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Abstract

Magnetic resonance imaging (MRI)-based mapping of the tissue oxygenation would be highly beneficial for the treatment of patients with brain tumours. Hence, the purpose of this thesis was to incorporate quantitative susceptibility mapping (QSM) into a reconstruction of the oxygen extraction fraction (OEF), cerebral blood flow (CBF) and cerebral metabolic rate of oxygen (CMRO2). In the first part, a joint QSM and quantitative blood oxygenation level-dependent (qBOLD) approach from two different MRI sequences, gradient echo sampling of spin echo (GESSE) and multi-gradient echo (GRE), was compared in seven healthy subjects and simulations. GESSE yielded higher parameter accuracy in simulated grey matter but produced unphysiological grey–white matter contrast in OEF in vivo. GRE is more efficient and generated uniform OEF maps in vivo but revealed biases in simulation. In the second part, an artificial neural network (ANN) was trained for QSM+qBOLD analysis and compared to the initial quasi-Newton (QN) reconstruction. The ANN allowed a faster and more robust reconstruction of OEF maps with lower intersubject variation (OEF_ANN = 43.5 +- 0.8% vs OEF_QN = 43.8 +- 5.2 %). In the third part, machine learning-based clustering was incorporated into the QN QSM+qBOLD analysis and applied to eight patients with high-grade gliomas. The OEF was significantly lower inside the tumour compared to the contralateral side in grade III (OEF_tum = 12.5 +- 0.5% vs OEF_con = 24.5 +- 2.3 %) and grade IV (OEF_tum = 17.2 +- 6.1% vs OEF_con = 24.8 +- 4.5 %) gliomas. The CBF was significantly higher; yet, only in grade IV gliomas (CBF_tum = 108.1 +- 83.3 ml/100 g/min vs CBF_con = 29.1 +- 21.0 ml/100 g/min). The CMRO2 revealed no significant differences. Exploiting the phase and magnitude of the acquired MRI signal and including machine learning for reconstruction of the OEF with QSM+qBOLD is promising and might facilitate the implementation of a robust quantification of the tissue oxygenation in the clinical routine in the future.