 4 cores from Fagus sylvatica, the bottom left pane shows the Gleichlaüfigkeit (GLK) and the bottom right pane the Spearman correlation coefficient. Please click here to view a larger version of this figure.
File name | Explanation | ||
SAMPLENAME_density.txt | Uncorrected density values for the entire length of the image, thus not corrected for ring and fibre angle | ||
SAMPLENAME _density_corr.txt | cf. previous txt but then with corrected density values | ||
SAMPLENAME _ring-and-fibre.txt | ring and fibre indication positions and angles | ||
SAMPLENAME _zpos.txt | position of the tree ring boundaries on your core, without ring and fibre correction | ||
SAMPLENAME _zpos.corr.txt | idem as zpos, but then corrected for ring and fibre angle | ||
SAMPLENAME _ringwidth.txt | calculated ring widths, considering ring and fibre angle |
Table 1. Text files generated by RingIndicator. List of text files that are generated after indicating rings and exporting the density profile in RingIndicator. These should remain in the same folder as the .tiff file in order to be opened in the CoreComparison module.
Tree-ring variable | Explanation | ||
Mean density | mean density per tree ring | ||
Max density half-based | MXD but only based on the second half of the tree ring to avoid that high density values of the previous year are not included | ||
Min density | the minimum density per tree ring | ||
Q1-Q4 | mean and maximum values for each quartile of the intra-annual ring profile |
Table 2. Spreadsheet details. Tree-ring variables that are exported into a spreadsheet named export clustered data.
If the goal is biomass estimation or tree growth increment of many samples, i.e., inter-ring scale (Figure 1), then sample holder 1 (Figure 5) is used to scan samples to obtain density profiles (see step 5.4.3) and estimates of tree growth, e.g., for fast-growing trees with large TRW, which allows for a coarser resolution. Figure 12 shows an example of both pith-to-bark and axial density trends of T. superba, a species from the Congo basin. These diagrams were based on scans of 110 µm from tree increment cores taken at various height positions in the stem.
For the ring scale (Figure 1) that is targeted by scanning cores with sample holder 2 (Figure 5B), tree ring analysis and MXD calculation is possible. An example of this is Figure 13, where a minimum density (in this case linked to wet season precipitation) and maximum density chronology is plotted for cores of W. cedarbergensis from the Cedarberg mountains in South Africa26. This was based on scanning mounted cores at 10 µm resolution.
For the anatomical scale (Figure 1), scanning cores with holder 3, i.e., multiple cores in papers straws wrapped together, allows a resolution of up to 4 µm. The result is shown in Figure 14, where an X-ray CT image slice of oak (Q. robur) is shown, with both earlywood and latewood vessels segmented.
Figure 12. Radial and axial wood density trends. (A) Radial variation of ovendry wood density (kg/m3) from 46 Terminalia superba trees from the Congo Basin (DRC; data extracted from 21), with minimum (min), mean, and maximum (max) values, with a raw profile (red) superposed. (B) Radial variation and axial variation from a single Terminalia superba tree, where at every height interval of 1 m an increment core was taken and scanned (23 cores in total). There is an increasing density trend from pith to bark (highlighted according to colorbar) , and a higher wood density in the upper stem. Scanning resolution = 110 µm. Please click here to view a larger version of this figure.
Figure 13. Chronology development. A minimum density and a maximum latewood density chronology from Widdringtonia cedarbergensis. Data extracted from26. The scanning resolution is 10 µm. Please click here to view a larger version of this figure.
Figure 14. High-resolution scan of an oak sample. Example of a segmentation of oak vessels on Quercus robur scanned at 4 µm. Segmentation was performed using the Octopus analysis software, using bilateral filtering, thresholding, a series of binary shrink and expand operations, Euclidian distance transform and finally labelling of the vessels. Color represents vessel size, ranging from small (dark blue) to large (dark orange). Please click here to view a larger version of this figure.
