PhD defense Isabel K. Bones

On Thursday 7 October 2021, Isabell K. Bones will defend her thesis: “Technical advances paving the way for non – invasive renal perfusion MRI”.


Kidneys need healthy blood supply to perform their function and clean the body from waste products and excess water. Hence, monitoring of renal perfusion may provide valuable information about renal function. Having biomarkers that reveal information about renal dysfunction at an earlier stage could help stopping renal disease progress or at least slow down the damage. A potential early biomarker for functional kidney assessment could be renal perfusion imaging. For that purpose, a promising candidate is non-invasive Arterial Spin Labeling (ASL) MRI. In recent years, encouraging results of renal ASL MRI have been presented in the research domain that raised clinical interest. Nevertheless, a few challenges remain to be overcome to facilitate its clinical adoption. 1) Respiratory motion introduces subtraction artifacts and increases physiologic noise that corrupt the isolation of the ASL signal and decrease its accuracy as well as precision. 2) Currently applied spatially selective ASL schemes in renal ASL are sensitive to changes in the arterial transit time 3) and they require planning of a labeling slab, which can be time consuming and may introduce operator dependence. 4) Furthermore, clinical applicability of renal ASL MRI is hampered due to labour intensive and observer dependent post-processing.

In this dissertation we applied technical advances to tackle those challenges in order to make renal ASL MRI more clinically attractive. 1) Our results show that background suppression increases ASL quality when respiratory motion is present. 2+3) Moreover, we present the first application of a flow based ASL technique, VSASL, for renal perfusion measurement with promising results. In this technique labeling is operator independent and insensitive to arterial transit time changes. 4) Finally, we established a workflow for fully automatic renal ASL cortical RBF quantification using machine learning.

ASL MRI is a unique, completely noninvasive and robust approach for quantitative perfusion imaging, whose technical feasibility and potential for clinical applications have already been demonstrated. While there is still some road to be covered, the research in this thesis shows that important barriers towards clinical applicability can be overcome with the application of recent technical advances.