Quantitative Muscle Composition Analysis Using Magnetic Resonance Imaging

Abstract: Changes in muscle tissue composition, e.g. decrease in volume and/or increase of fat infiltration, are related to adverse health conditions such as sarcopenia, inflammation, muscular dystrophy, and chronic pain. However, the onset and progression of disease and the effect of potential intervention effects are not fully understood, partly due to insufficient measurement tools. For advanced knowledge regarding these diseases, an accurate and precise measurement tool for detecting changes in muscle composition is needed. The tool must be able to detect both local changes on specific muscles for investigating local injuries and generalized muscle composition changes on a whole-body level. Magnetic resonance imaging is an excellent tool due to its superior soft tissue contrast but is normally not quantitative, making it challenging to produce reproducible results. Furthermore, manual analysis of the vast amount of images produced is extremely time consuming and therefore expensive. The aim of this thesis was to develop and validate a new magnetic resonance imaging method for muscle volume quantification and fat infiltration estimation that would have the potential to be used in both large-scale studies and for analyzing small individual muscles.The method development was divided into four main steps: 1) Rapid acquisition and reconstruction of data with sufficient resolution and calibration giving quantitative images where the relative fat content of each voxel (related to pure fat voxels) is attainable; 2) Automated muscle tissue classification based on non-rigid multi-atlas segmentation followed by probability voting to acquire the region of interest for each muscle; 3) Quantification of muscle tissue volume and fat infiltration from the classification step and the local fat signal; 4) Evaluation of the potential of the method in clinical studies.In Paper I, a method for automatic muscle volume quantification of both whole-body and regional muscles, i.e. involving steps 1–3, is presented. The automated method showed good agreement compared to manual segmentation. It was robust to an 8-fold resolution difference using two different scanner field strengths. Papers II and III evaluated the clinical relevance and the need for developing methods with high-resolution images to answer the research questions regarding the effect of a whiplash trauma on the multifidus muscles. This involved steps 1–4. The method enabled acquisition of high-resolution data to distinguish the small multifidus muscles (Paper II). The paper also showed a higher fat infiltration in the multifidus muscles in individuals with severe self-reported disability compared to individuals with milder symptoms and to healthy controls. Furthermore, the local fat infiltration was also related to widespread muscle fat infiltration (Paper III). However, the difference in widespread muscle fat infiltration could not alone distinguish between the three different groups. Paper IV showed the robustness of fat infiltration estimation when changing flip angle, and thereby the T1 weighting, of the acquired images (steps 1–3). The higher flip angle also provided better noise characteristics. Therefore, this quantitative method can be used with higher flip angle, and thus a potentially better anatomical contrast, without losing accuracy or precision.To conclude, this thesis presents a method that quantifies muscle volume and estimates fat infiltration robustly and reproducibly. The versatility of the method allows for both high-resolution images of small muscles and rapid acquisition of whole-body data. The method can be a useful tool in clinical studies regarding small individual muscles. Furthermore, the combination of being quantitative and automatic means that the method has potential to be used in longitudinal, multi-center, and large-scale studies for advanced understanding of muscular diseases.