Critical steps within the protocol
Critical steps within the protocol include proper handling of the increment borer to obtain high-quality increment cores (step 1.1. and see39) to avoid bits and pieces. Next, it is essential that cores be left unmounted (but see26), both for insertion in the sample holder (Figure 5, see21) as well as for proper resin extraction50 and for possible future analysis. Right before scanning (step 2.2.2.), the conditioning of the samples in the scanner room is essential to avoid dimensional deformations due to fluctuations of moisture. Then, once the rings can be indicated in the RingIndicator module, it is essential that the ring boundaries and angles are well indicated, because the density peaks can flatten, similar to inaccurate indications on classic densitometry systems13. The indication is also important to have correct ring width measurements21. The next critical step is proper cross dating through the CoreComparison module (step 4.4.1.). If some cores have wrong indications, there could be an environmental signal loss51.
Modifications and troubleshooting of the technique
Note that the toolboxes RingIndicator and CoreComparison can be used for flatbed images as well52 after converting the data to the proper resolution in the CoreProcessor module. Therefore, the DICOM format (common format for medical purposes and is also used for wood samples53) needs to be converted to .tiff. Departments that have medical CT scanner can use this software only for the type 1 inter-ring scale analysis type with resolutions of around 200 µm.
Limitations of the technique
The drawback of the method is that there are only a few facilities available for scanning for tree ring scale and anatomical scale (which require a higher resolution). Data volume and data handling can be challenging for larger volumes54. For very high-resolution images of wood anatomy (e.g., to further elucidate pit structures etc.), refer to classic procedures of using microsections for classic transmitted light microscopy27, scanning electron microscopy or confocal microscopy55.
Significance with respect to existing methods
Using the X-ray CT toolchain for increment cores presented here has been tested as showing reliable density values due to the calibration step (Figure 6)56 and generates MXD values comparable to existing densitometry procedures13.
Future applications of the technique
Any future applications of the technique might include the application of deep learning, amongst others for the automated ring indications and wood tissue quantifications57, specifically for 3D images.
The authors have nothing to disclose.
We thank the three anonymous reviewers for their feedback and suggestions. This research was funded by the BOF Special Research Fund for JVdB (BOF Starting Grant BOF.STG.2018.0007.01), for the UGCT as a Center of Expertise (BOF.EXP.2017.0007) and as a Core Facility (BOF.COR.2022.008), The authors also acknowledge the Research Foundation Flanders (G019521N and G009720N), and the UGent Industrial Research Fund (IOF) for the financial support to the infrastructure through grant IOF.APP.2021.0005 (project FaCT F2021/IOF-Equip/021).
6 mm paper straws | http://artstraws.com/education/product/artstraws-thick-asst-cols/ | Coring | |
Aluminium core holders | |||
ASTM standard ASTM D 1107 – 96 | |||
Cardboard postal tubes | https://www.rajapack.co.uk/envelopes-mailing-bags/postal-tubes/1-5-2mm-brown-cardboard-postal-tubes_PDT05623.html | ||
Column drill | |||
Computer hardware for reconstruction and analysis | X-ray CT scanning | ||
Cooling | |||
Drying oven | |||
Ethanol | |||
Flask for under soxhlet (2000 ml) | |||
Floral foam | https://www.oasisfloral.eu/ | Sample holder type 1 | |
Glass beads to fill void volume of Soxhlet to save solvent | |||
Glue | |||
Hot water bath | https://www.memmert.com/products/water-baths/water-bath/#!filters=%7B%7D | Soxhlet extraction | |
Increment borer | https://haglofsweden.com/project/increment-borers/ | ||
Plastic cylinder | Moonen et al. 2022 | Sample holder type 2 | |
Plastic cylinders | |||
Reservoir | |||
Tailored soxhlet apparatus | |||
Toluene | |||
Water pump | |||
X-ray CT scanner |