For a long time, radiography and subsequently conventional magnetic resonance imaging (MRI) were used as imaging biomarkers for evaluating cartilage morphological disease state in osteoarthritis (OA). Because research is switching its focus towards disease modification or even prevention to target OA at an early stage, imaging techniques that measure cartilage composition rather than its morphology became of interest. Several MRI and computed tomography (CT) based quantitative imaging biomarkers for cartilage composition were developed. These techniques were advocated to allow a quantitative measure of the sulphated glycosaminoglycan (sGAG) content, an important composite of the cartilage extracellular matrix. The main aims of this thesis is based have been divided between MRI and CT based quantitative imaging biomarkers since their different stage of application in research. MRI has already been applied in human OA research, whereas CT was still to be translated and implemented in clinical research.
The first part of this thesis focused on MRI based techniques and aimed at optimization of image post processing, assessing reproducibility, comparison of different MRI sequences and application in clinical OA research.
Since accurate image post processing is of utmost importance to generate reliable and robust quantitative MRI outcomes, an imaging post processing tool was developed and described in chapter 2. This tool corrects for intra-sequence patient motion during acquisition of quantitative MR images, by applying image registration reducing errors and incorrect outcomes. This resulted in 6-14% improvement in accuracy of delayed gadolinium-enhanced MRI of cartilage (dGEMRIC) T1 relaxation time. Using image registration, the tool also allows assessment of the same cartilage region throughout multiple MRI acquisitions, which makes analyses less time consuming. Finally, the algorithm also involves a fitting technique which corrects for unreliable quantitative MRI biomarker data by calculating a weighted mean outcome for all voxels in a specific cartilage region based on the inaccuracy of each voxel. Because of these abilities and the fact that this tool could be used in any quantitative MRI biomarker, e.g. T1rho-mapping or T2-mapping, the image post processing tool was used in all chapters in this thesis where MRI based measures were used for cartilage sGAG content.
Along with robust image processing tools, the outcomes of the MRI exam itself should also be reproducible in order to be able to apply the particular technique in cross-sectional or longitudinal study designs. Therefore, chapter 3 described a reproducibility study of dGEMRIC acquired at 3 Tesla in early stage knee OA patient. It was shown that dGEMRIC is highly reproducible in terms of results in large cartilage regions, as well as for differentiating between spatial distributions of diverse cartilage quality within a single slice. dGEMRIC can therefore be used as an imaging biomarker in cross-sectional and longitudinal study designs. In addition, a threshold for defining significant changes in dGEMRIC results for longitudinal follow-up was determined.
T1rho-mapping has been proposed as a non-contrast-enhanced alternative to dGEMRIC for sGAG quantification in clinical studies. However, no thorough validation has been performed comparing both techniques within the same OA patients using a reference standard for cartilage sGAG. Therefore, in chapter 4 an in vivo comparison and validation study assessing the capability of dGEMRIC and T1rho-mapping was performed. In knee OA patients, dGEMRIC results strongly correlate with cartilage sGAG content, whereas T1rho-mapping did not. Therefore, it appears that T1rho-mapping cannot be regarded as an alternative for dGEMRIC to measure cartilage sGAG content in clinical OA research. It was also shown that resu
