PhD defence C. (Chaoping) Zhang

Dissertation in short:

Magnetic resonance imaging (MRI) is one of the most widely used methods in medical imaging and can provide various soft tissue contrasts for various anatomies. MRI forms images from scanner acquired k-space signals using various imaging pulse sequences. Such acquisitions can take long times, which is one of the major drawbacks of MRI. To speed up, people have developed numerous fast imaging sequences, trying to acquire more k-space signals in shorter time. Another way to accelerate the imaging is subsampling the k-space, which imposes challenges for reconstruction. The clinical MRI scans are usually performed with a multi-channel receive coil and/or using multiple pulse sequence settings. The signal correlation among the channels or different sequence settings provides opportunities for reconstructing subsampled k-space. This thesis proposes multiple auto-calibrated reconstruction methods by exploiting such signal correlation. In this thesis, the autocalibration is not only used to accelerate the imaging but also to compensate for the motion that happens during the scanning. In addition to the conventional linear way of exploiting the signal correlation, we also explored non-linear autocalibration using a neural network. Finally, we explored the potential of acceleration with a deep learning inverse problem solver by exploiting further the shared knowledge between image reconstruction and relaxometry parameter mapping for quantitative MRI. The image reconstruction and motion compensation methods proposed in this thesis may contribute to the implementation of faster MRI methods in clinical practice.