Technical developments for MR-based electrical property mapping

Abstract

The electrical properties of tissues (permittivity and conductivity) regulate the effects of electromagnetic (EM) fields in the human body. Knowledge of the electrical properties (EPs) permits assessing the safety of human exposure to em fields as generated by telecommunication and medical devices (e.g. mobile phones and magnetic resonance systems). It also allows reliable planning of medical treatments using EM fields. Examples of these treatments are radiofrequency hyperthermia for cancer therapy and EM stimulation for neurological disorders. The EPs are intrinsic tissue characteristics and hold the promise of being endogenous biomarkers, i.e. they could be valuable to discriminate a pathological condition or monitor treatment effects.

Magnetic Resonance (MR)-based Electrical Properties Tomography (EPT) is a powerful technique to measure such properties non-invasively. This is possible because the EPs perturb the spatial distribution of the complex B1+ field, i.e. the magnetic field responsible for spin excitation in MR imaging (MRI). MR-EPT requires the acquisition of both amplitude and phase of the complex B1+ field, from which subject-specific permittivity and conductivity maps are reconstructed. Both B1+ acquisition and EP reconstruction can be performed in different ways, as outlined in Chapter 1, and ultimately influence the accuracy and precision of EP maps.

Assessing the accuracy and precision of EP maps is a central theme in this thesis. These characteristics determine the validity of EPT as a quantitative mapping tool for clinical applications. This thesis specifically explored different aspects of an MR-EPT experiment that define the accuracy and precision of EPT-based maps: the MR acquisition, the EPT reconstruction and the intended clinical application. First, the impact of acquisition techniques on EPT was quantified: in particular, Chapter 2 focused on B1+ phase mapping methods and their impact on Helmholtz-based EPT (H-EPT) conductivity reconstruction, and Chapter 3 dealt with the influence of three commonly available B1+ amplitude mapping sequences on H-EPT permittivity reconstruction. Then, Chapter 4 presented a new deep learning-based EPT (DL-EPT) approach for conductivity reconstruction in the pelvic region and analyzed its reconstruction performance. Finally, Chapter 5 was extensively dedicated to hyperthermia treatment planning (HTP), the medical application of interest in this thesis. Here, DL-EPT and advanced HTP elements were combined within a single workflow to enable more reliable hyperthermia treatment plans.

In conclusion, this thesis implemented technical frameworks on which future research may further build to study the acquisition and reconstruction aspects contributing to the accuracy, precision and clinical applicability of EPT maps. Furthermore, this work paved the way for more reliable, clinically feasible and personalized hyperthermia treatment planning.