F UTURE P ERSPECTIVES

CS is currently a well-developed technique. Some type of algorithms have meanwhile received approval from the US Food and Drug Administration (FDA) and have found their way into clinical routines [21]. Later approaches, for example the ones combined with deep learning models, still require further testing before integration in a clinical workflow can be realized. With the emerging role of machine learning in MRI recon- structions, the development of quality tests and the collaboration between researchers and radiologists will become more and more important. Incorporation of physics into the deep learning models, either via the input data or via mathematical formulations in the minimization algorithm, will constrain the solution space, which will help to better control and interpret any resulting image artifacts.

Although the enthusiasm for CS is mostly related to the shorter scanning times when undersamplingk-space, the optimization methods that have been developed for CS can serve a wider purpose. Future developments could also address artifact cor- rections, for example in case of gradient non-linearities and main field imperfections, or image reconstructions for low SNR settings, such as in permanent magnet low field MRI [22]. In some cases, the Fourier structure of the system matrix (describing the data model) may be lost, which can lead to longer reconstruction times. Future research should adapt existing reconstruction schemes to the different structures of the system matrices.

While the use of CS in a clinical environment has been tested extensively, MRF is cur- rently not widely adopted in standard clinical protocols. Although first steps have been taken to address reproducibility and repeatability questions, further steps require more extensive comparisons, including the examination of different MRF sequences and flip angle patterns. Furthermore, there should be a better understanding of the dif- ferences between relaxation times measured with MRF and conventional quantitative measures. As a first step, the sensitivity of MRF to flow an other physiological pro- cesses, such as perfusion and diffusion, should be compared with that of conventional techniques. These experiments should result in guidelines for how much deviation from nominal values can be expected due to these patient-specific processes, such that interpreting variability in relaxation times between patients will become easier.

Many developments in MRF were performed at 1.5T or 3T, while only some tech- niques have been applied at 7T [23–26]. The effect of inhomogeneous transmit and main magnetic fields is much stronger at high field, which is why techniques often cannot be transferred directly from 1.5T or 3T to 7T. Especially fast approaches, involv- ing spiral acquisitions with long spiral sampling durations, or applications with a large amount of fat, can be challenging. Applications in which large areas of low transmit efficiency are observed may also require modified flip angle patterns. Future research should explore the usability of existing MRF techniques at high field, and adapt or ex- tend the approaches where necessary.

Finally, reconstruction of undersampled MRF data can take much longer than for a typically undersampled clinical scan. This is due to the large amount of images ac- quired in MRF, and especially the case when CS or MC reconstructions are combined with motion correction, parallel imaging, and non-uniform FFT (NUFFT) reconstruc- tions. Some approaches even reported reconstruction times up to 20 hours [12]. To

support integration of MRF in a clinical workflow, image reconstruction and parame- ter matching times should be reduced to practicable durations (∼1 minute). This will likely involve the help of good hardware, machine learning and clever formulations of minimization cost functions.

To conclude, the chapters in this thesis have shown that the MR data is rich in structure and information. Insight about the specific structure can improve the performance of model-based reconstruction techniques, and mathematically understanding physical and physiological processes can help to distill more information from a single scan. Future research will teach us how to optimally exploit all this structure and informa- tion, such that the efficiency of MR will grow, and can open up new opportunities for research and finally for clinical applications.

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