Applications of advanced MRI methods in cancer and neuroimaging

Magnetic resonance imaging (MRI) is a powerful and versatile non-invasive medical imaging modality. In this thesis, advanced MRI methods in cancer and neuroimaging were investigated. More specifically, we focus on the development and application of advanced diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI) and functionalMRI (fMRI) in prostate cancer and the entorhinal cortex of the brain. Prostate cancer is one of the most common types of cancer among men worldwide, and MRI is essential in detection and staging of the disease. However, improved tools are needed to distinguish between low-risk and high-risk cancer, and the widely used mono-exponential apparent diffusion coefficient (ADC) derived from DWI is a crude simplification of the underlying tissue microstructure. In paper I of this thesis, we therefore develop and apply an ADC- and T2-dependent two-component model based on combined T2-DWI, in order to investigate its diagnostic potential in prostate cancer. We found that signal fractions of a slow diffusion component estimated from this model were able to significantly discriminate between tumor and normal prostate tissue, and showed a fair correlation with tumor aggressiveness. Our findings thus indicate that the ADC- and T2-dependent two-component model shows potential for diagnosis and characterization of prostate cancer, although it only performed similarly, and not better than more conventional diffusion models. The entorhinal cortex (EC) is a part of the hippocampal formation of the brain involved in cognitive processes such as memory formation, spatial navigation and time perception. It can be divided into twomain subregions—medial (MEC) and lateral (LEC) EC—which differ in both functional properties and connectivity to other regions, and these have been widely studied and defined in rodents. Despite previous attempts to localize the human homologues of the subregions using fMRI, where they were identified as posteromedial (pmEC) and anterolateral (alEC) EC, uncertainty remains about the choice of imaging modality and seed regions for connectivity analysis. In paper II, we therefore use DTI and probabilistic tractography to segment the human EC based on differential connectivity to other brain regions known to project selectively to MEC or LEC. Furthermore, in paper III, we aimed to extend this analysis to a cohort with both DTI and resting-state fMRI data, in order to directly compare the results from using structural and functional connectivity to segment the EC. Both theDTI and fMRI results fromthe two papers support the subdivision of the human EC into pmEC and alEC, although with a larger medial-lateral component than in the previous fMRI studies. We also showed that the segmentation results using DTI are relatively reproducible across cohorts and acquisition protocols. Correctly delineating the human homologues of MEC and LEC has importance not only for research in systems and cognitive neuroscience, but also for translational studies on neurodegenerative processes such as Alzheimer’s disease, which starts in the EC and transentorhinal area. In conclusion, the research in this thesis demonstrates how advanced DWI and DTI can be used to model different types of tissue. It also shows that DTI and fMRI are able to similarly describe connectivity between brain regions. Both cancer and neuroimaging are highly relevant disciplines for applications of these advanced MRI methods, which might gain increased importance in diagnosis and management of cancer and dementia in the future

Implementation of geometric distortion correction of EPI images in clinical workflow

Background: Geometric distortion in echo-planar (EPI) magnetic resonance (MR) images, caused by low bandwidth combined with variations in magnetic susceptibility, is a problem that can make it challenging to connect functional MR data to anatomical position. A distortion correction method based on the acquisition of images with opposing phase encoding directions has been successfully used at NTNU for several years. However, this method can currently only be performed offline on anonymised images and is therefore not used in clinical routine. The aim of this master's project was to implement and test this method in the clinical software environment of syngo.via Frontier. Materials and methods: A prototype for distortion correction was made in MeVisLab and installed in syngo.via Frontier. It was tested on diffusion-weighted EPI images from 13 breast and 16 prostate patients, and the images were examined visually to determine the robustness in terms of percentage non-failed corrections, and the quality of corrections. Various image similarity metrics were also calculated in order to try to determine the quality of corrections quantitatively. To assess the user-friendliness of the prototype, it was tested by a radiographer and a medical physicist. Results: None of the corrections failed, giving a robustness of 100%. By visual assessment, the quality of correction was determined to be successful for 12 of 13 breast patients and all 16 prostate patients. The calculated metrics generally showed good results for the breast images, but they gave some inconclusive results for the prostate images. The feedback from the user-friendliness testing showed that after minimal training it would be relatively easy to use the prototype. Conclusion: The prototype was able to successfully correct geometrically distorted images. Nevertheless, the calculated metrics yielded varying results, and more investigations should therefore be performed to find suitable metrics for determining the quality of corrections quantitatively. Although there is still room for improvements, the prototype has potential to be an easy-to-use tool for geometric distortion correction of EPI images in clinical workflow

Exploring the diagnostic potential of adding T2 dependence in diffusion-weighted MR imaging of the prostate.

BackgroundMagnetic resonance imaging (MRI) is essential in the detection and staging of prostate cancer. However, improved tools to distinguish between low-risk and high-risk cancer are needed in order to select the appropriate treatment.PurposeTo investigate the diagnostic potential of signal fractions estimated from a two-component model using combined T2- and diffusion-weighted imaging (T2-DWI).Material and methods62 patients with prostate cancer and 14 patients with benign prostatic hyperplasia (BPH) underwent combined T2-DWI (TE = 55 and 73 ms, b-values = 50 and 700 s/mm2) following clinical suspicion of cancer, providing a set of 4 measurements per voxel. Cancer was confirmed in post-MRI biopsy, and regions of interest (ROIs) were delineated based on radiology reporting. Signal fractions of the slow component (SFslow) of the proposed two-component model were calculated from a model fit with 2 free parameters, and compared to conventional bi- and mono-exponential apparent diffusion coefficient (ADC) models.ResultsAll three models showed a significant difference (pConclusionSignal fraction estimates from a two-component model based on combined T2-DWI can differentiate between tumor and normal prostate tissue and show potential for prostate cancer diagnosis. The model performed similarly to conventional diffusion models