X-ray computed tomography (CT) has been used as an analysing tool for different features in wood research since the beginning of the1980s, but it can also be used to study wood-water interactions in different ways, such as by determining wood moisture content (MC). The determination of wood MC with CT requires two CT images: one at the unknown moisture distribution and a second one at a known reference MC level, usually at oven-dry condition. The two scans are then compared, and the MC is calculated based on the differences between the images. If the goal is to determine the MC in local regions within the wood volume, e.g. when studying moisture gradients in wood drying, wood shrinkage must be taken into account during the data processing of the images. The anisotropy of wood shrinkage creates an obstacle, however, since the shrinkage is not uniform throughout the wood specimen. The technique is thus limited in two ways: it cannot measure MC in local regions and it cannot do it in real time.

The objective of this thesis was to study methods to overcome these two limitations. The work explores up to three different methods to estimate local MC from CT images in real time. The first method determines shrinkage for each pixel using digital image correlation (DIC) and is embedded in a broader method to estimate the MC, which verified against a reference. It involves several steps in different pieces of software, making it time-consuming and creating many sources of possible experimental errors. The determination of shrinkage within this method is further explored to enable the implementation of all steps in a unique piece of software. It is shown that it is possible to calculate MC through this method with a root mean square error of prediction of 1.4 percentage points for MC between 6 and 25%.

The second method studied succeeds in determining the MC distribution in research applied to wood drying, but the calculation of shrinkage differs from the previous method: instead of calculating shrinkage in the radial and tangential directions, it does so by using the displacement information generated from the spatial alignment of the CT images. Results show that the algorithm can provide consistent data of internal MC distribution of wood at the pixel level that entail continuing researching wood drying processes with an improvement in the accuracy of the MC determination. It represents an improvement regarding the first method because the calculation is fast and highly automatized in a single piece of software.

The third method studied is the application of dual energy CT (DECT) to moisture. DECT would provide means for MC calculation at the pixel level and, potentially, in real time, which would mean an important breakthrough in wood drying research. Previous research shows promising results, but its implementation in medical CT, the tool used throughout this work, has shown poor predicting ability. Nevertheless, further research is encouraged.

The work done in this thesis proves that it is possible to measure local distribution of MC in wood using CT with accuracy and precision. It also shows that further research could potentially provide a means for MC estimation in real time.