A major constraint to crop growth is the poor bioavailability of edaphic nutrients, especially phosphate (P). Improving the nutrient acquisition efficiency of crops is crucial in addressing pressing global food-security issues arising from increasing world population, reduced fertile land and changes in the climate. Despite the undoubted importance of root architecture and root/soil interactions to nutrient uptake, there is a lack of approaches for quantifying plant roots non-invasively at all scales. Mathematical models have allowed our understanding of root and soil interactions to be improved, but are almost invariably reliant on idealised geometries or virtual root growth models. In order to improve phenotyping of advantageous traits for low-P conditions and improve the accuracy of root growth and uptake models, more sophisticated and robust approaches to in vivo root and soil characterisation are needed. Microfocus X-ray Computed Tomography (�-CT) is a methodology that has shown promise for noninvasive imaging of roots and soil at various scales. However, this potential has not been extended to consideration of either very small (rhizosphere scale) or large (mature root system scale) samples. This thesis combines discovery experiments and method development in order to achieve two primary objectives: • The development of more robust, well-described approaches to root and soil �-CT imaging. Chapters 2 and 3 explore the potential of clinical contrasting methods in root investigation, and show how careful consideration of imaging parameters combined with development of user invariant image-processing protocol can improve measurement of macro-porous volume fraction, a key soil parameter. • Chapter 4 develops an assay for first-time 3D imaging of root hairs in situ within the rhizosphere. The resulting data is used to parameterise an explicit P uptake model at the hair scale, suggesting a different contribution of hairs to uptake than was predicted using idealised geometries. Chapter 5 then extends the paradigm for root hair imaging and model generation, building a robust, modular workflow for investigating P dynamics in the rhizosphere that can accommodate non-optimal soil-water states.

Rheumatoid arthritis is an autoimmune disease that affects approximately 1% of the population, where chronic inflammation of the synovial joints can lead to active destruction of cartilage and bone. New therapeutic targets are discovered by investigating genes or processes that exacerbate or ameliorate disease progression. Mouse models of inflammatory arthritis are commonly employed for this purpose, in conjunction with biomedical imaging techniques and suitable measures of disease severity. This thesis investigated the hypothesis that a statistical model of non-pathological bone shape variation could be used to quantify bone destruction present in micro-CT images. A framework for constructing statistical shape models of the hind paw was developed, based on articulated registration of a manually segmented reference image. Successful registration of the reference towards ten healthy hind paw samples was followed by statistical shape analysis. Mouse models of inflammatory arthritis were then investigated and compared by identifying bone abnormalities as deviations from the model statistics. Validation of the model against digital phantoms and clinical scores indicates that the method is largely successful in this effort. Application of the method in a novel study of macrophage-mediated inflammation shows promising results that are supportive of previous findings.