Abstract:
MRI is a non-invasive imaging modality that provides excellent soft tissue
contrast as compared with other imaging techniques e.g., X-ray and CT,
etc. MRI comes with the drawback of long scan time due to the slow data
acquisition process. Data under-sampling is performed to accelerate the scan
time which leads to artifacts. This thesis presents a deep learning-based MR
image reconstruction from 1D-Cartesian variable density under-sampled MR
image data. The proposed method significantly outperforms all the evaluated
approaches, based on a thorough comparison of the proposed methodology
with other approaches.
