Advanced analysis of contrast agents in X-ray and magnetic resonance imaging

Abstract

Medical imaging techniques, such as x-ray imaging and magnetic resonance imaging (MRI), allow us to visualize anatomical structures of the body and in some cases, study underlying biological processes. Contrast agents used in imaging aid in visualizing different structures and processes by increasing contrast differences, either generally or in a targeted fashion. In x-ray imaging, contrast agents enhance radiodensity in target tissues. In MRI, contrast agents work by shortening relaxation times of nuclei in the body. In both cases, contrast agents can yield important information about the mechanisms of disease and/or therapies and how best to optimize care. For radiotherapy cancer treatment planning, the target area needs to be accurately delineated on a computed tomography (CT) image. In some instances, the target area may be difficult to see, resulting in inaccurate delineation of the target area. Materials modified with contrast agents can be placed in areas of interest after surgery to help accurately delineate the target. Molecular MRI is useful for assessing immunotherapy treatments for certain types of cancer. Contrast agents are used for cell labelling and in the case of brain tumours, assessing the structure of the blood brain barrier (BBB). We can image at different time points throughout treatment to monitor the immunotherapy treatment. With large amounts of imaging data, analysis becomes complex. We can apply a radiomics approach, which extracts features that are not obvious from individual images. Machine learning algorithms can be applied to determine if there are any correlations between features and treatment outcomes. The objectives of this project were as follows. First was to modify a commercially available hydrogel material with a gadolinium-based contrast agent that can be injected into the surgical bed after lumpectomy so that it can be seen with CT, planar x-ray imaging and cone beam CT (CBCT). The second objective used radiomics and machine learning to identify potential preclinical MR imaging features that can be used as predictors for immunotherapy treatment success in a glioblastoma mouse model. Additionally, radiomics features were explored to determine if they could be used to differentiate between sex or treatment groups.