Magnetic resonance fingerprinting is an MRI-based technique that allows for fast, simultaneous quantitative mapping of multiple tissue parameters. Multi-component MRF (MC-MRF) additionally allows for the mapping of multiple tissue components per voxel. Currently, most MC-MRF implementations ignore any nonlinear effects on signals resulting from multi-component systems, such as the effects introduced by magnetisation transfer (MT). Here, we investigate the effects of free pool to free pool magnetisation transfer on the accuracy of MC-MRF, with a focus on the application of this technique for myelin water fraction (MWF) imaging. Assessment of the different MC-MRF techniques is done through application of these algorithms to two-component numerical phantoms, where the signals from two interacting components were simulated using the EPG-X framework. Results show that MT has a negative effect on the accuracy of the acquired parameter estimates for all estimated parameters, resulting in biases and incorrect parameter estimates. Several adjusted methods for MC-MRF including magnetisation transfer were proposed and tested. Although theoretically improvements were expected, the used sequences showed to be inadequate for accurate MT estimates. More research into these techniques is still required to improve their performance and accuracy. A technique left mainly unexplored here is sequence optimisation to minimise the effects of magnetisation transfer on the resulting signals. A quick exploration, however, showed that this might be a viable approach for future research as well.

Purpose: Pre- and post-treatment MRI based Diffusion Weighted Imaging (DWI) has shown to be an effective predictor and indicator of treatment response in cancer. However, clinical applications of the Apparent Diffusion Coefficient (ADC) requires sufficient precision; this can be assessed with repeatability studies. Therefore, the purpose of this study was to assess the repeatability of ADC measurements on an MR-Linac for patients with locally advanced rectal cancer (LARC). Additionally, the relative change in ADC during treatment was compared against the repeatability.
Methods: For 17 patients, DWI was performed once on 3T MRI during pre-treatment, and twice on an 1.5T MR-Linac during each treatment fraction. Manual delineations of the Gross Tumour Volume (GTV) were created by the author. In addition two semi-automatic delineation methods were implemented, which used; a registration pipeline to propagate T2 delineations; a geometric distortion correction algorithm to correct for DWI susceptibility artefacts. Based on visual inspection the most accurate and consistent delineations were used to calculate the ADC; Bland-Altman repeatability coefficient (RC), within subject coefficient of variation (wCV), and the intraclass correlation coefficient (ICC). Lastly, the relative change in mean and median tumour ADC was compared against the RC.
Results: Manual delineations were determined to have the highest agreement with ADC tumour location and were used in subsequent calculations. The mean and median tumour ADC wCV was 7.6% (CI95:5.5-9.2%) and 8.2% (CI95:5.9-9.9%) respectively, which corresponds with a RC of 21.0% (CI95:15.1-25.6%) and 22.7% (CI95:16.3-27.4%). The reliability of the measured data was good, with an ICC of 0.81 (CI95:0.72-0.87). In 6 out of 17 patients the relative change in ADC exceeded the RC at some point during treatment, which suggest a potential for future clinical applications.
Conclusion: Despite the low precision of rectal cancer ADC measurements, daily DWI imaging on MR-Linac has been shown to be viable for clinical applications in a subset of patients whom show significant ADC changes during treatment. Further studies are required to determine whether the measured ADC repeatability allows for clinically relevant observations.

Magnetic Resonance Fingerprinting (MRF) is a relatively new approach for simultaneously estimating multiple quantitative maps in one acquisition. Sequence optimisation for MRF can be a powerful tool in increasing the accuracy an precision of the quantitative results. Multi-component analysis in the MRF framework can distinguish multiple different tissues in one voxel such as myelin water and white matter which play an important role in monitoring progressive diseases such as multiple sclerosis. Using the estimation theoretic Cramér-Rao bound, optimisations of the acquisition sequences can be performed, that increase the precision of the resulting tissue maps. The effect of this optimisation has been confirmed using numerical simulations. Speed-ups in MRF are generated using significant undersampling of the k-space information. This results in spatially coherent undersampling artefacts, that generally is the dominating error source for regular $T_1$ and $T_2$ mapping. The undersampling artefacts can be predicted using a mathematical model leveraging on techniques from perturbation theory. Numerical simulations suggested that optimisations of the acquisition parameters are effective in reducing the undersampling error. This was confirmed using in vivo scans. The optimisations resulting from these two different models are easily implemented in future clinical practice.