## 2 Materials and Methods
### Image Reconstruction  
The tomogram data sets were reconstructed using Tomopy Python package [33]. The raw intensity data were normalized using the white-field and dark-field corrections to compensate for different detector pixel responses. The intensity data were then scaled between 0 and 1 by calculating the normalized data's negative logarithm. Detector artifacts,  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Relative Humidity** \\ \hline Desiccant & 0\% \\ Magnesium chloride & 33\% \\ Sodium chloride & 75\% \\ Potassium chloride & 95\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Desiccant or salts used in saturated aqueous solutions with corresponding relative humidity condition created [32].  
Figure 2: Custom-built relative humidity (RH) chamber for APS beamline 2-BM-B. The chamber separated into two pieces to facilitate sample changes. The top of the chamber consisted of 6.3 cm length of a Precision Paper Tube Company (Wheeling, IL, USA) Kapton® tube with 127 \(\upmu\)m thick walls and 4.45 cm outside diameter. The Kapton® tube had machined aluminum caps at each end. The bottom consisted of machined aluminum with a Ted PellaTM Mountain Lakes Blvd, Redding, CA, USA) pin mount sample holder that was secured in the chamber with a set screw, a hose barb connector for the RH generator connection, a SensirionTM (Staefa, Switzerland) SHT1x relative humidity and temperature sensor to monitor the conditions inside the chamber, and a ThorLabsTM (Sparta Ave, Newton, NJ, USA) kinematic mount to attach the RH chamber to the beamline rotation stage.  
such as ring artifacts and streaks, caused by the drift of the inhomogeneous X-ray beam or imperfections of the imaging detector system were not entirely removed by normalization. The detector artifacts were further reduced by employing stripe removal filtering in the sinograms using the combined wavelet-Fourier filtering technique [34]. The best results were obtained with the wavelet Daubechies 5 (db5) with a decomposition level of 10 and a smoothing factor of 3. Higher-order wavelet filters reduced the sharpness of the reconstructed images. The filter parameters were chosen as an optimization between artifact suppression and the preservation of the image object details. However, some streak artifacts persisted in the reconstructed images as are described later in the manuscript.  
For reconstruction, both filtered back projection (FBP) and conjugate gradient least squares (CGLS) methods were initially performed and compared. FBP methods have previously been used in \(\upmu\)XCT wood studies [18; 19; 20]. The FBP reconstruction method is based on the Fourier grid reconstruction algorithm and has the advantage of computational efficiency [35]. However, FBP reconstructions have the disadvantage of resulting in a noisier reconstruction when there is noise in the acquired projection data. The CGLS algorithm is an algebraic iterative reconstruction method based on numerical approximations determined by minimizing the difference between the forward projection of the reconstructed image with the acquired projection data using the conjugate gradient method [36]. Although the CGLS method is more computationally intensive than the FBP method, it has the advantage that the effect of noise in the projection images can be reduced by finding the optimum number of iterations [36]. In this work, the optimal iteration was 100. Lower numbers of iterations were tested to decrease the computation intensity. However, the lower numbers resulted in high-frequency noise in the reconstructed images. Figure 3 shows comparisons from the current experiments between FBP and CGLS reconstructed image slices and intensity line profiles. The CGLS reconstructed image is less noisy, which will be better for image segmentation and further analysis [36]. Therefore, the CGLS method was used for reconstructing the data for further analysis.