With the emergence of deep learning, their successful applications have been witnessed in various computer-vision tasks. As computational power has grown over the past decades, it has become possible to design deeper and more complex architectures to better extract features from the raw data, which can ultimately achieve better performance. However, medical images have different properties compared to natural images, and straightforward applications of deep networks for these tasks may not be feasible in clinical settings. Therefore, in this thesis, we propose deep learning-based frameworks that leverage additional information that is typically available in clinical conditions, with a focus on the modality of magnetic resonance imaging (MRI). Specifically, we investigate the inverse problem of MRI reconstruction, aiming to accelerate MRI acquisition by under-sampling while preserving the quality of acquired images.

First, we address the issue of effectively leveraging highly correlated information across contrasts in sequentially acquired multi-contrast MR scans. We design a framework to optimise the under-sampling pattern of a target MRI contrast, by exploiting the com- plementary fully-sampled reference contrast through a novel synthesis-based information extraction strategy. Meanwhile, the reconstruction network is jointly optimised to provide guidance on the selection of sampled positions, allowing for better exploitation given limited measurements to be sampled.

Second, we propose to leverage information from the original acquisition domain of MRI, known as the k-space, which is associated with, but different from the image domain. To allow for improved reconstruction with information in both domains, we present a dual-domain reconstruction framework with specifically designed regularisations to exploit the coupled information during optimisation. Moreover, the framework is extended for scenarios of multi-contrast MRI, with the focus on reducing the cross-contrast misalignment in both domains to better leverage the information from the reference contrast.

Finally, we deal with a specific clinical scenario where it is impractical to acquire largefully-sampled dataset by introducing a scan-specific multi-contrast MRI reconstruction framework. To effectively learn from limited sparsely sampled k-space data of highly imbalanced distribution, the model can adaptively learn from such sparse data without additional regularisation on a coarse-to-fine basis, to better capture information of various frequency levels. Moreover, the technique of implicit neural representation learning is employed to enable reconstruction of a single subject without supervision of fully-sampled data. To maximise the assistance power from the reference contrast, a Siamese architecture is designed to simultaneously reconstruct both contrasts, which can also mitigate overfitting due to the lack of supervision.

Through comprehensive experiments conducted across multiple datasets, we demon- strate the superiority of our proposed frameworks. These frameworks achieve improvements by leveraging diverse information types, including different contrasts, tasks, domains, and frequency bands from the data. Meanwhile, the developed methods covered both super- vised and unsupervised learning paradigms. The approaches explored in our research not only contribute to the current state of MRI reconstruction but also offer possible directions for future investigation in this field, potentially leading to further improvements in MRI reconstruction.