Magnetic Resonance Imaging (MRI) is a versatile modality which is widely used for anatomical and physiological imaging in applications including oncology, neuroimaging, and cardiac angiography-to name a few. However, due to patient motion, hardware limitations and constraints on acquisition time, anisotropic images are acquired, sacrificing detail in through-plane. Multiplane super-resolution is a post-processing technique that uses data from multiplane anisotropic acquisition and reconstructs higher resolution MR images. However, certain gaps exist in the application of super-resolution in the context of MRI and research presented in this thesis attempts to address some of the unexplored areas in terms of clinical applications as well as applicability of current stateof-art super-resolution frameworks. The focus of this thesis is on the application of super-resolution for resolution improvement of MR images and development of a novel super-resolution framework that broadens its applicability to other MR applications. For this, three main studies were performed. In the first study, super-resolution was employed to enhance DWI resolution in the context of prostate cancer assessment. In this study, MRI data of 25 patients were acquired, isotropically reconstructed using super-resolution and analysed. Since it is unclear how through plane affects prostate cancer assessment, impact of through plane resolution improvement on prostate cancer diagnosis has been investigated. From the study, super-resolution reconstructions have been found to have increased SNR, sharpness and reduced volumetric error compared to anisotropic acquisitions. These isotropic super